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audiocraft/modules/activations.py

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+# 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.
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from typing import Union, Callable
+
+
+class CustomGLU(nn.Module):
+    """Custom Gated Linear Unit activation.
+    Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
+    of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
+    function (i.e. sigmoid, swish, etc.).
+
+    Args:
+        activation (nn.Module): The custom activation to apply in the Gated Linear Unit
+        dim (int): the dimension on which to split the input. Default: -1
+
+    Shape:
+        - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
+          dimensions
+        - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
+
+    Examples::
+        >>> m = CustomGLU(nn.Sigmoid())
+        >>> input = torch.randn(4, 2)
+        >>> output = m(input)
+    """
+    def __init__(self, activation: nn.Module, dim: int = -1):
+        super(CustomGLU, self).__init__()
+        self.dim = dim
+        self.activation = activation
+
+    def forward(self, x: Tensor):
+        assert x.shape[self.dim] % 2 == 0  # M = N / 2
+        a, b = torch.chunk(x, 2, dim=self.dim)
+        return a * self.activation(b)
+
+
+class SwiGLU(CustomGLU):
+    """SiLU Gated Linear Unit activation.
+    Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(SwiGLU, self).__init__(nn.SiLU(), dim)
+
+
+class GeGLU(CustomGLU):
+    """GeLU Gated Linear Unit activation.
+    Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(GeGLU, self).__init__(nn.GELU(), dim)
+
+
+class ReGLU(CustomGLU):
+    """ReLU Gated Linear Unit activation.
+    Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(ReGLU, self).__init__(nn.ReLU(), dim)
+
+
+def get_activation_fn(
+    activation: Union[str, Callable[[Tensor], Tensor]]
+) -> Union[str, Callable[[Tensor], Tensor]]:
+    """Helper function to map an activation string to the activation class.
+    If the supplied activation is not a string that is recognized, the activation is passed back.
+
+    Args:
+        activation (str, or Callable[[Tensor], Tensor]): Activation to check
+    """
+    if isinstance(activation, str):
+        if activation == "reglu":
+            return ReGLU()
+        elif activation == "geglu":
+            return GeGLU()
+        elif activation == "swiglu":
+            return SwiGLU()
+    return activation

audiocraft/modules/chroma.py

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+# 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.
+import typing as tp
+
+from einops import rearrange
+from librosa import filters
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+
+
+class ChromaExtractor(nn.Module):
+    """Chroma extraction and quantization.
+
+    Args:
+        sample_rate (int): Sample rate for the chroma extraction.
+        n_chroma (int): Number of chroma bins for the chroma extraction.
+        radix2_exp (int): Size of stft window for the chroma extraction (power of 2, e.g. 12 -> 2^12).
+        nfft (int, optional): Number of FFT.
+        winlen (int, optional): Window length.
+        winhop (int, optional): Window hop size.
+        argmax (bool, optional): Whether to use argmax. Defaults to False.
+        norm (float, optional): Norm for chroma normalization. Defaults to inf.
+    """
+    def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12, nfft: tp.Optional[int] = None,
+                 winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None, argmax: bool = False,
+                 norm: float = torch.inf):
+        super().__init__()
+        self.winlen = winlen or 2 ** radix2_exp
+        self.nfft = nfft or self.winlen
+        self.winhop = winhop or (self.winlen // 4)
+        self.sample_rate = sample_rate
+        self.n_chroma = n_chroma
+        self.norm = norm
+        self.argmax = argmax
+        self.register_buffer('fbanks', torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
+                                                                       n_chroma=self.n_chroma)), persistent=False)
+        self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
+                                                      hop_length=self.winhop, power=2, center=True,
+                                                      pad=0, normalized=True)
+
+    def forward(self, wav: torch.Tensor) -> torch.Tensor:
+        T = wav.shape[-1]
+        # in case we are getting a wav that was dropped out (nullified)
+        # from the conditioner, make sure wav length is no less that nfft
+        if T < self.nfft:
+            pad = self.nfft - T
+            r = 0 if pad % 2 == 0 else 1
+            wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
+            assert wav.shape[-1] == self.nfft, f"expected len {self.nfft} but got {wav.shape[-1]}"
+
+        spec = self.spec(wav).squeeze(1)
+        raw_chroma = torch.einsum('cf,...ft->...ct', self.fbanks, spec)
+        norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
+        norm_chroma = rearrange(norm_chroma, 'b d t -> b t d')
+
+        if self.argmax:
+            idx = norm_chroma.argmax(-1, keepdim=True)
+            norm_chroma[:] = 0
+            norm_chroma.scatter_(dim=-1, index=idx, value=1)
+
+        return norm_chroma

audiocraft/modules/codebooks_patterns.py

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+# 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
+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)

audiocraft/modules/conditioners.py

@@ -0,0 +1,1416 @@
+# 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 defaultdict
+from copy import deepcopy
+from dataclasses import dataclass, field
+from itertools import chain
+import logging
+import math
+from pathlib import Path
+import random
+import re
+import typing as tp
+import warnings
+
+import einops
+from num2words import num2words
+import spacy
+from transformers import RobertaTokenizer, T5EncoderModel, T5Tokenizer  # type: ignore
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from .chroma import ChromaExtractor
+from .streaming import StreamingModule
+from .transformer import create_sin_embedding
+from ..data.audio import audio_read
+from ..data.audio_dataset import SegmentInfo
+from ..data.audio_utils import convert_audio
+from ..environment import AudioCraftEnvironment
+from ..quantization import ResidualVectorQuantizer
+from ..utils.autocast import TorchAutocast
+from ..utils.cache import EmbeddingCache
+from ..utils.utils import collate, hash_trick, length_to_mask, load_clap_state_dict, warn_once
+
+
+logger = logging.getLogger(__name__)
+TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
+ConditionType = tp.Tuple[torch.Tensor, torch.Tensor]  # condition, mask
+
+
+class WavCondition(tp.NamedTuple):
+    wav: torch.Tensor
+    length: torch.Tensor
+    sample_rate: tp.List[int]
+    path: tp.List[tp.Optional[str]] = []
+    seek_time: tp.List[tp.Optional[float]] = []
+
+
+class JointEmbedCondition(tp.NamedTuple):
+    wav: torch.Tensor
+    text: tp.List[tp.Optional[str]]
+    length: torch.Tensor
+    sample_rate: tp.List[int]
+    path: tp.List[tp.Optional[str]] = []
+    seek_time: tp.List[tp.Optional[float]] = []
+
+
+@dataclass
+class ConditioningAttributes:
+    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
+    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
+    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
+
+    def __getitem__(self, item):
+        return getattr(self, item)
+
+    @property
+    def text_attributes(self):
+        return self.text.keys()
+
+    @property
+    def wav_attributes(self):
+        return self.wav.keys()
+
+    @property
+    def joint_embed_attributes(self):
+        return self.joint_embed.keys()
+
+    @property
+    def attributes(self):
+        return {
+            "text": self.text_attributes,
+            "wav": self.wav_attributes,
+            "joint_embed": self.joint_embed_attributes,
+        }
+
+    def to_flat_dict(self):
+        return {
+            **{f"text.{k}": v for k, v in self.text.items()},
+            **{f"wav.{k}": v for k, v in self.wav.items()},
+            **{f"joint_embed.{k}": v for k, v in self.joint_embed.items()}
+        }
+
+    @classmethod
+    def from_flat_dict(cls, x):
+        out = cls()
+        for k, v in x.items():
+            kind, att = k.split(".")
+            out[kind][att] = v
+        return out
+
+
+class SegmentWithAttributes(SegmentInfo):
+    """Base class for all dataclasses that are used for conditioning.
+    All child classes should implement `to_condition_attributes` that converts
+    the existing attributes to a dataclass of type ConditioningAttributes.
+    """
+    def to_condition_attributes(self) -> ConditioningAttributes:
+        raise NotImplementedError()
+
+
+def nullify_condition(condition: ConditionType, dim: int = 1):
+    """Transform an input condition to a null condition.
+    The way it is done by converting it to a single zero vector similarly
+    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
+
+    Args:
+        condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor])
+        dim (int): The dimension that will be truncated (should be the time dimension)
+        WARNING!: dim should not be the batch dimension!
+    Returns:
+        ConditionType: A tuple of null condition and mask
+    """
+    assert dim != 0, "dim cannot be the batch dimension!"
+    assert isinstance(condition, tuple) and \
+        isinstance(condition[0], torch.Tensor) and \
+        isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!"
+    cond, mask = condition
+    B = cond.shape[0]
+    last_dim = cond.dim() - 1
+    out = cond.transpose(dim, last_dim)
+    out = 0. * out[..., :1]
+    out = out.transpose(dim, last_dim)
+    mask = torch.zeros((B, 1), device=out.device).int()
+    assert cond.dim() == out.dim()
+    return out, mask
+
+
+def nullify_wav(cond: WavCondition) -> WavCondition:
+    """Transform a WavCondition to a nullified WavCondition.
+    It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes.
+
+    Args:
+        cond (WavCondition): Wav condition with wav, tensor of shape [B, T].
+    Returns:
+        WavCondition: Nullified wav condition.
+    """
+    null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1)
+    return WavCondition(
+        wav=null_wav,
+        length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device),
+        sample_rate=cond.sample_rate,
+        path=[None] * cond.wav.shape[0],
+        seek_time=[None] * cond.wav.shape[0],
+    )
+
+
+def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition:
+    """Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0,
+    and replacing metadata by dummy attributes.
+
+    Args:
+        cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T].
+    """
+    null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1)
+    return JointEmbedCondition(
+        wav=null_wav, text=[None] * len(embed.text),
+        length=torch.LongTensor([0]).to(embed.wav.device),
+        sample_rate=embed.sample_rate,
+        path=[None] * embed.wav.shape[0],
+        seek_time=[0] * embed.wav.shape[0],
+    )
+
+
+class Tokenizer:
+    """Base tokenizer implementation
+    (in case we want to introduce more advances tokenizers in the future).
+    """
+    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        raise NotImplementedError()
+
+
+class WhiteSpaceTokenizer(Tokenizer):
+    """This tokenizer should be used for natural language descriptions.
+    For example:
+    ["he didn't, know he's going home.", 'shorter sentence'] =>
+    [[78, 62, 31,  4, 78, 25, 19, 34],
+    [59, 77,  0,  0,  0,  0,  0,  0]]
+    """
+    PUNCTUATION = "?:!.,;"
+
+    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
+                 lemma: bool = True, stopwords: bool = True) -> None:
+        self.n_bins = n_bins
+        self.pad_idx = pad_idx
+        self.lemma = lemma
+        self.stopwords = stopwords
+        try:
+            self.nlp = spacy.load(language)
+        except IOError:
+            spacy.cli.download(language)  # type: ignore
+            self.nlp = spacy.load(language)
+
+    @tp.no_type_check
+    def __call__(self, texts: tp.List[tp.Optional[str]],
+                 return_text: bool = False) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        """Take a list of strings and convert them to a tensor of indices.
+
+        Args:
+            texts (list[str]): List of strings.
+            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
+        Returns:
+            tuple[torch.Tensor, torch.Tensor]:
+                - Indices of words in the LUT.
+                - And a mask indicating where the padding tokens are
+        """
+        output, lengths = [], []
+        texts = deepcopy(texts)
+        for i, text in enumerate(texts):
+            # if current sample doesn't have a certain attribute, replace with pad token
+            if text is None:
+                output.append(torch.Tensor([self.pad_idx]))
+                lengths.append(0)
+                continue
+
+            # convert numbers to words
+            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
+            # normalize text
+            text = self.nlp(text)  # type: ignore
+            # remove stopwords
+            if self.stopwords:
+                text = [w for w in text if not w.is_stop]  # type: ignore
+            # remove punctuation
+            text = [w for w in text if w.text not in self.PUNCTUATION]  # type: ignore
+            # lemmatize if needed
+            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore
+
+            texts[i] = " ".join(text)
+            lengths.append(len(text))
+            # convert to tensor
+            tokens = torch.Tensor([hash_trick(w, self.n_bins) for w in text])
+            output.append(tokens)
+
+        mask = length_to_mask(torch.IntTensor(lengths)).int()
+        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
+        if return_text:
+            return padded_output, mask, texts  # type: ignore
+        return padded_output, mask
+
+
+class NoopTokenizer(Tokenizer):
+    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
+    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
+    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
+    split it to ["Jeff", "Buckley"] and return an index per word.
+
+    For example:
+    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
+    ["Metal", "Rock", "Classical"] => [0, 223, 51]
+    """
+    def __init__(self, n_bins: int, pad_idx: int = 0):
+        self.n_bins = n_bins
+        self.pad_idx = pad_idx
+
+    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        output, lengths = [], []
+        for text in texts:
+            # if current sample doesn't have a certain attribute, replace with pad token
+            if text is None:
+                output.append(self.pad_idx)
+                lengths.append(0)
+            else:
+                output.append(hash_trick(text, self.n_bins))
+                lengths.append(1)
+
+        tokens = torch.LongTensor(output).unsqueeze(1)
+        mask = length_to_mask(torch.IntTensor(lengths)).int()
+        return tokens, mask
+
+
+class BaseConditioner(nn.Module):
+    """Base model for all conditioner modules.
+    We allow the output dim to be different than the hidden dim for two reasons:
+    1) keep our LUTs small when the vocab is large;
+    2) make all condition dims consistent.
+
+    Args:
+        dim (int): Hidden dim of the model.
+        output_dim (int): Output dim of the conditioner.
+    """
+    def __init__(self, dim: int, output_dim: int):
+        super().__init__()
+        self.dim = dim
+        self.output_dim = output_dim
+        self.output_proj = nn.Linear(dim, output_dim)
+
+    def tokenize(self, *args, **kwargs) -> tp.Any:
+        """Should be any part of the processing that will lead to a synchronization
+        point, e.g. BPE tokenization with transfer to the GPU.
+
+        The returned value will be saved and return later when calling forward().
+        """
+        raise NotImplementedError()
+
+    def forward(self, inputs: tp.Any) -> ConditionType:
+        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
+        Outputs a ConditionType, after the input data was embedded as a dense vector.
+
+        Returns:
+            ConditionType:
+                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
+                  output embedding and D is the dimension of the embedding.
+                - And a mask indicating where the padding tokens.
+        """
+        raise NotImplementedError()
+
+
+class TextConditioner(BaseConditioner):
+    ...
+
+
+class LUTConditioner(TextConditioner):
+    """Lookup table TextConditioner.
+
+    Args:
+        n_bins (int): Number of bins.
+        dim (int): Hidden dim of the model (text-encoder/LUT).
+        output_dim (int): Output dim of the conditioner.
+        tokenizer (str): Name of the tokenizer.
+        pad_idx (int, optional): Index for padding token. Defaults to 0.
+    """
+    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
+        super().__init__(dim, output_dim)
+        self.embed = nn.Embedding(n_bins, dim)
+        self.tokenizer: Tokenizer
+        if tokenizer == 'whitespace':
+            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
+        elif tokenizer == 'noop':
+            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
+        else:
+            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
+
+    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        device = self.embed.weight.device
+        tokens, mask = self.tokenizer(x)
+        tokens, mask = tokens.to(device), mask.to(device)
+        return tokens, mask
+
+    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
+        tokens, mask = inputs
+        embeds = self.embed(tokens)
+        embeds = self.output_proj(embeds)
+        embeds = (embeds * mask.unsqueeze(-1))
+        return embeds, mask
+
+
+class T5Conditioner(TextConditioner):
+    """T5-based TextConditioner.
+
+    Args:
+        name (str): Name of the T5 model.
+        output_dim (int): Output dim of the conditioner.
+        finetune (bool): Whether to fine-tune T5 at train time.
+        device (str): Device for T5 Conditioner.
+        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
+        word_dropout (float, optional): Word dropout probability.
+        normalize_text (bool, optional): Whether to apply text normalization.
+    """
+    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
+              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
+              "google/flan-t5-xl", "google/flan-t5-xxl"]
+    MODELS_DIMS = {
+        "t5-small": 512,
+        "t5-base": 768,
+        "t5-large": 1024,
+        "t5-3b": 1024,
+        "t5-11b": 1024,
+        "google/flan-t5-small": 512,
+        "google/flan-t5-base": 768,
+        "google/flan-t5-large": 1024,
+        "google/flan-t5-3b": 1024,
+        "google/flan-t5-11b": 1024,
+    }
+
+    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
+                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
+                 normalize_text: bool = False):
+        assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})"
+        super().__init__(self.MODELS_DIMS[name], output_dim)
+        self.device = device
+        self.name = name
+        self.finetune = finetune
+        self.word_dropout = word_dropout
+        if autocast_dtype is None or self.device == 'cpu':
+            self.autocast = TorchAutocast(enabled=False)
+            if self.device != 'cpu':
+                logger.warning("T5 has no autocast, this might lead to NaN")
+        else:
+            dtype = getattr(torch, autocast_dtype)
+            assert isinstance(dtype, torch.dtype)
+            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
+            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
+        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
+        # thanks https://gist.github.com/simon-weber/7853144
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
+                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
+            finally:
+                logging.disable(previous_level)
+        if finetune:
+            self.t5 = t5
+        else:
+            # this makes sure that the t5 models is not part
+            # of the saved checkpoint
+            self.__dict__['t5'] = t5.to(device)
+
+        self.normalize_text = normalize_text
+        if normalize_text:
+            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
+
+    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
+        # if current sample doesn't have a certain attribute, replace with empty string
+        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
+        if self.normalize_text:
+            _, _, entries = self.text_normalizer(entries, return_text=True)
+        if self.word_dropout > 0. and self.training:
+            new_entries = []
+            for entry in entries:
+                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
+                new_entries.append(" ".join(words))
+            entries = new_entries
+
+        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
+
+        inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device)
+        mask = inputs['attention_mask']
+        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
+        return inputs
+
+    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
+        mask = inputs['attention_mask']
+        with torch.set_grad_enabled(self.finetune), self.autocast:
+            embeds = self.t5(**inputs).last_hidden_state
+        embeds = self.output_proj(embeds.to(self.output_proj.weight))
+        embeds = (embeds * mask.unsqueeze(-1))
+        return embeds, mask
+
+
+class WaveformConditioner(BaseConditioner):
+    """Base class for all conditioners that take a waveform as input.
+    Classes that inherit must implement `_get_wav_embedding` that outputs
+    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
+    factor of the embedding model.
+
+    Args:
+        dim (int): The internal representation dimension.
+        output_dim (int): Output dimension.
+        device (tp.Union[torch.device, str]): Device.
+    """
+    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
+        super().__init__(dim, output_dim)
+        self.device = device
+        # if False no masking is done, used in ChromaStemConditioner when completing by periodicity a sample.
+        self._use_masking = True
+
+    def tokenize(self, x: WavCondition) -> WavCondition:
+        wav, length, sample_rate, path, seek_time = x
+        assert length is not None
+        return WavCondition(wav.to(self.device), length.to(self.device), sample_rate, path, seek_time)
+
+    def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
+        """Gets as input a WavCondition and returns a dense embedding."""
+        raise NotImplementedError()
+
+    def _downsampling_factor(self):
+        """Returns the downsampling factor of the embedding model."""
+        raise NotImplementedError()
+
+    def forward(self, x: WavCondition) -> ConditionType:
+        """Extract condition embedding and mask from a waveform and its metadata.
+        Args:
+            x (WavCondition): Waveform condition containing raw waveform and metadata.
+        Returns:
+            ConditionType: a dense vector representing the conditioning along with its mask
+        """
+        wav, lengths, *_ = x
+        with torch.no_grad():
+            embeds = self._get_wav_embedding(x)
+        embeds = embeds.to(self.output_proj.weight)
+        embeds = self.output_proj(embeds)
+
+        if lengths is not None and self._use_masking:
+            lengths = lengths / self._downsampling_factor()
+            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
+        else:
+            mask = torch.ones_like(embeds[..., 0])
+        embeds = (embeds * mask.unsqueeze(-1))
+        return embeds, mask
+
+
+class ChromaStemConditioner(WaveformConditioner):
+    """Chroma conditioner based on stems.
+    The ChromaStemConditioner uses DEMUCS to first filter out drums and bass, as
+    the drums and bass often dominate the chroma leading to the chroma features
+    not containing information about the melody.
+
+    Args:
+        output_dim (int): Output dimension for the conditioner.
+        sample_rate (int): Sample rate for the chroma extractor.
+        n_chroma (int): Number of chroma bins for the chroma extractor.
+        radix2_exp (int): Size of stft window for the chroma extractor (power of 2, e.g. 12 -> 2^12).
+        duration (int): duration used during training. This is later used for correct padding
+            in case we are using chroma as prefix.
+        match_len_on_eval (bool, optional): if True then all chromas are padded to the training
+            duration. Defaults to False.
+        eval_wavs (str, optional): path to a dataset manifest with waveform, this waveforms are used as
+            conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
+            Defaults to None.
+        n_eval_wavs (int, optional): limits the number of waveforms used for conditioning. Defaults to 0.
+        device (tp.Union[torch.device, str], optional): Device for the conditioner.
+        **kwargs: Additional parameters for the chroma extractor.
+    """
+    def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
+                 duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
+                 n_eval_wavs: int = 0, cache_path: tp.Optional[tp.Union[str, Path]] = None,
+                 device: tp.Union[torch.device, str] = 'cpu', **kwargs):
+        from demucs import pretrained
+        super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
+        self.autocast = TorchAutocast(enabled=device != 'cpu', device_type=self.device, dtype=torch.float32)
+        self.sample_rate = sample_rate
+        self.match_len_on_eval = match_len_on_eval
+        if match_len_on_eval:
+            self._use_masking = False
+        self.duration = duration
+        self.__dict__['demucs'] = pretrained.get_model('htdemucs').to(device)
+        stem_sources: list = self.demucs.sources  # type: ignore
+        self.stem_indices = torch.LongTensor([stem_sources.index('vocals'), stem_sources.index('other')]).to(device)
+        self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma,
+                                      radix2_exp=radix2_exp, **kwargs).to(device)
+        self.chroma_len = self._get_chroma_len()
+        self.eval_wavs: tp.Optional[torch.Tensor] = self._load_eval_wavs(eval_wavs, n_eval_wavs)
+        self.cache = None
+        if cache_path is not None:
+            self.cache = EmbeddingCache(Path(cache_path) / 'wav', self.device,
+                                        compute_embed_fn=self._get_full_chroma_for_cache,
+                                        extract_embed_fn=self._extract_chroma_chunk)
+
+    def _downsampling_factor(self) -> int:
+        return self.chroma.winhop
+
+    def _load_eval_wavs(self, path: tp.Optional[str], num_samples: int) -> tp.Optional[torch.Tensor]:
+        """Load pre-defined waveforms from a json.
+        These waveforms will be used for chroma extraction during evaluation.
+        This is done to make the evaluation on MusicCaps fair (we shouldn't see the chromas of MusicCaps).
+        """
+        if path is None:
+            return None
+
+        logger.info(f"Loading evaluation wavs from {path}")
+        from audiocraft.data.audio_dataset import AudioDataset
+        dataset: AudioDataset = AudioDataset.from_meta(
+            path, segment_duration=self.duration, min_audio_duration=self.duration,
+            sample_rate=self.sample_rate, channels=1)
+
+        if len(dataset) > 0:
+            eval_wavs = dataset.collater([dataset[i] for i in range(num_samples)]).to(self.device)
+            logger.info(f"Using {len(eval_wavs)} evaluation wavs for chroma-stem conditioner")
+            return eval_wavs
+        else:
+            raise ValueError("Could not find evaluation wavs, check lengths of wavs")
+
+    def reset_eval_wavs(self, eval_wavs: tp.Optional[torch.Tensor]) -> None:
+        self.eval_wavs = eval_wavs
+
+    def has_eval_wavs(self) -> bool:
+        return self.eval_wavs is not None
+
+    def _sample_eval_wavs(self, num_samples: int) -> torch.Tensor:
+        """Sample wavs from a predefined list."""
+        assert self.eval_wavs is not None, "Cannot sample eval wavs as no eval wavs provided."
+        total_eval_wavs = len(self.eval_wavs)
+        out = self.eval_wavs
+        if num_samples > total_eval_wavs:
+            out = self.eval_wavs.repeat(num_samples // total_eval_wavs + 1, 1, 1)
+        return out[torch.randperm(len(out))][:num_samples]
+
+    def _get_chroma_len(self) -> int:
+        """Get length of chroma during training."""
+        dummy_wav = torch.zeros((1, int(self.sample_rate * self.duration)), device=self.device)
+        dummy_chr = self.chroma(dummy_wav)
+        return dummy_chr.shape[1]
+
+    @torch.no_grad()
+    def _get_stemmed_wav(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
+        """Get parts of the wav that holds the melody, extracting the main stems from the wav."""
+        from demucs.apply import apply_model
+        from demucs.audio import convert_audio
+        with self.autocast:
+            wav = convert_audio(
+                wav, sample_rate, self.demucs.samplerate, self.demucs.audio_channels)  # type: ignore
+            stems = apply_model(self.demucs, wav, device=self.device)
+            stems = stems[:, self.stem_indices]  # extract relevant stems for melody conditioning
+            mix_wav = stems.sum(1)  # merge extracted stems to single waveform
+            mix_wav = convert_audio(mix_wav, self.demucs.samplerate, self.sample_rate, 1)  # type: ignore
+            return mix_wav
+
+    @torch.no_grad()
+    def _extract_chroma(self, wav: torch.Tensor) -> torch.Tensor:
+        """Extract chroma features from the waveform."""
+        with self.autocast:
+            return self.chroma(wav)
+
+    @torch.no_grad()
+    def _compute_wav_embedding(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
+        """Compute wav embedding, applying stem and chroma extraction."""
+        # avoid 0-size tensors when we are working with null conds
+        if wav.shape[-1] == 1:
+            return self._extract_chroma(wav)
+        stems = self._get_stemmed_wav(wav, sample_rate)
+        chroma = self._extract_chroma(stems)
+        return chroma
+
+    @torch.no_grad()
+    def _get_full_chroma_for_cache(self, path: tp.Union[str, Path], x: WavCondition, idx: int) -> torch.Tensor:
+        """Extract chroma from the whole audio waveform at the given path."""
+        wav, sr = audio_read(path)
+        wav = wav[None].to(self.device)
+        wav = convert_audio(wav, sr, self.sample_rate, to_channels=1)
+        chroma = self._compute_wav_embedding(wav, self.sample_rate)[0]
+        return chroma
+
+    def _extract_chroma_chunk(self, full_chroma: torch.Tensor, x: WavCondition, idx: int) -> torch.Tensor:
+        """Extract a chunk of chroma from the full chroma derived from the full waveform."""
+        wav_length = x.wav.shape[-1]
+        seek_time = x.seek_time[idx]
+        assert seek_time is not None, (
+            "WavCondition seek_time is required "
+            "when extracting chroma chunks from pre-computed chroma.")
+        full_chroma = full_chroma.float()
+        frame_rate = self.sample_rate / self._downsampling_factor()
+        target_length = int(frame_rate * wav_length / self.sample_rate)
+        index = int(frame_rate * seek_time)
+        out = full_chroma[index: index + target_length]
+        out = F.pad(out[None], (0, 0, 0, target_length - out.shape[0]))[0]
+        return out.to(self.device)
+
+    @torch.no_grad()
+    def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
+        """Get the wav embedding from the WavCondition.
+        The conditioner will either extract the embedding on-the-fly computing it from the condition wav directly
+        or will rely on the embedding cache to load the pre-computed embedding if relevant.
+        """
+        sampled_wav: tp.Optional[torch.Tensor] = None
+        if not self.training and self.eval_wavs is not None:
+            warn_once(logger, "Using precomputed evaluation wavs!")
+            sampled_wav = self._sample_eval_wavs(len(x.wav))
+
+        no_undefined_paths = all(p is not None for p in x.path)
+        no_nullified_cond = x.wav.shape[-1] > 1
+        if sampled_wav is not None:
+            chroma = self._compute_wav_embedding(sampled_wav, self.sample_rate)
+        elif self.cache is not None and no_undefined_paths and no_nullified_cond:
+            paths = [Path(p) for p in x.path if p is not None]
+            chroma = self.cache.get_embed_from_cache(paths, x)
+        else:
+            assert all(sr == x.sample_rate[0] for sr in x.sample_rate), "All sample rates in batch should be equal."
+            chroma = self._compute_wav_embedding(x.wav, x.sample_rate[0])
+
+        if self.match_len_on_eval:
+            B, T, C = chroma.shape
+            if T > self.chroma_len:
+                chroma = chroma[:, :self.chroma_len]
+                logger.debug(f"Chroma was truncated to match length! ({T} -> {chroma.shape[1]})")
+            elif T < self.chroma_len:
+                n_repeat = int(math.ceil(self.chroma_len / T))
+                chroma = chroma.repeat(1, n_repeat, 1)
+                chroma = chroma[:, :self.chroma_len]
+                logger.debug(f"Chroma was repeated to match length! ({T} -> {chroma.shape[1]})")
+
+        return chroma
+
+    def tokenize(self, x: WavCondition) -> WavCondition:
+        """Apply WavConditioner tokenization and populate cache if needed."""
+        x = super().tokenize(x)
+        no_undefined_paths = all(p is not None for p in x.path)
+        if self.cache is not None and no_undefined_paths:
+            paths = [Path(p) for p in x.path if p is not None]
+            self.cache.populate_embed_cache(paths, x)
+        return x
+
+
+class JointEmbeddingConditioner(BaseConditioner):
+    """Joint embedding conditioning supporting both audio or text conditioning.
+
+    Args:
+        dim (int): Dimension.
+        output_dim (int): Output dimension.
+        device (str): Device.
+        attribute (str): Attribute used by the conditioner.
+        autocast_dtype (str): Autocast for the conditioner.
+        quantize (bool): Whether to quantize the CLAP embedding.
+        n_q (int): Number of residual quantizers (used if quantize is true).
+        bins (int): Quantizers' codebooks size (used if quantize is true).
+        kwargs: Additional parameters for residual vector quantizer.
+    """
+    def __init__(self, dim: int, output_dim: int, device: str, attribute: str,
+                 autocast_dtype: tp.Optional[str] = 'float32', quantize: bool = True,
+                 n_q: int = 12, bins: int = 1024, **kwargs):
+        super().__init__(dim=dim, output_dim=output_dim)
+        self.device = device
+        self.attribute = attribute
+        if autocast_dtype is None or device == 'cpu':
+            self.autocast = TorchAutocast(enabled=False)
+            logger.warning("JointEmbeddingConditioner has no autocast, this might lead to NaN.")
+        else:
+            dtype = getattr(torch, autocast_dtype)
+            assert isinstance(dtype, torch.dtype)
+            logger.info(f"JointEmbeddingConditioner will be evaluated with autocast as {autocast_dtype}.")
+            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
+        # residual vector quantizer to discretize the conditioned embedding
+        self.quantizer: tp.Optional[ResidualVectorQuantizer] = None
+        if quantize:
+            self.quantizer = ResidualVectorQuantizer(dim, n_q=n_q, bins=bins, **kwargs)
+
+    def _get_embed(self, x: JointEmbedCondition) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        """Get joint embedding in latent space from the inputs.
+
+        Returns:
+            tuple[torch.Tensor, torch.Tensor]: Tensor for the latent embedding
+                and corresponding empty indexes.
+        """
+        raise NotImplementedError()
+
+    def forward(self, x: JointEmbedCondition) -> ConditionType:
+        with self.autocast:
+            embed, empty_idx = self._get_embed(x)
+            if self.quantizer is not None:
+                embed = embed.view(-1, self.dim, 1)
+                q_res = self.quantizer(embed, frame_rate=1)
+                out_embed = q_res.x.view(-1, self.dim)
+            else:
+                out_embed = embed
+            out_embed = self.output_proj(out_embed).view(-1, 1, self.output_dim)
+            mask = torch.ones(*out_embed.shape[:2], device=out_embed.device)
+            mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
+            out_embed = (out_embed * mask.unsqueeze(-1))
+            return out_embed, mask
+
+    def tokenize(self, x: JointEmbedCondition) -> JointEmbedCondition:
+        return x
+
+
+class CLAPEmbeddingConditioner(JointEmbeddingConditioner):
+    """Joint Embedding conditioner based on pre-trained CLAP model.
+
+    This CLAP-based conditioner supports a caching mechanism
+    over the computed embeddings for faster training.
+
+    Args:
+        dim (int): Dimension.
+        output_dim (int): Output dimension.
+        device (str): Device.
+        attribute (str): Attribute used by the conditioner.
+        quantize (bool): Whether to quantize the CLAP embedding.
+        n_q (int): Number of residual quantizers (used if quantize is true).
+        bins (int): Quantizers' codebooks size (used if quantize is true).
+        checkpoint (str): Path to CLAP checkpoint.
+        model_arch (str): CLAP model architecture.
+        enable_fusion (bool): Enable fusion for CLAP model.
+        sample_rate (int): Sample rate used by CLAP model.
+        max_audio_length (float): Maximum audio length for CLAP model.
+        audio_stride (float): Stride to use for getting a CLAP embedding on the full sequence.
+        normalize (bool): Whether to normalize the CLAP embedding.
+        text_p (float): Probability of using text representation instead of audio at train time.
+        batch_size (Optional[int]): Batch size for CLAP embedding computation.
+        autocast_dtype (str): Autocast for the conditioner.
+        cache_path (Optional[str]): Path for pre-computed embeddings caching.
+        kwargs: Additional parameters for residual vector quantizer.
+    """
+    def __init__(self, dim: int, output_dim: int, device: str, attribute: str,
+                 quantize: bool, n_q: int, bins: int, checkpoint: tp.Union[str, Path], model_arch: str,
+                 enable_fusion: bool, sample_rate: int, max_audio_length: int, audio_stride: int,
+                 normalize: bool, text_p: bool, batch_size: tp.Optional[int] = None,
+                 autocast_dtype: tp.Optional[str] = 'float32', cache_path: tp.Optional[str] = None, **kwargs):
+        try:
+            import laion_clap  # type: ignore
+        except ImportError:
+            raise ImportError("Please install CLAP to use the CLAPEmbeddingConditioner: 'pip install laion_clap'")
+        warnings.warn("Sample rate for CLAP conditioner was fixed in version v1.1.0, (from 44.1 to 48 kHz). "
+                      "Please retrain all models.")
+        checkpoint = AudioCraftEnvironment.resolve_reference_path(checkpoint)
+        clap_tokenize = RobertaTokenizer.from_pretrained('roberta-base')
+        clap_model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=model_arch)
+        load_clap_state_dict(clap_model, checkpoint)
+        clap_model.eval()
+        clap_model.to(device)
+        super().__init__(dim=dim, output_dim=output_dim, device=device, attribute=attribute,
+                         autocast_dtype=autocast_dtype, quantize=quantize, n_q=n_q, bins=bins,
+                         **kwargs)
+        self.checkpoint = checkpoint
+        self.enable_fusion = enable_fusion
+        self.model_arch = model_arch
+        self.clap: laion_clap.CLAP_Module
+        self.clap_tokenize: RobertaTokenizer
+        self.clap_sample_rate = sample_rate
+        self.clap_max_frames = int(self.clap_sample_rate * max_audio_length)
+        self.clap_stride = int(self.clap_sample_rate * audio_stride)
+        self.batch_size = batch_size or 1
+        self.normalize = normalize
+        self.text_p = text_p
+        self.__dict__['clap_tokenize'] = clap_tokenize
+        self.__dict__['clap'] = clap_model
+        self.wav_cache, self.text_cache = None, None
+        if cache_path is not None:
+            self.wav_cache = EmbeddingCache(Path(cache_path) / 'wav', self.device,
+                                            compute_embed_fn=self._get_wav_embedding_for_cache,
+                                            extract_embed_fn=self._extract_wav_embedding_chunk)
+            self.text_cache = EmbeddingCache(Path(cache_path) / 'text', self.device,
+                                             compute_embed_fn=self._get_text_embedding_for_cache)
+
+    def _tokenizer(self, texts: tp.Union[str, tp.List[str]]) -> dict:
+        # we use the default params from CLAP module here as well
+        return self.clap_tokenize(texts, padding="max_length", truncation=True, max_length=77, return_tensors="pt")
+
+    def _compute_text_embedding(self, text: tp.List[str]) -> torch.Tensor:
+        """Compute text embedding from CLAP model on a given a batch of text.
+
+        Args:
+            text (list[str]): List of text for the batch, with B items.
+        Returns:
+            torch.Tensor: CLAP embedding derived from text, of shape [B, 1, D], with D the CLAP embedding dimension.
+        """
+        with torch.no_grad():
+            embed = self.clap.get_text_embedding(text, tokenizer=self._tokenizer, use_tensor=True)
+            return embed.view(embed.size(0), 1, embed.size(-1))
+
+    def _get_text_embedding_for_cache(self, path: tp.Union[Path, str],
+                                      x: JointEmbedCondition, idx: int) -> torch.Tensor:
+        """Get text embedding function for the cache."""
+        text = x.text[idx]
+        text = text if text is not None else ""
+        return self._compute_text_embedding([text])[0]
+
+    def _preprocess_wav(self, wav: torch.Tensor, length: torch.Tensor, sample_rates: tp.List[int]) -> torch.Tensor:
+        """Preprocess wav to expected format by CLAP model.
+
+        Args:
+            wav (torch.Tensor): Audio wav, of shape [B, C, T].
+            length (torch.Tensor): Actual length of the audio for each item in the batch, of shape [B].
+            sample_rates (list[int]): Sample rates for each sample in the batch
+        Returns:
+            torch.Tensor: Audio wav of shape [B, T].
+        """
+        assert wav.dim() == 3, "Expecting wav to be [B, C, T]"
+        if sample_rates is not None:
+            _wav = []
+            for i, audio in enumerate(wav):
+                sr = sample_rates[i]
+                audio = convert_audio(audio, from_rate=sr, to_rate=self.clap_sample_rate, to_channels=1)
+                _wav.append(audio)
+            wav = torch.stack(_wav, dim=0)
+        wav = wav.mean(dim=1)
+        return wav
+
+    def _compute_wav_embedding(self, wav: torch.Tensor, length: torch.Tensor,
+                               sample_rates: tp.List[int], reduce_mean: bool = False) -> torch.Tensor:
+        """Compute audio wave embedding from CLAP model.
+
+        Since CLAP operates on a fixed sequence length audio inputs and we need to process longer audio sequences,
+        we calculate the wav embeddings on `clap_max_frames` windows with `clap_stride`-second stride and
+        average the resulting embeddings.
+
+        Args:
+            wav (torch.Tensor): Audio wav, of shape [B, C, T].
+            length (torch.Tensor): Actual length of the audio for each item in the batch, of shape [B].
+            sample_rates (list[int]): Sample rates for each sample in the batch.
+            reduce_mean (bool): Whether to get the average tensor.
+        Returns:
+            torch.Tensor: Audio embedding of shape [B, F, D], F being the number of chunks, D the dimension.
+        """
+        with torch.no_grad():
+            wav = self._preprocess_wav(wav, length, sample_rates)
+            B, T = wav.shape
+            if T >= self.clap_max_frames:
+                wav = wav.unfold(-1, self.clap_max_frames, self.clap_stride)  # [B, F, T]
+            else:
+                wav = wav.view(-1, 1, T)  # [B, F, T] with F=1
+            wav = einops.rearrange(wav, 'b f t -> (b f) t')
+            embed_list = []
+            for i in range(0, wav.size(0), self.batch_size):
+                _wav = wav[i:i+self.batch_size, ...]
+                _embed = self.clap.get_audio_embedding_from_data(_wav, use_tensor=True)
+                embed_list.append(_embed)
+            embed = torch.cat(embed_list, dim=0)
+            embed = einops.rearrange(embed, '(b f) d -> b f d', b=B)
+            if reduce_mean:
+                embed = embed.mean(dim=1, keepdim=True)
+            return embed  # [B, F, D] with F=1 if reduce_mean is True
+
+    def _get_wav_embedding_for_cache(self, path: tp.Union[str, Path],
+                                     x: JointEmbedCondition, idx: int) -> torch.Tensor:
+        """Compute audio wave embedding for the cache.
+        The embedding is computed on a given audio read from file.
+
+        Args:
+            path (str or Path): Path to the full audio file.
+        Returns:
+            torch.Tensor: Single-item tensor of shape [F, D], F being the number of chunks, D the dimension.
+        """
+        wav, sr = audio_read(path)  # [C, T]
+        wav = wav.unsqueeze(0).to(self.device)  # [1, C, T]
+        wav_len = torch.LongTensor([wav.shape[-1]]).to(self.device)
+        embed = self._compute_wav_embedding(wav, wav_len, [sr], reduce_mean=False)  # [B, F, D]
+        return embed.squeeze(0)  # [F, D]
+
+    def _extract_wav_embedding_chunk(self, full_embed: torch.Tensor, x: JointEmbedCondition, idx: int) -> torch.Tensor:
+        """Extract the chunk of embedding matching the seek_time and length from the full CLAP audio embedding.
+
+        Args:
+            full_embed (torch.Tensor): CLAP embedding computed on the full wave, of shape [F, D].
+            x (JointEmbedCondition): Joint embedding condition for the full batch.
+            idx (int): Index considered for the given embedding to extract.
+        Returns:
+            torch.Tensor: Wav embedding averaged on sliding window, of shape [1, D].
+        """
+        sample_rate = x.sample_rate[idx]
+        seek_time = x.seek_time[idx]
+        seek_time = 0. if seek_time is None else seek_time
+        clap_stride = int(self.clap_stride / self.clap_sample_rate) * sample_rate
+        end_seek_time = seek_time + self.clap_max_frames / self.clap_sample_rate
+        start_offset = int(seek_time * sample_rate // clap_stride)
+        end_offset = int(end_seek_time * sample_rate // clap_stride)
+        wav_embed = full_embed[start_offset:end_offset, ...]
+        wav_embed = wav_embed.mean(dim=0, keepdim=True)
+        return wav_embed.to(self.device)  # [F, D]
+
+    def _get_text_embedding(self, x: JointEmbedCondition) -> torch.Tensor:
+        """Get CLAP embedding from a batch of text descriptions."""
+        no_nullified_cond = x.wav.shape[-1] > 1  # we don't want to read from cache when condition dropout
+        if self.text_cache is not None and no_nullified_cond:
+            assert all(p is not None for p in x.path), "Cache requires all JointEmbedCondition paths to be provided"
+            paths = [Path(p) for p in x.path if p is not None]
+            embed = self.text_cache.get_embed_from_cache(paths, x)
+        else:
+            text = [xi if xi is not None else "" for xi in x.text]
+            embed = self._compute_text_embedding(text)
+        if self.normalize:
+            embed = torch.nn.functional.normalize(embed, p=2.0, dim=-1)
+        return embed
+
+    def _get_wav_embedding(self, x: JointEmbedCondition) -> torch.Tensor:
+        """Get CLAP embedding from a batch of audio tensors (and corresponding sample rates)."""
+        no_undefined_paths = all(p is not None for p in x.path)
+        no_nullified_cond = x.wav.shape[-1] > 1  # we don't want to read from cache when condition dropout
+        if self.wav_cache is not None and no_undefined_paths and no_nullified_cond:
+            paths = [Path(p) for p in x.path if p is not None]
+            embed = self.wav_cache.get_embed_from_cache(paths, x)
+        else:
+            embed = self._compute_wav_embedding(x.wav, x.length, x.sample_rate, reduce_mean=True)
+        if self.normalize:
+            embed = torch.nn.functional.normalize(embed, p=2.0, dim=-1)
+        return embed
+
+    def tokenize(self, x: JointEmbedCondition) -> JointEmbedCondition:
+        # Trying to limit as much as possible sync points when the cache is warm.
+        no_undefined_paths = all(p is not None for p in x.path)
+        if self.wav_cache is not None and no_undefined_paths:
+            assert all([p is not None for p in x.path]), "Cache requires all JointEmbedCondition paths to be provided"
+            paths = [Path(p) for p in x.path if p is not None]
+            self.wav_cache.populate_embed_cache(paths, x)
+        if self.text_cache is not None and no_undefined_paths:
+            assert all([p is not None for p in x.path]), "Cache requires all JointEmbedCondition paths to be provided"
+            paths = [Path(p) for p in x.path if p is not None]
+            self.text_cache.populate_embed_cache(paths, x)
+        return x
+
+    def _get_embed(self, x: JointEmbedCondition) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        """Extract shared latent representation from either the wav or the text using CLAP."""
+        # decide whether to use text embedding at train time or not
+        use_text_embed = random.random() < self.text_p
+        if self.training and not use_text_embed:
+            embed = self._get_wav_embedding(x)
+            empty_idx = torch.LongTensor([])  # we assume we always have the audio wav
+        else:
+            embed = self._get_text_embedding(x)
+            empty_idx = torch.LongTensor([i for i, xi in enumerate(x.text) if xi is None or xi == ""])
+        return embed, empty_idx
+
+
+def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str) -> ConditioningAttributes:
+    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
+    If the condition is of type "wav", then nullify it using `nullify_condition` function.
+    If the condition is of any other type, set its value to None.
+    Works in-place.
+    """
+    if condition_type not in ['text', 'wav', 'joint_embed']:
+        raise ValueError(
+            "dropout_condition got an unexpected condition type!"
+            f" expected 'text', 'wav' or 'joint_embed' but got '{condition_type}'"
+        )
+
+    if condition not in getattr(sample, condition_type):
+        raise ValueError(
+            "dropout_condition received an unexpected condition!"
+            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
+            f" but got '{condition}' of type '{condition_type}'!"
+        )
+
+    if condition_type == 'wav':
+        wav_cond = sample.wav[condition]
+        sample.wav[condition] = nullify_wav(wav_cond)
+    elif condition_type == 'joint_embed':
+        embed = sample.joint_embed[condition]
+        sample.joint_embed[condition] = nullify_joint_embed(embed)
+    else:
+        sample.text[condition] = None
+
+    return sample
+
+
+class DropoutModule(nn.Module):
+    """Base module for all dropout modules."""
+    def __init__(self, seed: int = 1234):
+        super().__init__()
+        self.rng = torch.Generator()
+        self.rng.manual_seed(seed)
+
+
+class AttributeDropout(DropoutModule):
+    """Dropout with a given probability per attribute.
+    This is different from the behavior of ClassifierFreeGuidanceDropout as this allows for attributes
+    to be dropped out separately. For example, "artist" can be dropped while "genre" remains.
+    This is in contrast to ClassifierFreeGuidanceDropout where if "artist" is dropped "genre"
+    must also be dropped.
+
+    Args:
+        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
+            ...
+            "genre": 0.1,
+            "artist": 0.5,
+            "wav": 0.25,
+            ...
+        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
+        seed (int, optional): Random seed.
+    """
+    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
+        super().__init__(seed=seed)
+        self.active_on_eval = active_on_eval
+        # construct dict that return the values from p otherwise 0
+        self.p = {}
+        for condition_type, probs in p.items():
+            self.p[condition_type] = defaultdict(lambda: 0, probs)
+
+    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
+        """
+        Args:
+            samples (list[ConditioningAttributes]): List of conditions.
+        Returns:
+            list[ConditioningAttributes]: List of conditions after certain attributes were set to None.
+        """
+        if not self.training and not self.active_on_eval:
+            return samples
+
+        samples = deepcopy(samples)
+        for condition_type, ps in self.p.items():  # for condition types [text, wav]
+            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
+                if torch.rand(1, generator=self.rng).item() < p:
+                    for sample in samples:
+                        dropout_condition(sample, condition_type, condition)
+        return samples
+
+    def __repr__(self):
+        return f"AttributeDropout({dict(self.p)})"
+
+
+class ClassifierFreeGuidanceDropout(DropoutModule):
+    """Classifier Free Guidance dropout.
+    All attributes are dropped with the same probability.
+
+    Args:
+        p (float): Probability to apply condition dropout during training.
+        seed (int): Random seed.
+    """
+    def __init__(self, p: float, seed: int = 1234):
+        super().__init__(seed=seed)
+        self.p = p
+
+    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
+        """
+        Args:
+            samples (list[ConditioningAttributes]): List of conditions.
+        Returns:
+            list[ConditioningAttributes]: List of conditions after all attributes were set to None.
+        """
+        if not self.training:
+            return samples
+
+        # decide on which attributes to drop in a batched fashion
+        drop = torch.rand(1, generator=self.rng).item() < self.p
+        if not drop:
+            return samples
+
+        # nullify conditions of all attributes
+        samples = deepcopy(samples)
+        for condition_type in ["wav", "text"]:
+            for sample in samples:
+                for condition in sample.attributes[condition_type]:
+                    dropout_condition(sample, condition_type, condition)
+        return samples
+
+    def __repr__(self):
+        return f"ClassifierFreeGuidanceDropout(p={self.p})"
+
+
+class ConditioningProvider(nn.Module):
+    """Prepare and provide conditions given all the supported conditioners.
+
+    Args:
+        conditioners (dict): Dictionary of conditioners.
+        device (torch.device or str, optional): Device for conditioners and output condition types.
+    """
+    def __init__(self, conditioners: tp.Dict[str, BaseConditioner], device: tp.Union[torch.device, str] = "cpu"):
+        super().__init__()
+        self.device = device
+        self.conditioners = nn.ModuleDict(conditioners)
+
+    @property
+    def joint_embed_conditions(self):
+        return [m.attribute for m in self.conditioners.values() if isinstance(m, JointEmbeddingConditioner)]
+
+    @property
+    def has_joint_embed_conditions(self):
+        return len(self.joint_embed_conditions) > 0
+
+    @property
+    def text_conditions(self):
+        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
+
+    @property
+    def wav_conditions(self):
+        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
+
+    @property
+    def has_wav_condition(self):
+        return len(self.wav_conditions) > 0
+
+    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
+        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
+        This should be called before starting any real GPU work to avoid synchronization points.
+        This will return a dict matching conditioner names to their arbitrary tokenized representations.
+
+        Args:
+            inputs (list[ConditioningAttributes]): List of ConditioningAttributes objects containing
+                text and wav conditions.
+        """
+        assert all([isinstance(x, ConditioningAttributes) for x in inputs]), (
+            "Got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]",
+            f" but types were {set([type(x) for x in inputs])}"
+        )
+
+        output = {}
+        text = self._collate_text(inputs)
+        wavs = self._collate_wavs(inputs)
+        joint_embeds = self._collate_joint_embeds(inputs)
+
+        assert set(text.keys() | wavs.keys() | joint_embeds.keys()).issubset(set(self.conditioners.keys())), (
+            f"Got an unexpected attribute! Expected {self.conditioners.keys()}, ",
+            f"got {text.keys(), wavs.keys(), joint_embeds.keys()}"
+        )
+
+        for attribute, batch in chain(text.items(), wavs.items(), joint_embeds.items()):
+            output[attribute] = self.conditioners[attribute].tokenize(batch)
+        return output
+
+    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
+        """Compute pairs of `(embedding, mask)` using the configured conditioners and the tokenized representations.
+        The output is for example:
+        {
+            "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
+            "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
+            ...
+        }
+
+        Args:
+            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
+        """
+        output = {}
+        for attribute, inputs in tokenized.items():
+            condition, mask = self.conditioners[attribute](inputs)
+            output[attribute] = (condition, mask)
+        return output
+
+    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
+        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
+        are the attributes and the values are the aggregated input per attribute.
+        For example:
+        Input:
+        [
+            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
+            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
+        ]
+        Output:
+        {
+            "genre": ["Rock", "Hip-hop"],
+            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
+        }
+
+        Args:
+            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
+        Returns:
+            dict[str, list[str, optional]]: A dictionary mapping an attribute name to text batch.
+        """
+        out: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
+        texts = [x.text for x in samples]
+        for text in texts:
+            for condition in self.text_conditions:
+                out[condition].append(text[condition])
+        return out
+
+    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, WavCondition]:
+        """Generate a dict where the keys are attributes by which we fetch similar wavs,
+        and the values are Tensors of wavs according to said attributes.
+
+        *Note*: by the time the samples reach this function, each sample should have some waveform
+        inside the "wav" attribute. It should be either:
+        1. A real waveform
+        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
+        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
+
+        Args:
+            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
+        Returns:
+            dict[str, WavCondition]: A dictionary mapping an attribute name to wavs.
+        """
+        wavs = defaultdict(list)
+        lengths = defaultdict(list)
+        sample_rates = defaultdict(list)
+        paths = defaultdict(list)
+        seek_times = defaultdict(list)
+        out: tp.Dict[str, WavCondition] = {}
+
+        for sample in samples:
+            for attribute in self.wav_conditions:
+                wav, length, sample_rate, path, seek_time = sample.wav[attribute]
+                assert wav.dim() == 3, f"Got wav with dim={wav.dim()}, but expected 3 [1, C, T]"
+                assert wav.size(0) == 1, f"Got wav [B, C, T] with shape={wav.shape}, but expected B == 1"
+                # mono-channel conditioning
+                wav = wav.mean(1, keepdim=True)  # [1, 1, T]
+                wavs[attribute].append(wav.flatten())  # [T]
+                lengths[attribute].append(length)
+                sample_rates[attribute].extend(sample_rate)
+                paths[attribute].extend(path)
+                seek_times[attribute].extend(seek_time)
+
+        # stack all wavs to a single tensor
+        for attribute in self.wav_conditions:
+            stacked_wav, _ = collate(wavs[attribute], dim=0)
+            out[attribute] = WavCondition(
+                stacked_wav.unsqueeze(1), torch.cat(lengths[attribute]), sample_rates[attribute],
+                paths[attribute], seek_times[attribute])
+
+        return out
+
+    def _collate_joint_embeds(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, JointEmbedCondition]:
+        """Generate a dict where the keys are attributes by which we compute joint embeddings,
+        and the values are Tensors of pre-computed embeddings and the corresponding text attributes.
+
+        Args:
+            samples (list[ConditioningAttributes]): List of ConditioningAttributes samples.
+        Returns:
+            A dictionary mapping an attribute name to joint embeddings.
+        """
+        texts = defaultdict(list)
+        wavs = defaultdict(list)
+        lengths = defaultdict(list)
+        sample_rates = defaultdict(list)
+        paths = defaultdict(list)
+        seek_times = defaultdict(list)
+        channels: int = 0
+
+        out = {}
+        for sample in samples:
+            for attribute in self.joint_embed_conditions:
+                wav, text, length, sample_rate, path, seek_time = sample.joint_embed[attribute]
+                assert wav.dim() == 3
+                if channels == 0:
+                    channels = wav.size(1)
+                else:
+                    assert channels == wav.size(1), "not all audio has same number of channels in batch"
+                assert wav.size(0) == 1, "Expecting single-wav batch in the collate method"
+                wav = einops.rearrange(wav, "b c t -> (b c t)")  # [1, C, T] => [C * T]
+                wavs[attribute].append(wav)
+                texts[attribute].extend(text)
+                lengths[attribute].append(length)
+                sample_rates[attribute].extend(sample_rate)
+                paths[attribute].extend(path)
+                seek_times[attribute].extend(seek_time)
+
+        for attribute in self.joint_embed_conditions:
+            stacked_texts = texts[attribute]
+            stacked_paths = paths[attribute]
+            stacked_seek_times = seek_times[attribute]
+            stacked_wavs = pad_sequence(wavs[attribute]).to(self.device)
+            stacked_wavs = einops.rearrange(stacked_wavs, "(c t) b -> b c t", c=channels)
+            stacked_sample_rates = sample_rates[attribute]
+            stacked_lengths = torch.cat(lengths[attribute]).to(self.device)
+            assert stacked_lengths.size(0) == stacked_wavs.size(0)
+            assert len(stacked_sample_rates) == stacked_wavs.size(0)
+            assert len(stacked_texts) == stacked_wavs.size(0)
+            out[attribute] = JointEmbedCondition(
+                text=stacked_texts, wav=stacked_wavs,
+                length=stacked_lengths, sample_rate=stacked_sample_rates,
+                path=stacked_paths, seek_time=stacked_seek_times)
+
+        return out
+
+
+class ConditionFuser(StreamingModule):
+    """Condition fuser handles the logic to combine the different conditions
+    to the actual model input.
+
+    Args:
+        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
+            each condition. For example:
+            {
+                "prepend": ["description"],
+                "sum": ["genre", "bpm"],
+                "cross": ["description"],
+            }
+        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
+        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
+    """
+    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
+
+    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
+                 cross_attention_pos_emb_scale: float = 1.0):
+        super().__init__()
+        assert all(
+            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
+        ), f"Got invalid fuse method, allowed methods: {self.FUSING_METHODS}"
+        self.cross_attention_pos_emb = cross_attention_pos_emb
+        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
+        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
+        self.cond2fuse: tp.Dict[str, str] = {}
+        for fuse_method, conditions in fuse2cond.items():
+            for condition in conditions:
+                self.cond2fuse[condition] = fuse_method
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        conditions: tp.Dict[str, ConditionType]
+    ) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        """Fuse the conditions to the provided model input.
+
+        Args:
+            input (torch.Tensor): Transformer input.
+            conditions (dict[str, ConditionType]): Dict of conditions.
+        Returns:
+            tuple[torch.Tensor, torch.Tensor]: The first tensor is the transformer input
+                after the conditions have been fused. The second output tensor is the tensor
+                used for cross-attention or None if no cross attention inputs exist.
+        """
+        B, T, _ = input.shape
+
+        if 'offsets' in self._streaming_state:
+            first_step = False
+            offsets = self._streaming_state['offsets']
+        else:
+            first_step = True
+            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
+
+        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
+            f"given conditions contain unknown attributes for fuser, " \
+            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
+        cross_attention_output = None
+        for cond_type, (cond, cond_mask) in conditions.items():
+            op = self.cond2fuse[cond_type]
+            if op == 'sum':
+                input += cond
+            elif op == 'input_interpolate':
+                cond = einops.rearrange(cond, "b t d -> b d t")
+                cond = F.interpolate(cond, size=input.shape[1])
+                input += einops.rearrange(cond, "b d t -> b t d")
+            elif op == 'prepend':
+                if first_step:
+                    input = torch.cat([cond, input], dim=1)
+            elif op == 'cross':
+                if cross_attention_output is not None:
+                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
+                else:
+                    cross_attention_output = cond
+            else:
+                raise ValueError(f"unknown op ({op})")
+
+        if self.cross_attention_pos_emb and cross_attention_output is not None:
+            positions = torch.arange(
+                cross_attention_output.shape[1],
+                device=cross_attention_output.device
+            ).view(1, -1, 1)
+            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
+            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
+
+        if self._is_streaming:
+            self._streaming_state['offsets'] = offsets + T
+
+        return input, cross_attention_output

audiocraft/modules/conv.py

@@ -0,0 +1,243 @@
+# 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.
+
+import math
+import typing as tp
+import warnings
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import spectral_norm, weight_norm
+
+
+CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
+                                 'time_group_norm'])
+
+
+def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'weight_norm':
+        return weight_norm(module)
+    elif norm == 'spectral_norm':
+        return spectral_norm(module)
+    else:
+        # We already check was in CONV_NORMALIZATION, so any other choice
+        # doesn't need reparametrization.
+        return module
+
+
+def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
+    """Return the proper normalization module. If causal is True, this will ensure the returned
+    module is causal, or return an error if the normalization doesn't support causal evaluation.
+    """
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'time_group_norm':
+        if causal:
+            raise ValueError("GroupNorm doesn't support causal evaluation.")
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
+    else:
+        return nn.Identity()
+
+
+def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
+                                 padding_total: int = 0) -> int:
+    """See `pad_for_conv1d`."""
+    length = x.shape[-1]
+    n_frames = (length - kernel_size + padding_total) / stride + 1
+    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
+    return ideal_length - length
+
+
+def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
+    """Pad for a convolution to make sure that the last window is full.
+    Extra padding is added at the end. This is required to ensure that we can rebuild
+    an output of the same length, as otherwise, even with padding, some time steps
+    might get removed.
+    For instance, with total padding = 4, kernel size = 4, stride = 2:
+        0 0 1 2 3 4 5 0 0   # (0s are padding)
+        1   2   3           # (output frames of a convolution, last 0 is never used)
+        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
+            1 2 3 4         # once you removed padding, we are missing one time step !
+    """
+    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+    return F.pad(x, (0, extra_padding))
+
+
+def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right before the reflection happen.
+    """
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    if mode == 'reflect':
+        max_pad = max(padding_left, padding_right)
+        extra_pad = 0
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            x = F.pad(x, (0, extra_pad))
+        padded = F.pad(x, paddings, mode, value)
+        end = padded.shape[-1] - extra_pad
+        return padded[..., :end]
+    else:
+        return F.pad(x, paddings, mode, value)
+
+
+def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
+    """Remove padding from x, handling properly zero padding. Only for 1d!"""
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    assert (padding_left + padding_right) <= x.shape[-1]
+    end = x.shape[-1] - padding_right
+    return x[..., padding_left: end]
+
+
+class NormConv1d(nn.Module):
+    """Wrapper around Conv1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConv2d(nn.Module):
+    """Wrapper around Conv2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose1d(nn.Module):
+    """Wrapper around ConvTranspose1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose2d(nn.Module):
+    """Wrapper around ConvTranspose2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class StreamableConv1d(nn.Module):
+    """Conv1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, dilation: int = 1,
+                 groups: int = 1, bias: bool = True, causal: bool = False,
+                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
+                 pad_mode: str = 'reflect'):
+        super().__init__()
+        # warn user on unusual setup between dilation and stride
+        if stride > 1 and dilation > 1:
+            warnings.warn("StreamableConv1d has been initialized with stride > 1 and dilation > 1"
+                          f" (kernel_size={kernel_size} stride={stride}, dilation={dilation}).")
+        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
+                               dilation=dilation, groups=groups, bias=bias, causal=causal,
+                               norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.pad_mode = pad_mode
+
+    def forward(self, x):
+        B, C, T = x.shape
+        kernel_size = self.conv.conv.kernel_size[0]
+        stride = self.conv.conv.stride[0]
+        dilation = self.conv.conv.dilation[0]
+        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
+        padding_total = kernel_size - stride
+        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+        if self.causal:
+            # Left padding for causal
+            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
+        return self.conv(x)
+
+
+class StreamableConvTranspose1d(nn.Module):
+    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, causal: bool = False,
+                 norm: str = 'none', trim_right_ratio: float = 1.,
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
+        super().__init__()
+        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
+                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.trim_right_ratio = trim_right_ratio
+        assert self.causal or self.trim_right_ratio == 1., \
+            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
+        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
+
+    def forward(self, x):
+        kernel_size = self.convtr.convtr.kernel_size[0]
+        stride = self.convtr.convtr.stride[0]
+        padding_total = kernel_size - stride
+
+        y = self.convtr(x)
+
+        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
+        # removed at the very end, when keeping only the right length for the output,
+        # as removing it here would require also passing the length at the matching layer
+        # in the encoder.
+        if self.causal:
+            # Trim the padding on the right according to the specified ratio
+            # if trim_right_ratio = 1.0, trim everything from right
+            padding_right = math.ceil(padding_total * self.trim_right_ratio)
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        return y

audiocraft/modules/diffusion_schedule.py

@@ -0,0 +1,272 @@
+# 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.
+
+"""
+Functions for Noise Schedule, defines diffusion process, reverse process and data processor.
+"""
+
+from collections import namedtuple
+import random
+import typing as tp
+import julius
+import torch
+
+TrainingItem = namedtuple("TrainingItem", "noisy noise step")
+
+
+def betas_from_alpha_bar(alpha_bar):
+    alphas = torch.cat([torch.Tensor([alpha_bar[0]]), alpha_bar[1:]/alpha_bar[:-1]])
+    return 1 - alphas
+
+
+class SampleProcessor(torch.nn.Module):
+    def project_sample(self, x: torch.Tensor):
+        """Project the original sample to the 'space' where the diffusion will happen."""
+        return x
+
+    def return_sample(self, z: torch.Tensor):
+        """Project back from diffusion space to the actual sample space."""
+        return z
+
+
+class MultiBandProcessor(SampleProcessor):
+    """
+    MultiBand sample processor. The input audio is splitted across
+    frequency bands evenly distributed in mel-scale.
+
+    Each band will be rescaled to match the power distribution
+    of Gaussian noise in that band, using online metrics
+    computed on the first few samples.
+
+    Args:
+        n_bands (int): Number of mel-bands to split the signal over.
+        sample_rate (int): Sample rate of the audio.
+        num_samples (int): Number of samples to use to fit the rescaling
+            for each band. The processor won't be stable
+            until it has seen that many samples.
+        power_std (float or list/tensor): The rescaling factor computed to match the
+            power of Gaussian noise in each band is taken to
+            that power, i.e. `1.` means full correction of the energy
+            in each band, and values less than `1` means only partial
+            correction. Can be used to balance the relative importance
+            of low vs. high freq in typical audio signals.
+    """
+    def __init__(self, n_bands: int = 8, sample_rate: float = 24_000,
+                 num_samples: int = 10_000, power_std: tp.Union[float, tp.List[float], torch.Tensor] = 1.):
+        super().__init__()
+        self.n_bands = n_bands
+        self.split_bands = julius.SplitBands(sample_rate, n_bands=n_bands)
+        self.num_samples = num_samples
+        self.power_std = power_std
+        if isinstance(power_std, list):
+            assert len(power_std) == n_bands
+            power_std = torch.tensor(power_std)
+        self.register_buffer('counts', torch.zeros(1))
+        self.register_buffer('sum_x', torch.zeros(n_bands))
+        self.register_buffer('sum_x2', torch.zeros(n_bands))
+        self.register_buffer('sum_target_x2', torch.zeros(n_bands))
+        self.counts: torch.Tensor
+        self.sum_x: torch.Tensor
+        self.sum_x2: torch.Tensor
+        self.sum_target_x2: torch.Tensor
+
+    @property
+    def mean(self):
+        mean = self.sum_x / self.counts
+        return mean
+
+    @property
+    def std(self):
+        std = (self.sum_x2 / self.counts - self.mean**2).clamp(min=0).sqrt()
+        return std
+
+    @property
+    def target_std(self):
+        target_std = self.sum_target_x2 / self.counts
+        return target_std
+
+    def project_sample(self, x: torch.Tensor):
+        assert x.dim() == 3
+        bands = self.split_bands(x)
+        if self.counts.item() < self.num_samples:
+            ref_bands = self.split_bands(torch.randn_like(x))
+            self.counts += len(x)
+            self.sum_x += bands.mean(dim=(2, 3)).sum(dim=1)
+            self.sum_x2 += bands.pow(2).mean(dim=(2, 3)).sum(dim=1)
+            self.sum_target_x2 += ref_bands.pow(2).mean(dim=(2, 3)).sum(dim=1)
+        rescale = (self.target_std / self.std.clamp(min=1e-12)) ** self.power_std  # same output size
+        bands = (bands - self.mean.view(-1, 1, 1, 1)) * rescale.view(-1, 1, 1, 1)
+        return bands.sum(dim=0)
+
+    def return_sample(self, x: torch.Tensor):
+        assert x.dim() == 3
+        bands = self.split_bands(x)
+        rescale = (self.std / self.target_std) ** self.power_std
+        bands = bands * rescale.view(-1, 1, 1, 1) + self.mean.view(-1, 1, 1, 1)
+        return bands.sum(dim=0)
+
+
+class NoiseSchedule:
+    """Noise schedule for diffusion.
+
+    Args:
+        beta_t0 (float): Variance of the first diffusion step.
+        beta_t1 (float): Variance of the last diffusion step.
+        beta_exp (float): Power schedule exponent
+        num_steps (int): Number of diffusion step.
+        variance (str): choice of the sigma value for the denoising eq. Choices: "beta" or "beta_tilde"
+        clip (float): clipping value for the denoising steps
+        rescale (float): rescaling value to avoid vanishing signals unused by default (i.e 1)
+        repartition (str): shape of the schedule only power schedule is supported
+        sample_processor (SampleProcessor): Module that normalize data to match better the gaussian distribution
+        noise_scale (float): Scaling factor for the noise
+    """
+    def __init__(self, beta_t0: float = 1e-4, beta_t1: float = 0.02, num_steps: int = 1000, variance: str = 'beta',
+                 clip: float = 5., rescale: float = 1., device='cuda', beta_exp: float = 1,
+                 repartition: str = "power", alpha_sigmoid: dict = {}, n_bands: tp.Optional[int] = None,
+                 sample_processor: SampleProcessor = SampleProcessor(), noise_scale: float = 1.0, **kwargs):
+
+        self.beta_t0 = beta_t0
+        self.beta_t1 = beta_t1
+        self.variance = variance
+        self.num_steps = num_steps
+        self.clip = clip
+        self.sample_processor = sample_processor
+        self.rescale = rescale
+        self.n_bands = n_bands
+        self.noise_scale = noise_scale
+        assert n_bands is None
+        if repartition == "power":
+            self.betas = torch.linspace(beta_t0 ** (1 / beta_exp), beta_t1 ** (1 / beta_exp), num_steps,
+                                        device=device, dtype=torch.float) ** beta_exp
+        else:
+            raise RuntimeError('Not implemented')
+        self.rng = random.Random(1234)
+
+    def get_beta(self, step: tp.Union[int, torch.Tensor]):
+        if self.n_bands is None:
+            return self.betas[step]
+        else:
+            return self.betas[:, step]  # [n_bands, len(step)]
+
+    def get_initial_noise(self, x: torch.Tensor):
+        if self.n_bands is None:
+            return torch.randn_like(x)
+        return torch.randn((x.size(0), self.n_bands, x.size(2)))
+
+    def get_alpha_bar(self, step: tp.Optional[tp.Union[int, torch.Tensor]] = None) -> torch.Tensor:
+        """Return 'alpha_bar', either for a given step, or as a tensor with its value for each step."""
+        if step is None:
+            return (1 - self.betas).cumprod(dim=-1)  # works for simgle and multi bands
+        if type(step) is int:
+            return (1 - self.betas[:step + 1]).prod()
+        else:
+            return (1 - self.betas).cumprod(dim=0)[step].view(-1, 1, 1)
+
+    def get_training_item(self, x: torch.Tensor, tensor_step: bool = False) -> TrainingItem:
+        """Create a noisy data item for diffusion model training:
+
+        Args:
+            x (torch.Tensor): clean audio data torch.tensor(bs, 1, T)
+            tensor_step (bool): If tensor_step = false, only one step t is sample,
+                the whole batch is diffused to the same step and t is int.
+                If tensor_step = true, t is a tensor of size (x.size(0),)
+                every element of the batch is diffused to a independently sampled.
+        """
+        step: tp.Union[int, torch.Tensor]
+        if tensor_step:
+            bs = x.size(0)
+            step = torch.randint(0, self.num_steps, size=(bs,), device=x.device)
+        else:
+            step = self.rng.randrange(self.num_steps)
+        alpha_bar = self.get_alpha_bar(step)  # [batch_size, n_bands, 1]
+
+        x = self.sample_processor.project_sample(x)
+        noise = torch.randn_like(x)
+        noisy = (alpha_bar.sqrt() / self.rescale) * x + (1 - alpha_bar).sqrt() * noise * self.noise_scale
+        return TrainingItem(noisy, noise, step)
+
+    def generate(self, model: torch.nn.Module, initial: tp.Optional[torch.Tensor] = None,
+                 condition: tp.Optional[torch.Tensor] = None, return_list: bool = False):
+        """Full ddpm reverse process.
+
+        Args:
+            model (nn.Module): Diffusion model.
+            initial (tensor): Initial Noise.
+            condition (tensor): Input conditionning Tensor (e.g. encodec compressed representation).
+            return_list (bool): Whether to return the whole process or only the sampled point.
+        """
+        alpha_bar = self.get_alpha_bar(step=self.num_steps - 1)
+        current = initial
+        iterates = [initial]
+        for step in range(self.num_steps)[::-1]:
+            with torch.no_grad():
+                estimate = model(current, step, condition=condition).sample
+            alpha = 1 - self.betas[step]
+            previous = (current - (1 - alpha) / (1 - alpha_bar).sqrt() * estimate) / alpha.sqrt()
+            previous_alpha_bar = self.get_alpha_bar(step=step - 1)
+            if step == 0:
+                sigma2 = 0
+            elif self.variance == 'beta':
+                sigma2 = 1 - alpha
+            elif self.variance == 'beta_tilde':
+                sigma2 = (1 - previous_alpha_bar) / (1 - alpha_bar) * (1 - alpha)
+            elif self.variance == 'none':
+                sigma2 = 0
+            else:
+                raise ValueError(f'Invalid variance type {self.variance}')
+
+            if sigma2 > 0:
+                previous += sigma2**0.5 * torch.randn_like(previous) * self.noise_scale
+            if self.clip:
+                previous = previous.clamp(-self.clip, self.clip)
+            current = previous
+            alpha_bar = previous_alpha_bar
+            if step == 0:
+                previous *= self.rescale
+            if return_list:
+                iterates.append(previous.cpu())
+
+        if return_list:
+            return iterates
+        else:
+            return self.sample_processor.return_sample(previous)
+
+    def generate_subsampled(self, model: torch.nn.Module, initial: torch.Tensor, step_list: tp.Optional[list] = None,
+                            condition: tp.Optional[torch.Tensor] = None, return_list: bool = False):
+        """Reverse process that only goes through Markov chain states in step_list."""
+        if step_list is None:
+            step_list = list(range(1000))[::-50] + [0]
+        alpha_bar = self.get_alpha_bar(step=self.num_steps - 1)
+        alpha_bars_subsampled = (1 - self.betas).cumprod(dim=0)[list(reversed(step_list))].cpu()
+        betas_subsampled = betas_from_alpha_bar(alpha_bars_subsampled)
+        current = initial * self.noise_scale
+        iterates = [current]
+        for idx, step in enumerate(step_list[:-1]):
+            with torch.no_grad():
+                estimate = model(current, step, condition=condition).sample * self.noise_scale
+            alpha = 1 - betas_subsampled[-1 - idx]
+            previous = (current - (1 - alpha) / (1 - alpha_bar).sqrt() * estimate) / alpha.sqrt()
+            previous_alpha_bar = self.get_alpha_bar(step_list[idx + 1])
+            if step == step_list[-2]:
+                sigma2 = 0
+                previous_alpha_bar = torch.tensor(1.0)
+            else:
+                sigma2 = (1 - previous_alpha_bar) / (1 - alpha_bar) * (1 - alpha)
+            if sigma2 > 0:
+                previous += sigma2**0.5 * torch.randn_like(previous) * self.noise_scale
+            if self.clip:
+                previous = previous.clamp(-self.clip, self.clip)
+            current = previous
+            alpha_bar = previous_alpha_bar
+            if step == 0:
+                previous *= self.rescale
+            if return_list:
+                iterates.append(previous.cpu())
+        if return_list:
+            return iterates
+        else:
+            return self.sample_processor.return_sample(previous)

audiocraft/modules/lstm.py

@@ -0,0 +1,25 @@
+# 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 torch import nn
+
+
+class StreamableLSTM(nn.Module):
+    """LSTM without worrying about the hidden state, nor the layout of the data.
+    Expects input as convolutional layout.
+    """
+    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
+        super().__init__()
+        self.skip = skip
+        self.lstm = nn.LSTM(dimension, dimension, num_layers)
+
+    def forward(self, x):
+        x = x.permute(2, 0, 1)
+        y, _ = self.lstm(x)
+        if self.skip:
+            y = y + x
+        y = y.permute(1, 2, 0)
+        return y

audiocraft/modules/rope.py

@@ -0,0 +1,125 @@
+# 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.
+
+import typing as tp
+
+from torch import nn
+import torch
+
+
+class XPos(nn.Module):
+    """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
+    This applies an exponential decay to the RoPE rotation matrix.
+
+    Args:
+        dim (int): Embedding dimension.
+        smoothing (float): Smoothing factor applied to the decay rates.
+        base_scale (int): Base decay rate, given in terms of scaling time.
+        device (torch.device, optional): Device on which to initialize the module.
+        dtype (torch.dtype): dtype to use to generate the embedding.
+    """
+    def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
+                 device=None, dtype: torch.dtype = torch.float32):
+        super().__init__()
+        assert dim % 2 == 0
+        assert dtype in [torch.float64, torch.float32]
+        self.dtype = dtype
+        self.base_scale = base_scale
+
+        half_dim = dim // 2
+        adim = torch.arange(half_dim, device=device, dtype=dtype)
+        decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
+        self.register_buffer("decay_rates", decay_rates)
+        self.decay: tp.Optional[torch.Tensor] = None
+
+    def get_decay(self, start: int, end: int):
+        """Create complex decay tensor, cache values for fast computation."""
+        if self.decay is None or end > self.decay.shape[0]:
+            assert isinstance(self.decay_rates, torch.Tensor)  # Satisfy type checker.
+            idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
+            power = idx / self.base_scale
+            scale = self.decay_rates ** power.unsqueeze(-1)
+            self.decay = torch.polar(scale, torch.zeros_like(scale))
+        return self.decay[start:end]  # [T, C/2]
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
+
+    Args:
+        dim (int): Embedding dimension (twice the number of frequencies).
+        max_period (float): Maximum period of the rotation frequencies.
+        xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
+        scale (float): Scale of positional embedding, set to 0 to deactivate.
+        device (torch.device, optional): Device on which to initialize the module.
+        dtype (torch.dtype): dtype to use to generate the embedding.
+    """
+    def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
+                 scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
+        super().__init__()
+        assert dim % 2 == 0
+        self.scale = scale
+        assert dtype in [torch.float64, torch.float32]
+        self.dtype = dtype
+
+        adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
+        frequencies = 1.0 / (max_period ** (adim / dim))
+        self.register_buffer("frequencies", frequencies)
+        self.rotation: tp.Optional[torch.Tensor] = None
+
+        self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
+
+    def get_rotation(self, start: int, end: int):
+        """Create complex rotation tensor, cache values for fast computation."""
+        if self.rotation is None or end > self.rotation.shape[0]:
+            assert isinstance(self.frequencies, torch.Tensor)  # Satisfy type checker.
+            idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
+            angles = torch.outer(idx, self.frequencies)
+            self.rotation = torch.polar(torch.ones_like(angles), angles)
+        return self.rotation[start:end]
+
+    def rotate(self, x: torch.Tensor, start: int = 0, time_dim: int = 1, invert_decay: bool = False):
+        """Apply rope rotation to query or key tensor."""
+        T = x.shape[time_dim]
+        target_shape = [1] * x.dim()
+        target_shape[time_dim] = T
+        target_shape[-1] = -1
+        rotation = self.get_rotation(start, start + T).view(target_shape)
+
+        if self.xpos:
+            decay = self.xpos.get_decay(start, start + T).view(target_shape)
+        else:
+            decay = 1.0
+
+        if invert_decay:
+            decay = decay ** -1
+
+        x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
+        scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
+        x_out = torch.view_as_real(x_complex * scaled_rotation).view_as(x)
+
+        return x_out.type_as(x)
+
+    def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0, time_dim: int = 1):
+        """ Apply rope rotation to both query and key tensors.
+        Supports streaming mode, in which query and key are not expected to have the same shape.
+        In streaming mode, key will be of length [P + C] with P the cached past timesteps, but
+        query will be [C] (typically C == 1).
+
+        Args:
+            query (torch.Tensor): Query to rotate.
+            key (torch.Tensor): Key to rotate.
+            start (int): Start index of the sequence for time offset.
+            time_dim (int): which dimension represent the time steps.
+        """
+        query_timesteps = query.shape[time_dim]
+        key_timesteps = key.shape[time_dim]
+        streaming_offset = key_timesteps - query_timesteps
+
+        query_out = self.rotate(query, start + streaming_offset, time_dim)
+        key_out = self.rotate(key, start, time_dim, invert_decay=True)
+
+        return query_out, key_out

audiocraft/modules/seanet.py

@@ -0,0 +1,258 @@
+# 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.
+
+import typing as tp
+
+import numpy as np
+import torch.nn as nn
+
+from .conv import StreamableConv1d, StreamableConvTranspose1d
+from .lstm import StreamableLSTM
+
+
+class SEANetResnetBlock(nn.Module):
+    """Residual block from SEANet model.
+
+    Args:
+        dim (int): Dimension of the input/output.
+        kernel_sizes (list): List of kernel sizes for the convolutions.
+        dilations (list): List of dilations for the convolutions.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        true_skip (bool): Whether to use true skip connection or a simple
+            (streamable) convolution as the skip connection.
+    """
+    def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1],
+                 activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False,
+                 pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True):
+        super().__init__()
+        assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations'
+        act = getattr(nn, activation)
+        hidden = dim // compress
+        block = []
+        for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
+            in_chs = dim if i == 0 else hidden
+            out_chs = dim if i == len(kernel_sizes) - 1 else hidden
+            block += [
+                act(**activation_params),
+                StreamableConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation,
+                                 norm=norm, norm_kwargs=norm_params,
+                                 causal=causal, pad_mode=pad_mode),
+            ]
+        self.block = nn.Sequential(*block)
+        self.shortcut: nn.Module
+        if true_skip:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = StreamableConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params,
+                                             causal=causal, pad_mode=pad_mode)
+
+    def forward(self, x):
+        return self.shortcut(x) + self.block(x)
+
+
+class SEANetEncoder(nn.Module):
+    """SEANet encoder.
+
+    Args:
+        channels (int): Audio channels.
+        dimension (int): Intermediate representation dimension.
+        n_filters (int): Base width for the model.
+        n_residual_layers (int): nb of residual layers.
+        ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of
+            upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here
+            that must match the decoder order. We use the decoder order as some models may only employ the decoder.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        kernel_size (int): Kernel size for the initial convolution.
+        last_kernel_size (int): Kernel size for the initial convolution.
+        residual_kernel_size (int): Kernel size for the residual layers.
+        dilation_base (int): How much to increase the dilation with each layer.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        true_skip (bool): Whether to use true skip connection or a simple
+            (streamable) convolution as the skip connection in the residual network blocks.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        lstm (int): Number of LSTM layers at the end of the encoder.
+        disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm.
+            For the encoder, it corresponds to the N first blocks.
+    """
+    def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3,
+                 ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7,
+                 last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False,
+                 pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0,
+                 disable_norm_outer_blocks: int = 0):
+        super().__init__()
+        self.channels = channels
+        self.dimension = dimension
+        self.n_filters = n_filters
+        self.ratios = list(reversed(ratios))
+        del ratios
+        self.n_residual_layers = n_residual_layers
+        self.hop_length = np.prod(self.ratios)
+        self.n_blocks = len(self.ratios) + 2  # first and last conv + residual blocks
+        self.disable_norm_outer_blocks = disable_norm_outer_blocks
+        assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \
+            "Number of blocks for which to disable norm is invalid." \
+            "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0."
+
+        act = getattr(nn, activation)
+        mult = 1
+        model: tp.List[nn.Module] = [
+            StreamableConv1d(channels, mult * n_filters, kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks >= 1 else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+        # Downsample to raw audio scale
+        for i, ratio in enumerate(self.ratios):
+            block_norm = 'none' if self.disable_norm_outer_blocks >= i + 2 else norm
+            # Add residual layers
+            for j in range(n_residual_layers):
+                model += [
+                    SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1],
+                                      dilations=[dilation_base ** j, 1],
+                                      norm=block_norm, norm_params=norm_params,
+                                      activation=activation, activation_params=activation_params,
+                                      causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)]
+
+            # Add downsampling layers
+            model += [
+                act(**activation_params),
+                StreamableConv1d(mult * n_filters, mult * n_filters * 2,
+                                 kernel_size=ratio * 2, stride=ratio,
+                                 norm=block_norm, norm_kwargs=norm_params,
+                                 causal=causal, pad_mode=pad_mode),
+            ]
+            mult *= 2
+
+        if lstm:
+            model += [StreamableLSTM(mult * n_filters, num_layers=lstm)]
+
+        model += [
+            act(**activation_params),
+            StreamableConv1d(mult * n_filters, dimension, last_kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class SEANetDecoder(nn.Module):
+    """SEANet decoder.
+
+    Args:
+        channels (int): Audio channels.
+        dimension (int): Intermediate representation dimension.
+        n_filters (int): Base width for the model.
+        n_residual_layers (int): nb of residual layers.
+        ratios (Sequence[int]): kernel size and stride ratios.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        final_activation (str): Final activation function after all convolutions.
+        final_activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        kernel_size (int): Kernel size for the initial convolution.
+        last_kernel_size (int): Kernel size for the initial convolution.
+        residual_kernel_size (int): Kernel size for the residual layers.
+        dilation_base (int): How much to increase the dilation with each layer.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        true_skip (bool): Whether to use true skip connection or a simple.
+            (streamable) convolution as the skip connection in the residual network blocks.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        lstm (int): Number of LSTM layers at the end of the encoder.
+        disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm.
+            For the decoder, it corresponds to the N last blocks.
+        trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup.
+            If equal to 1.0, it means that all the trimming is done at the right.
+    """
+    def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3,
+                 ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None,
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7,
+                 last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False,
+                 pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0,
+                 disable_norm_outer_blocks: int = 0, trim_right_ratio: float = 1.0):
+        super().__init__()
+        self.dimension = dimension
+        self.channels = channels
+        self.n_filters = n_filters
+        self.ratios = ratios
+        del ratios
+        self.n_residual_layers = n_residual_layers
+        self.hop_length = np.prod(self.ratios)
+        self.n_blocks = len(self.ratios) + 2  # first and last conv + residual blocks
+        self.disable_norm_outer_blocks = disable_norm_outer_blocks
+        assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \
+            "Number of blocks for which to disable norm is invalid." \
+            "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0."
+
+        act = getattr(nn, activation)
+        mult = int(2 ** len(self.ratios))
+        model: tp.List[nn.Module] = [
+            StreamableConv1d(dimension, mult * n_filters, kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+
+        if lstm:
+            model += [StreamableLSTM(mult * n_filters, num_layers=lstm)]
+
+        # Upsample to raw audio scale
+        for i, ratio in enumerate(self.ratios):
+            block_norm = 'none' if self.disable_norm_outer_blocks >= self.n_blocks - (i + 1) else norm
+            # Add upsampling layers
+            model += [
+                act(**activation_params),
+                StreamableConvTranspose1d(mult * n_filters, mult * n_filters // 2,
+                                          kernel_size=ratio * 2, stride=ratio,
+                                          norm=block_norm, norm_kwargs=norm_params,
+                                          causal=causal, trim_right_ratio=trim_right_ratio),
+            ]
+            # Add residual layers
+            for j in range(n_residual_layers):
+                model += [
+                    SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1],
+                                      dilations=[dilation_base ** j, 1],
+                                      activation=activation, activation_params=activation_params,
+                                      norm=block_norm, norm_params=norm_params, causal=causal,
+                                      pad_mode=pad_mode, compress=compress, true_skip=true_skip)]
+
+            mult //= 2
+
+        # Add final layers
+        model += [
+            act(**activation_params),
+            StreamableConv1d(n_filters, channels, last_kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks >= 1 else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+        # Add optional final activation to decoder (eg. tanh)
+        if final_activation is not None:
+            final_act = getattr(nn, final_activation)
+            final_activation_params = final_activation_params or {}
+            model += [
+                final_act(**final_activation_params)
+            ]
+        self.model = nn.Sequential(*model)
+
+    def forward(self, z):
+        y = self.model(z)
+        return y

audiocraft/modules/streaming.py

@@ -0,0 +1,131 @@
+# 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.
+
+"""
+Streaming module API that should be implemented by all Streaming components,
+"""
+
+from contextlib import contextmanager
+import typing as tp
+from torch import nn
+import torch
+
+
+State = tp.Dict[str, torch.Tensor]
+
+
+class StreamingModule(nn.Module):
+    """Common API for streaming components.
+
+    Each streaming component has a streaming state, which is just a dict[str, Tensor].
+    By convention, the first dim of each tensor must be the batch size.
+    Don't use dots in the key names, as this would clash with submodules
+    (like in state_dict).
+
+    If `self._is_streaming` is True, the component should use and remember
+    the proper state inside `self._streaming_state`.
+
+    To set a streaming component in streaming state, use
+
+        with module.streaming():
+            ...
+
+    This will automatically reset the streaming state when exiting the context manager.
+    This also automatically propagates to all streaming children module.
+
+    Some module might also implement the `StreamingModule.flush` method, although
+    this one is trickier, as all parents module must be StreamingModule and implement
+    it as well for it to work properly. See `StreamingSequential` after.
+    """
+    def __init__(self) -> None:
+        super().__init__()
+        self._streaming_state: State = {}
+        self._is_streaming = False
+
+    def _apply_named_streaming(self, fn: tp.Any):
+        for name, module in self.named_modules():
+            if isinstance(module, StreamingModule):
+                fn(name, module)
+
+    def _set_streaming(self, streaming: bool):
+        def _set_streaming(name, module):
+            module._is_streaming = streaming
+        self._apply_named_streaming(_set_streaming)
+
+    @contextmanager
+    def streaming(self):
+        """Context manager to enter streaming mode. Reset streaming state on exit."""
+        self._set_streaming(True)
+        try:
+            yield
+        finally:
+            self._set_streaming(False)
+            self.reset_streaming()
+
+    def reset_streaming(self):
+        """Reset the streaming state."""
+        def _reset(name: str, module: StreamingModule):
+            module._streaming_state.clear()
+
+        self._apply_named_streaming(_reset)
+
+    def get_streaming_state(self) -> State:
+        """Return the streaming state, including that of sub-modules."""
+        state: State = {}
+
+        def _add(name: str, module: StreamingModule):
+            if name:
+                name += "."
+            for key, value in module._streaming_state.items():
+                state[name + key] = value
+
+        self._apply_named_streaming(_add)
+        return state
+
+    def set_streaming_state(self, state: State):
+        """Set the streaming state, including that of sub-modules."""
+        state = dict(state)
+
+        def _set(name: str, module: StreamingModule):
+            if name:
+                name += "."
+            module._streaming_state.clear()
+            for key, value in list(state.items()):
+                # complexity is not ideal here, but probably fine.
+                if key.startswith(name):
+                    local_key = key[len(name):]
+                    if '.' not in local_key:
+                        module._streaming_state[local_key] = value
+                        del state[key]
+
+        self._apply_named_streaming(_set)
+        assert len(state) == 0, list(state.keys())
+
+    def flush(self, x: tp.Optional[torch.Tensor] = None):
+        """Flush any remaining outputs that were waiting for completion.
+        Typically, for convolutions, this will add the final padding
+        and process the last buffer.
+
+        This should take an optional argument `x`, which will be provided
+        if a module before this one in the streaming pipeline has already
+        spitted out a flushed out buffer.
+        """
+        if x is None:
+            return None
+        else:
+            return self(x)
+
+
+class StreamingSequential(StreamingModule, nn.Sequential):
+    """A streaming compatible alternative of `nn.Sequential`.
+    """
+    def flush(self, x: tp.Optional[torch.Tensor] = None):
+        for module in self:
+            if isinstance(module, StreamingModule):
+                x = module.flush(x)
+            elif x is not None:
+                x = module(x)
+        return x

audiocraft/modules/transformer.py

@@ -0,0 +1,755 @@
+# 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.
+
+"""
+Transformer model, with streaming support, xformer attention support
+and easy causal attention with a potentially finite receptive field.
+
+See `StreamingTransformer` for more information.
+
+Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
+"""
+
+import typing as tp
+
+from einops import rearrange
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint as torch_checkpoint
+from xformers import ops
+
+from .rope import RotaryEmbedding
+from .streaming import StreamingModule
+
+_efficient_attention_backend: str = 'torch'
+
+
+def set_efficient_attention_backend(backend: str = 'torch'):
+    # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster).
+    global _efficient_attention_backend
+    assert _efficient_attention_backend in ['xformers', 'torch']
+    _efficient_attention_backend = backend
+
+
+def _get_attention_time_dimension(memory_efficient: bool) -> int:
+    if _efficient_attention_backend == 'torch' and memory_efficient:
+        return 2
+    else:
+        return 1
+
+
+def _is_profiled() -> bool:
+    # Return true if we are currently running with a xformers profiler activated.
+    try:
+        from xformers.profiler import profiler
+    except ImportError:
+        return False
+    return profiler._Profiler._CURRENT_PROFILER is not None
+
+
+def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
+    """Create normalization module for transformer encoder layer.
+
+    Args:
+        norm_type (str): Normalization method.
+        dim (int): Dimension of the normalized layer.
+        **kwargs (dict): Additional parameters for normalization layer.
+    Returns:
+        nn.Module: Normalization module.
+    """
+    if norm_type == 'layer_norm':
+        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
+    else:
+        raise ValueError(f"Unknown norm type: {norm_type}")
+
+
+def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
+                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
+    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
+
+    Args:
+        positions (torch.Tensor): LongTensor of positions.
+        dim (int): Dimension of the embedding.
+        max_period (float): Maximum period of the cosine/sine functions.
+        dtype (torch.dtype or str): dtype to use to generate the embedding.
+    Returns:
+        torch.Tensor: Sinusoidal positional embedding.
+    """
+    # We aim for BTC format
+    assert dim % 2 == 0
+    half_dim = dim // 2
+    positions = positions.to(dtype)
+    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
+    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
+    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
+    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
+
+
+def expand_repeated_kv(x: torch.Tensor, n_rep: int, memory_efficient: bool) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers."""
+    if n_rep == 1:
+        return x
+    if _efficient_attention_backend == 'torch' and memory_efficient:
+        bs, n_kv_heads, slen, head_dim = x.shape
+        return (
+            x[:, :, None, :, :]
+            .expand(bs, n_kv_heads, n_rep, slen, head_dim)
+            .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
+        )
+    else:
+        bs, slen, n_kv_heads, head_dim = x.shape
+        return (
+            x[:, :, :, None, :]
+            .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+            .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+        )
+
+
+class LayerScale(nn.Module):
+    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
+    This rescales diagonally the residual outputs close to 0, with a learnt scale.
+
+    Args:
+        channels (int): Number of channels.
+        init (float): Initial scale.
+        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
+        device (torch.device or str, optional): Device on which to initialize the module.
+        dtype (torch.dtype, optional): dtype to use to initialize the module.
+    """
+    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
+                 device=None, dtype=None):
+        super().__init__()
+        self.channel_last = channel_last
+        self.scale = nn.Parameter(
+            torch.full((channels,), init,
+                       requires_grad=True, device=device, dtype=dtype))
+
+    def forward(self, x: torch.Tensor):
+        if self.channel_last:
+            return self.scale * x
+        else:
+            return self.scale[:, None] * x
+
+
+class StreamingMultiheadAttention(StreamingModule):
+    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
+
+    Args:
+        embed_dim (int): Dimension to project to.
+        num_heads (int): Number of heads.
+        dropout (float): Dropout level.
+        bias (bool): Use bias in projections.
+        causal (bool): Causal mask applied automatically.
+        past_context (int, optional): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        rope (`RotaryEmbedding`, optional): Rope embedding to use.
+        cross_attention: Should be true when used as a cross attention.
+            All keys and values must be available at once, streaming is only for the queries.
+            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
+            interpret the time steps in the keys relative to those in the queries).
+        safe_streaming (bool): Bug fix, will go away with xformers update.
+        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
+        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
+            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
+        device (torch.device, optional): Device on which to initialize.
+        dtype (torch.dtype, optional): dtype to use.
+    """
+    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
+                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
+                 memory_efficient: bool = False, attention_as_float32: bool = False,
+                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
+                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
+                 device=None, dtype=None):
+        super().__init__()
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        if past_context is not None:
+            assert causal
+
+        self.embed_dim = embed_dim
+        self.causal = causal
+        self.past_context = past_context
+        self.memory_efficient = memory_efficient
+        self.attention_as_float32 = attention_as_float32
+        self.rope = rope
+        self.cross_attention = cross_attention
+        self.safe_streaming = safe_streaming
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.kv_repeat = kv_repeat
+        if cross_attention:
+            assert not causal, "Causal cannot work with cross attention."
+            assert rope is None, "Rope cannot work with cross attention."
+
+        if memory_efficient:
+            _verify_xformers_memory_efficient_compat()
+
+        self.custom = _is_custom(custom, memory_efficient)
+        if self.custom:
+            out_dim = embed_dim
+            assert num_heads % kv_repeat == 0
+            assert not cross_attention or kv_repeat == 1
+            num_kv = num_heads // kv_repeat
+            kv_dim = (embed_dim // num_heads) * num_kv
+            out_dim += 2 * kv_dim
+            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
+            # We try to follow the default PyTorch MHA convention, to easily compare results.
+            self.in_proj_weight = in_proj.weight
+            self.in_proj_bias = in_proj.bias
+            if bias:
+                self.in_proj_bias.data.zero_()  # Following Pytorch convention
+            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
+            if bias:
+                self.out_proj.bias.data.zero_()
+        else:
+            assert not qk_layer_norm
+            assert kv_repeat == 1
+            self.mha = nn.MultiheadAttention(
+                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
+                **factory_kwargs)
+        self.qk_layer_norm = qk_layer_norm
+        if qk_layer_norm:
+            assert self.custom
+            assert kv_repeat == 1
+            ln_dim = embed_dim
+            self.q_layer_norm = nn.LayerNorm(ln_dim)
+            self.k_layer_norm = nn.LayerNorm(ln_dim)
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        if not self.custom:
+            # Support compat with regular MHA
+            keys = [n for n, _ in self.mha.named_parameters()]
+            for key in keys:
+                if prefix + key in state_dict:
+                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
+        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
+    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
+        # Return a causal mask, accounting for potentially stored past keys/values
+        # We actually return a bias for the attention score, as this has the same
+        # convention both in the builtin MHA in Pytorch, and Xformers functions.
+        time_dim = _get_attention_time_dimension(self.memory_efficient)
+        if self.memory_efficient:
+            from xformers.ops import LowerTriangularMask
+            if current_steps == 1:
+                # If we only have one step, then we do not need a mask.
+                return None
+            elif 'past_keys' in self._streaming_state:
+                raise RuntimeError("Not supported at the moment")
+            else:
+                # Then we can safely use a lower triangular mask
+                return LowerTriangularMask()
+        if self._streaming_state:
+            past_keys = self._streaming_state['past_keys']
+            past_steps = past_keys.shape[time_dim]
+        else:
+            past_steps = 0
+
+        queries_pos = torch.arange(
+            past_steps, current_steps + past_steps, device=device).view(-1, 1)
+        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
+        delta = queries_pos - keys_pos
+        valid = delta >= 0
+        if self.past_context is not None:
+            valid &= (delta <= self.past_context)
+        return torch.where(
+            valid,
+            torch.zeros([], device=device, dtype=dtype),
+            torch.full([], float('-inf'), device=device, dtype=dtype))
+
+    def _complete_kv(self, k, v):
+        time_dim = _get_attention_time_dimension(self.memory_efficient)
+        if self.cross_attention:
+            # With cross attention we assume all keys and values
+            # are already available, and streaming is with respect
+            # to the queries only.
+            return k, v
+        # Complete the key/value pair using the streaming state.
+        if self._streaming_state:
+            pk = self._streaming_state['past_keys']
+            nk = torch.cat([pk, k], dim=time_dim)
+            if v is k:
+                nv = nk
+            else:
+                pv = self._streaming_state['past_values']
+                nv = torch.cat([pv, v], dim=time_dim)
+        else:
+            nk = k
+            nv = v
+
+        assert nk.shape[time_dim] == nv.shape[time_dim]
+        offset = 0
+        if self.past_context is not None:
+            offset = max(0, nk.shape[time_dim] - self.past_context)
+        if self._is_streaming:
+            self._streaming_state['past_keys'] = nk[:, offset:]
+            if v is not k:
+                self._streaming_state['past_values'] = nv[:, offset:]
+            if 'offset' in self._streaming_state:
+                self._streaming_state['offset'] += offset
+            else:
+                self._streaming_state['offset'] = torch.tensor(0)
+        return nk, nv
+
+    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
+        time_dim = _get_attention_time_dimension(self.memory_efficient)
+        # Apply rope embeddings to query and key tensors.
+        assert self.rope is not None
+        if 'past_keys' in self._streaming_state:
+            past_keys_offset = self._streaming_state['past_keys'].shape[1]
+        else:
+            past_keys_offset = 0
+        if 'offset' in self._streaming_state:
+            past_context_offset = int(self._streaming_state['offset'].item())
+        else:
+            past_context_offset = 0
+        streaming_offset = past_context_offset + past_keys_offset
+        return self.rope.rotate_qk(query, key, start=streaming_offset, time_dim=time_dim)
+
+    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
+                key_padding_mask=None, need_weights=False, attn_mask=None,
+                average_attn_weights=True, is_causal=False):
+        assert not is_causal, ("New param added in torch 2.0.1 not supported, "
+                               "use the causal args in the constructor.")
+
+        time_dim = _get_attention_time_dimension(self.memory_efficient)
+        if time_dim == 2:
+            layout = "b h t d"
+        else:
+            layout = "b t h d"
+        dtype = query.dtype
+        if self._is_streaming:
+            assert self.causal or self.cross_attention, \
+                "Streaming only available for causal or cross attention"
+
+        custom_attn_mask = attn_mask is not None
+
+        if self.causal:
+            assert attn_mask is None
+            # At the moment we specialize only for the self-attention case.
+            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
+            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
+            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
+
+        if self.custom:
+            # custom implementation
+            assert need_weights is False
+            assert key_padding_mask is None
+            if self.cross_attention:
+                # Different queries, keys, values, we have to spit manually the weights
+                # before applying the linear.
+                dim = self.in_proj_weight.shape[0] // 3
+                if self.in_proj_bias is None:
+                    bias_q, bias_k, bias_v = None, None, None
+                else:
+                    bias_q = self.in_proj_bias[:dim]
+                    bias_k = self.in_proj_bias[dim: 2 * dim]
+                    bias_v = self.in_proj_bias[2 * dim:]
+                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
+                # todo: when streaming, we could actually save k, v and check the shape actually match.
+                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
+                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
+                if self.qk_layer_norm is True:
+                    q = self.q_layer_norm(q)
+                    k = self.k_layer_norm(k)
+                q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]]
+            else:
+                if not _is_profiled():
+                    # profiling breaks that propertysomehow.
+                    assert query is key, "specialized implementation"
+                    assert value is key, "specialized implementation"
+                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
+                if self.kv_repeat == 1:
+                    if time_dim == 2:
+                        bound_layout = "b h p t d"
+                    else:
+                        bound_layout = "b t p h d"
+                    packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads)
+                    q, k, v = ops.unbind(packed, dim=2)
+                else:
+                    embed_dim = self.embed_dim
+                    per_head_dim = (embed_dim // self.num_heads)
+                    kv_heads = self.num_heads // self.kv_repeat
+                    q = projected[:, :, :embed_dim]
+                    start = embed_dim
+                    end = start + per_head_dim * kv_heads
+                    k = projected[:, :, start: end]
+                    v = projected[:, :, end:]
+                    q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads)
+                    k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads)
+                    v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads)
+
+                if self.qk_layer_norm is True:
+                    assert self.kv_repeat == 1
+                    q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]]
+                    q = self.q_layer_norm(q)
+                    k = self.k_layer_norm(k)
+                    q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]]
+                if self.rope:
+                    q, k = self._apply_rope(q, k)
+                k, v = self._complete_kv(k, v)
+                if self.kv_repeat > 1:
+                    k = expand_repeated_kv(k, self.kv_repeat, self.memory_efficient)
+                    v = expand_repeated_kv(v, self.kv_repeat, self.memory_efficient)
+            if self.attention_as_float32:
+                q, k, v = [x.float() for x in [q, k, v]]
+            if self.memory_efficient:
+                if custom_attn_mask:
+                    # When using a custom attn mask:
+                    # Move to query's device, repeat for each sample, remove align8 padding
+                    seq_len = query.shape[1]
+                    attn_mask = attn_mask.to(q.dtype)
+                    attn_mask = attn_mask.repeat((q.shape[0], 1, 1, 1))
+                    attn_mask = attn_mask[..., :seq_len, :seq_len]
+
+                p = self.dropout if self.training else 0
+                if _efficient_attention_backend == 'torch':
+                    x = torch.nn.functional.scaled_dot_product_attention(
+                        q, k, v, is_causal=attn_mask is not None, dropout_p=p)
+                else:
+                    x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
+            else:
+                # We include the dot product as float32, for consistency
+                # with the other implementations that include that step
+                # as part of the attention. Note that when using `autocast`,
+                # the einsums would be done as bfloat16, but the softmax
+                # would be done as bfloat16, so `attention_as_float32` will
+                # extend a bit the range of operations done in float32,
+                # although this should make no difference.
+                q = q / q.shape[-1] ** 0.5
+                key_layout = layout.replace('t', 'k')
+                query_layout = layout
+                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
+                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
+                        pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
+                else:
+                    pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
+                if attn_mask is not None:
+                    pre_w = pre_w + attn_mask
+                w = torch.softmax(pre_w, dim=-1)
+                w = F.dropout(w, self.dropout, training=self.training).to(v)
+                # Key and value have the same format.
+                x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v)
+            x = x.to(dtype)
+            x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads)
+            x = self.out_proj(x)
+        else:
+            key, value = self._complete_kv(key, value)
+            if self.attention_as_float32:
+                query, key, value = [x.float() for x in [query, key, value]]
+            x, _ = self.mha(
+                query, key, value, key_padding_mask,
+                need_weights, attn_mask, average_attn_weights)
+            x = x.to(dtype)
+
+        return x, None
+
+
+class StreamingTransformerLayer(nn.TransformerEncoderLayer):
+    """TransformerLayer with Streaming / Causal support.
+    This also integrates cross_attention, when passing `cross_attention=True`,
+    rather than having two separate classes like in PyTorch.
+
+    Args:
+        d_model (int): Dimension of the data.
+        num_heads (int): Number of heads.
+        dim_feedforward (int): Intermediate dimension of FF module.
+        dropout (float): Dropout both for MHA and FF.
+        bias_ff (bool): Use bias for FF.
+        bias_attn (bool): Use bias for MHA.
+        causal (bool): Causal mask applied automatically.
+        past_context (int, optional): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
+        qk_layer_norm_cross (bool): Same for the cross attention.
+        cross_attention (bool): If True, expect to get secondary input for cross-attention.
+            Cross attention will use the default MHA, as it typically won't require
+            special treatment.
+        layer_scale (float, optional): If not None, LayerScale will be used with
+            the given value as initial scale.
+        rope (`RotaryEmbedding`, optional): Rope embedding to use.
+        attention_dropout (float, optional): If not None, separate the value of the dimension dropout
+            in FFN and of the attention dropout.
+        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
+            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
+        device (torch.device, optional): Device on which to initialize.
+        dtype (torch.dtype, optional): dtype to use.
+        **kwargs: See `nn.TransformerEncoderLayer`.
+    """
+    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
+                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
+                 past_context: tp.Optional[int] = None, custom: bool = False,
+                 memory_efficient: bool = False, attention_as_float32: bool = False,
+                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
+                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
+                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
+                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
+        super().__init__(d_model, num_heads, dim_feedforward, dropout,
+                         device=device, dtype=dtype, batch_first=True, **kwargs)
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        # Redefine self_attn to our streaming multi-head attention
+        attn_kwargs: tp.Dict[str, tp.Any] = {
+            'embed_dim': d_model,
+            'num_heads': num_heads,
+            'dropout': dropout if attention_dropout is None else attention_dropout,
+            'bias': bias_attn,
+            'custom': custom,
+            'memory_efficient': memory_efficient,
+            'attention_as_float32': attention_as_float32,
+        }
+        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
+            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
+            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
+        # Redefine feedforward layers to expose bias parameter
+        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
+        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
+
+        self.layer_scale_1: nn.Module
+        self.layer_scale_2: nn.Module
+        if layer_scale is None:
+            self.layer_scale_1 = nn.Identity()
+            self.layer_scale_2 = nn.Identity()
+        else:
+            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
+            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
+
+        self.cross_attention: tp.Optional[nn.Module] = None
+        if cross_attention:
+            self.cross_attention = StreamingMultiheadAttention(
+                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
+                **attn_kwargs, **factory_kwargs)
+            # Norm and dropout
+            self.dropout_cross = nn.Dropout(dropout)
+            # eps value matching that used in PyTorch reference implementation.
+            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
+            self.layer_scale_cross: nn.Module
+            if layer_scale is None:
+                self.layer_scale_cross = nn.Identity()
+            else:
+                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
+        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
+        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
+
+    def _cross_attention_block(self, src: torch.Tensor,
+                               cross_attention_src: torch.Tensor) -> torch.Tensor:
+        assert self.cross_attention is not None
+        # queries are from src, keys and values from cross_attention_src.
+        x = self.cross_attention(
+            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
+        return self.dropout_cross(x)  # type: ignore
+
+    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
+                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
+                cross_attention_src: tp.Optional[torch.Tensor] = None):
+        if self.cross_attention is None:
+            assert cross_attention_src is None
+        else:
+            assert cross_attention_src is not None
+        x = src
+        if self.norm_first:
+            x = x + self.layer_scale_1(
+                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
+            if cross_attention_src is not None:
+                x = x + self.layer_scale_cross(
+                    self._cross_attention_block(
+                        self.norm_cross(x), cross_attention_src))
+            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
+        else:
+            x = self.norm1(x + self.layer_scale_1(
+                self._sa_block(x, src_mask, src_key_padding_mask)))
+            if cross_attention_src is not None:
+                x = self.norm_cross(
+                    x + self.layer_scale_cross(
+                        self._cross_attention_block(src, cross_attention_src)))
+            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
+        return x
+
+
+class StreamingTransformer(StreamingModule):
+    """Transformer with Streaming / Causal support.
+
+    Args:
+        d_model (int): Dimension of the data.
+        num_heads (int): Number of heads.
+        dim_feedforward (int): Intermediate dimension of FF module.
+        dropout (float): Dropout both for MHA and FF.
+        bias_ff (bool): Use bias for FF.
+        bias_attn (bool): Use bias for MHA.
+        causal (bool): Causal mask applied automatically.
+        past_context (int, optional): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        cross_attention (bool): If True, expect to get secondary input for cross-attention.
+        layer_scale (float, optional): If not None, LayerScale will be used
+            with the given value as initial scale.
+        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
+        max_period (float): Maximum period of the time embedding.
+        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
+        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
+        lr (float, optional): learning rate override through the `make_optim_group` API.
+        weight_decay (float, optional): Weight_decay override through the `make_optim_group` API.
+        layer_class: (subclass of `StreamingTransformerLayer): class to use
+            to initialize the layers, allowing further customization outside of AudioCraft.
+        checkpointing (str): Checkpointing strategy to reduce memory usage.
+            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
+            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
+            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
+            a policy for opting-out some operations of the checkpointing like
+            linear layers and attention, providing a middle ground between speed and memory.
+        device (torch.device, optional): Device on which to initialize.
+        dtype (torch.dtype, optional): dtype to use.
+        **kwargs: See `nn.TransformerEncoderLayer`.
+    """
+    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
+                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
+                 causal: bool = False, past_context: tp.Optional[int] = None,
+                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
+                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
+                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
+                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
+                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
+                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
+        super().__init__()
+        assert d_model % num_heads == 0
+
+        self.positional_embedding = positional_embedding
+        self.max_period = max_period
+        self.positional_scale = positional_scale
+        self.weight_decay = weight_decay
+        self.lr = lr
+
+        assert positional_embedding in ['sin', 'rope', 'sin_rope']
+        self.rope: tp.Optional[RotaryEmbedding] = None
+        if self.positional_embedding in ['rope', 'sin_rope']:
+            assert _is_custom(custom, memory_efficient)
+            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
+                                        xpos=xpos, scale=positional_scale, device=device)
+
+        self.checkpointing = checkpointing
+
+        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
+        if self.checkpointing.startswith('xformers'):
+            _verify_xformers_internal_compat()
+
+        self.layers = nn.ModuleList()
+        for idx in range(num_layers):
+            self.layers.append(
+                layer_class(
+                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
+                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
+                    causal=causal, past_context=past_context, custom=custom,
+                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
+                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
+                    device=device, dtype=dtype, **kwargs))
+
+        if self.checkpointing != 'none':
+            for layer in self.layers:
+                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
+                # backward hook inside of FSDP...
+                layer._magma_checkpointed = True  # type: ignore
+
+    def _apply_layer(self, layer, *args, **kwargs):
+        method = self.checkpointing
+        if method == 'none':
+            return layer(*args, **kwargs)
+        elif method == 'torch':
+            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
+        elif method.startswith('xformers'):
+            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
+            if method == 'xformers_default':
+                # those operations will be saved, and not recomputed.
+                # According to Francisco we can get smarter policies but this is a good start.
+                allow_list = [
+                    "xformers.efficient_attention_forward_cutlass.default",
+                    "xformers_flash.flash_fwd.default",
+                    "aten.addmm.default",
+                    "aten.mm.default",
+                ]
+            elif method == 'xformers_mm':
+                # those operations will be saved, and not recomputed.
+                # According to Francisco we can get smarter policies but this is a good start.
+                allow_list = [
+                    "aten.addmm.default",
+                    "aten.mm.default",
+                ]
+            else:
+                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
+            policy_fn = _get_default_policy(allow_list)
+            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
+        else:
+            raise ValueError(f"Checkpointing method {method} is unknown.")
+
+    def forward(self, x: torch.Tensor, *args, **kwargs):
+        B, T, C = x.shape
+
+        if 'offsets' in self._streaming_state:
+            offsets = self._streaming_state['offsets']
+        else:
+            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
+
+        if self.positional_embedding in ['sin', 'sin_rope']:
+            positions = torch.arange(T, device=x.device).view(1, -1, 1)
+            positions = positions + offsets.view(-1, 1, 1)
+            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
+            x = x + self.positional_scale * pos_emb
+
+        for layer in self.layers:
+            x = self._apply_layer(layer, x, *args, **kwargs)
+
+        if self._is_streaming:
+            self._streaming_state['offsets'] = offsets + T
+
+        return x
+
+    def make_optim_group(self):
+        group = {"params": list(self.parameters())}
+        if self.lr is not None:
+            group["lr"] = self.lr
+        if self.weight_decay is not None:
+            group["weight_decay"] = self.weight_decay
+        return group
+
+
+# special attention related function
+
+def _verify_xformers_memory_efficient_compat():
+    try:
+        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
+    except ImportError:
+        raise ImportError(
+            "xformers is not installed. Please install it and try again.\n"
+            "To install on AWS and Azure, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
+            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
+            "To install on FAIR Cluster, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
+            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
+
+
+def _verify_xformers_internal_compat():
+    try:
+        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
+    except ImportError:
+        raise ImportError(
+            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
+            "To install on AWS and Azure, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
+            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
+            "To install on FAIR Cluster, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
+            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
+
+
+def _is_custom(custom: bool, memory_efficient: bool):
+    return custom or memory_efficient