src/third_party/musicfm/modules/random_quantizer.py

# MIT License
#
# Copyright 2023 ByteDance Inc.
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# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”),
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# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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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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# IN THE SOFTWARE.

import torch
from torch import nn, einsum
from einops import rearrange


class RandomProjectionQuantizer(nn.Module):
    """
    Random projection and codebook lookup module

    Some code is borrowed from:
     https://github.com/lucidrains/vector-quantize-pytorch/blob/master/vector_quantize_pytorch/random_projection_quantizer.py
    But I did normalization using pre-computed global mean & variance instead of using layer norm.
    """

    def __init__(
        self,
        input_dim,
        codebook_dim,
        codebook_size,
        seed=142,
    ):
        super().__init__()

        # random seed
        torch.manual_seed(seed)

        # randomly initialized projection
        random_projection = torch.empty(input_dim, codebook_dim)
        nn.init.xavier_normal_(random_projection)
        self.register_buffer("random_projection", random_projection)

        # randomly initialized codebook
        codebook = torch.empty(codebook_size, codebook_dim)
        nn.init.normal_(codebook)
        self.register_buffer("codebook", codebook)

    def codebook_lookup(self, x):
        # reshape
        b = x.shape[0]
        x = rearrange(x, "b n e -> (b n) e")

        # L2 normalization
        normalized_x = nn.functional.normalize(x, dim=1, p=2)
        normalized_codebook = nn.functional.normalize(self.codebook, dim=1, p=2)

        # compute distances
        distances = torch.cdist(normalized_codebook, normalized_x)

        # get nearest
        nearest_indices = torch.argmin(distances, dim=0)

        # reshape
        xq = rearrange(nearest_indices, "(b n) -> b n", b=b)

        return xq

    @torch.no_grad()
    def forward(self, x):
        # always eval
        self.eval()

        # random projection [batch, length, input_dim] -> [batch, length, codebook_dim]
        x = einsum("b n d, d e -> b n e", x, self.random_projection)

        # codebook lookup
        xq = self.codebook_lookup(x)

        return xq