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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 math | |
| import typing as tp | |
| import torch | |
| from .base import BaseQuantizer, QuantizedResult | |
| from .core_vq import ResidualVectorQuantization | |
| class ResidualVectorQuantizer(BaseQuantizer): | |
| """Residual Vector Quantizer. | |
| Args: | |
| dimension (int): Dimension of the codebooks. | |
| n_q (int): Number of residual vector quantizers used. | |
| q_dropout (bool): Random quantizer drop out at train time. | |
| bins (int): Codebook size. | |
| decay (float): Decay for exponential moving average over the codebooks. | |
| 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. | |
| orthogonal_reg_weight (float): Orthogonal regularization weights. | |
| orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes. | |
| orthogonal_reg_max_codes (optional int): Maximum number of codes to consider. | |
| for orthogonal regulariation. | |
| """ | |
| def __init__( | |
| self, | |
| dimension: int = 256, | |
| n_q: int = 8, | |
| q_dropout: bool = False, | |
| bins: int = 1024, | |
| decay: float = 0.99, | |
| kmeans_init: bool = True, | |
| kmeans_iters: int = 10, | |
| threshold_ema_dead_code: int = 2, | |
| orthogonal_reg_weight: float = 0.0, | |
| orthogonal_reg_active_codes_only: bool = False, | |
| orthogonal_reg_max_codes: tp.Optional[int] = None, | |
| ): | |
| super().__init__() | |
| self.max_n_q = n_q | |
| self.n_q = n_q | |
| self.q_dropout = q_dropout | |
| self.dimension = dimension | |
| self.bins = bins | |
| self.decay = decay | |
| self.kmeans_init = kmeans_init | |
| self.kmeans_iters = kmeans_iters | |
| self.threshold_ema_dead_code = threshold_ema_dead_code | |
| self.orthogonal_reg_weight = orthogonal_reg_weight | |
| self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only | |
| self.orthogonal_reg_max_codes = orthogonal_reg_max_codes | |
| self.vq = ResidualVectorQuantization( | |
| dim=self.dimension, | |
| codebook_size=self.bins, | |
| num_quantizers=self.n_q, | |
| decay=self.decay, | |
| kmeans_init=self.kmeans_init, | |
| kmeans_iters=self.kmeans_iters, | |
| threshold_ema_dead_code=self.threshold_ema_dead_code, | |
| orthogonal_reg_weight=self.orthogonal_reg_weight, | |
| orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only, | |
| orthogonal_reg_max_codes=self.orthogonal_reg_max_codes, | |
| channels_last=False | |
| ) | |
| def forward(self, x: torch.Tensor, frame_rate: int): | |
| n_q = self.n_q | |
| if self.training and self.q_dropout: | |
| n_q = int(torch.randint(1, self.n_q + 1, (1,)).item()) | |
| bw_per_q = math.log2(self.bins) * frame_rate / 1000 | |
| quantized, codes, commit_loss = self.vq(x, n_q=n_q) | |
| codes = codes.transpose(0, 1) | |
| # codes is [B, K, T], with T frames, K nb of codebooks. | |
| bw = torch.tensor(n_q * bw_per_q).to(x) | |
| return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss)) | |
| def encode(self, x: torch.Tensor) -> torch.Tensor: | |
| """Encode a given input tensor with the specified frame rate at the given bandwidth. | |
| The RVQ encode method sets the appropriate number of quantizer to use | |
| and returns indices for each quantizer. | |
| """ | |
| n_q = self.n_q | |
| codes = self.vq.encode(x, n_q=n_q) | |
| codes = codes.transpose(0, 1) | |
| # codes is [B, K, T], with T frames, K nb of codebooks. | |
| return codes | |
| def decode(self, codes: torch.Tensor) -> torch.Tensor: | |
| """Decode the given codes to the quantized representation. | |
| """ | |
| # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T]. | |
| codes = codes.transpose(0, 1) | |
| quantized = self.vq.decode(codes) | |
| return quantized | |
| def total_codebooks(self): | |
| return self.max_n_q | |
| def num_codebooks(self): | |
| return self.n_q | |
| def set_num_codebooks(self, n: int): | |
| assert n > 0 and n <= self.max_n_q | |
| self.n_q = n | |