higgs_audio/audio_processing/quantization/core_vq_lsx_version.py

# Copyright (c)
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This implementation is inspired from
# https://github.com/rosinality/vq-vae-2-pytorch/blob/master/vqvae.py and
# https://github.com/clementchadebec/benchmark_VAE/blob/dfa0dcf6c79172df5d27769c09c860c42008baaa/src/pythae/models/vq_vae/vq_vae_utils.py#L81
#
# 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.
#
# This implementation is inspired from
# https://github.com/lucidrains/vector-quantize-pytorch
# which is released under MIT License. Hereafter, the original license:
# MIT License
#
# Copyright (c) 2020 Phil Wang
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.

"""Core vector quantization implementation."""

import typing as tp

from einops import rearrange
import torch
from torch import nn
import torch.nn.functional as F
import torch.distributed as dist

from .distrib import broadcast_tensors, is_distributed
from .ddp_utils import SyncFunction


def default(val: tp.Any, d: tp.Any) -> tp.Any:
    return val if val is not None else d


def ema_inplace(moving_avg, new, decay: float):
    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))


def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
    return (x + epsilon) / (x.sum() + n_categories * epsilon)


def uniform_init(*shape: int):
    t = torch.empty(shape)
    nn.init.kaiming_uniform_(t)
    return t


def sample_vectors(samples, num: int):
    num_samples, device = samples.shape[0], samples.device

    if num_samples >= num:
        indices = torch.randperm(num_samples, device=device)[:num]
    else:
        indices = torch.randint(0, num_samples, (num,), device=device)

    return samples[indices]


def kmeans(
    samples,
    num_clusters: int,
    num_iters: int = 10,
    frames_to_use: int = 10_000,
    batch_size: int = 64,
):
    """
    Memory-efficient K-means clustering.
    Args:
        samples (tensor): shape [N, D]
        num_clusters (int): number of centroids.
        num_iters (int): number of iterations.
        frames_to_use (int): subsample size from total samples.
        batch_size (int): batch size used in distance computation.
    Returns:
        means: [num_clusters, D]
        bins: [num_clusters] (number of points per cluster)
    """
    N, D = samples.shape
    dtype, device = samples.dtype, samples.device

    if frames_to_use < N:
        indices = torch.randperm(N, device=device)[:frames_to_use]
        samples = samples[indices]

    means = sample_vectors(samples, num_clusters)

    for _ in range(num_iters):
        # Store cluster assignments
        all_assignments = []

        for i in range(0, samples.shape[0], batch_size):
            batch = samples[i : i + batch_size]  # [B, D]
            dists = torch.cdist(batch, means, p=2)  # [B, C]
            assignments = dists.argmin(dim=1)  # [B]
            all_assignments.append(assignments)

        buckets = torch.cat(all_assignments, dim=0)  # [N]
        bins = torch.bincount(buckets, minlength=num_clusters)
        zero_mask = bins == 0
        bins_min_clamped = bins.masked_fill(zero_mask, 1)

        # Compute new means
        new_means = torch.zeros_like(means)
        for i in range(num_clusters):
            mask = buckets == i
            if mask.any():
                new_means[i] = samples[mask].mean(dim=0)

        means = torch.where(zero_mask[:, None], means, new_means)

    return means, bins


class EuclideanCodebook(nn.Module):
    """Codebook with Euclidean distance.
    Args:
        dim (int): Dimension.
        codebook_size (int): Codebook size.
        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
            If set to true, run the k-means algorithm on the first training batch and use
            the learned centroids as initialization.
        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
        decay (float): Decay for exponential moving average over the codebooks.
        epsilon (float): Epsilon value for numerical stability.
        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
            that have an exponential moving average cluster size less than the specified threshold with
            randomly selected vector from the current batch.
    """

    def __init__(
        self,
        dim: int,
        codebook_size: int,
        kmeans_init: int = False,
        kmeans_iters: int = 10,
        decay: float = 0.99,
        epsilon: float = 1e-5,
        threshold_ema_dead_code: int = 2,
    ):
        super().__init__()
        self.decay = decay
        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
        embed = init_fn(codebook_size, dim)

        self.codebook_size = codebook_size

        self.kmeans_iters = kmeans_iters
        self.epsilon = epsilon
        self.threshold_ema_dead_code = threshold_ema_dead_code

        # Flag variable to indicate whether the codebook is initialized
        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
        # Runing EMA cluster size/count: N_i^t in eq. (6) in vqvae paper
        self.register_buffer("cluster_size", torch.zeros(codebook_size))
        # Codebook
        self.register_buffer("embed", embed)
        # EMA codebook: eq. (7) in vqvae paper
        self.register_buffer("embed_avg", embed.clone())

    @torch.jit.ignore
    def init_embed_(self, data):
        """Initialize codebook.
        Args:
            data (tensor): [B * T, D].
        """
        if self.inited:
            return

        ## NOTE (snippet added by Songxiang Liu): gather data from all gpus
        if dist.is_available() and dist.is_initialized():
            # [B * T * world_size, D]
            data = SyncFunction.apply(data)

        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
        self.embed.data.copy_(embed)
        self.embed_avg.data.copy_(embed.clone())
        self.cluster_size.data.copy_(cluster_size)
        self.inited.data.copy_(torch.Tensor([True]))
        # Make sure all buffers across workers are in sync after initialization
        broadcast_tensors(self.buffers())

    def replace_(self, samples, mask):
        modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
        self.embed.data.copy_(modified_codebook)

    def expire_codes_(self, batch_samples):
        if self.threshold_ema_dead_code == 0:
            return

        expired_codes = self.cluster_size < self.threshold_ema_dead_code
        if not torch.any(expired_codes):
            return

        ## NOTE (snippet added by Songxiang Liu): gather data from all gpus
        if is_distributed():
            # [B * T * world_size, D]
            batch_samples = SyncFunction.apply(batch_samples)

        batch_samples = rearrange(batch_samples, "... d -> (...) d")
        self.replace_(batch_samples, mask=expired_codes)
        broadcast_tensors(self.buffers())

    def preprocess(self, x):
        x = rearrange(x, "... d -> (...) d")
        return x

    def quantize(self, x):
        embed = self.embed.t()
        dist = -(x.pow(2).sum(1, keepdim=True) - 2 * x @ embed + embed.pow(2).sum(0, keepdim=True))
        embed_ind = dist.max(dim=-1).indices
        return embed_ind

    def postprocess_emb(self, embed_ind, shape):
        return embed_ind.view(*shape[:-1])

    def dequantize(self, embed_ind):
        quantize = F.embedding(embed_ind, self.embed)
        return quantize

    def encode(self, x):
        shape = x.shape
        # pre-process
        x = self.preprocess(x)  # [B, T, D] -> [B*T, D]
        # quantize
        embed_ind = self.quantize(x)
        # post-process
        embed_ind = self.postprocess_emb(embed_ind, shape)
        return embed_ind

    def decode(self, embed_ind):
        quantize = self.dequantize(embed_ind)
        return quantize

    def forward(self, x):
        # shape: [B, T, D]
        shape, dtype = x.shape, x.dtype
        x = self.preprocess(x)  # [B, T, D] -> [B*T, D]

        # Initialize codebook
        self.init_embed_(x)

        embed_ind = self.quantize(x)  # [B*T,]
        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)  # [B*T, cb-size]
        embed_ind = self.postprocess_emb(embed_ind, shape)  # [B, T]
        quantize = self.dequantize(embed_ind)  # [B, T, D]

        if self.training:
            ### Update codebook by EMA
            embed_onehot_sum = embed_onehot.sum(0)  # [cb-size,]
            embed_sum = x.t() @ embed_onehot  # [D, cb-size]
            if is_distributed():
                dist.all_reduce(embed_onehot_sum)
                dist.all_reduce(embed_sum)
            # Update ema cluster count N_i^t, eq. (6) in vqvae paper
            self.cluster_size.data.mul_(self.decay).add_(embed_onehot_sum, alpha=1 - self.decay)
            # Update ema embed: eq. (7) in vqvae paper
            self.embed_avg.data.mul_(self.decay).add_(embed_sum.t(), alpha=1 - self.decay)
            # apply laplace smoothing
            n = self.cluster_size.sum()
            cluster_size = (self.cluster_size + self.epsilon) / (n + self.codebook_size * self.epsilon) * n
            # Update ema embed: eq. (8) in vqvae paper
            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
            self.embed.data.copy_(embed_normalized)

            # We do the expiry of code at that point as buffers are in sync
            # and all the workers will take the same decision.
            self.expire_codes_(x)

        return quantize, embed_ind


class VectorQuantization(nn.Module):
    """Vector quantization implementation.
    Currently supports only euclidean distance.
    Args:
        dim (int): Dimension
        codebook_size (int): Codebook size
        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
        decay (float): Decay for exponential moving average over the codebooks.
        epsilon (float): Epsilon value for numerical stability.
        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
        kmeans_iters (int): Number of iterations used for kmeans initialization.
        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
            that have an exponential moving average cluster size less than the specified threshold with
            randomly selected vector from the current batch.
        commitment_weight (float): Weight for commitment loss.
    """

    def __init__(
        self,
        dim: int,
        codebook_size: int,
        codebook_dim: tp.Optional[int] = None,
        decay: float = 0.99,
        epsilon: float = 1e-5,
        kmeans_init: bool = True,
        kmeans_iters: int = 50,
        threshold_ema_dead_code: int = 2,
        commitment_weight: float = 1.0,
    ):
        super().__init__()
        _codebook_dim: int = default(codebook_dim, dim)

        requires_projection = _codebook_dim != dim
        self.project_in = nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
        self.project_out = nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()

        self.epsilon = epsilon
        self.commitment_weight = commitment_weight

        self._codebook = EuclideanCodebook(
            dim=_codebook_dim,
            codebook_size=codebook_size,
            kmeans_init=kmeans_init,
            kmeans_iters=kmeans_iters,
            decay=decay,
            epsilon=epsilon,
            threshold_ema_dead_code=threshold_ema_dead_code,
        )
        self.codebook_size = codebook_size

    @property
    def codebook(self):
        return self._codebook.embed

    def encode(self, x):
        x = rearrange(x, "b d n -> b n d")
        x = self.project_in(x)
        embed_in = self._codebook.encode(x)
        return embed_in

    def decode(self, embed_ind):
        quantize = self._codebook.decode(embed_ind)
        quantize = self.project_out(quantize)
        quantize = rearrange(quantize, "b n d -> b d n")
        return quantize

    def forward(self, x):
        device = x.device
        x = x.transpose(1, 2).contiguous()  # [b d n] -> [b n d]
        x = self.project_in(x)

        quantize, embed_ind = self._codebook(x)

        if self.training:
            quantize = x + (quantize - x).detach()

        loss = torch.tensor([0.0], device=device, requires_grad=self.training)

        if self.training:
            if self.commitment_weight > 0:
                commit_loss = F.mse_loss(quantize.detach(), x)
                loss = loss + commit_loss * self.commitment_weight

        quantize = self.project_out(quantize)
        quantize = quantize.transpose(1, 2).contiguous()  # [b n d] -> [b d n]
        return quantize, embed_ind, loss


class ResidualVectorQuantization(nn.Module):
    """Residual vector quantization implementation.
    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
    """

    def __init__(self, *, num_quantizers, **kwargs):
        super().__init__()
        self.layers = nn.ModuleList([VectorQuantization(**kwargs) for _ in range(num_quantizers)])

    def forward(self, x, n_q: tp.Optional[int] = None):
        quantized_out = 0.0
        residual = x

        all_losses = []
        all_indices = []

        n_q = n_q or len(self.layers)

        for layer in self.layers[:n_q]:
            quantized, indices, loss = layer(residual)
            residual = residual - quantized
            quantized_out = quantized_out + quantized

            all_indices.append(indices)
            all_losses.append(loss)

        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
        return quantized_out, out_indices, out_losses

    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
        residual = x
        all_indices = []
        n_q = n_q or len(self.layers)
        for layer in self.layers[:n_q]:
            indices = layer.encode(residual)
            quantized = layer.decode(indices)
            residual = residual - quantized
            all_indices.append(indices)
        out_indices = torch.stack(all_indices)
        return out_indices

    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
        quantized_out = torch.tensor(0.0, device=q_indices.device)
        for i, indices in enumerate(q_indices):
            layer = self.layers[i]
            quantized = layer.decode(indices)
            quantized_out = quantized_out + quantized
        return quantized_out