refactor: rename canto-backend → backend, canto-frontend → frontend

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/indextts/utils/maskgct/models/codec/ns3_codec/quantize/__init__.py

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+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .fvq import *
+from .rvq import *

backend/indextts/utils/maskgct/models/codec/ns3_codec/quantize/fvq.py

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+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+
+class FactorizedVectorQuantize(nn.Module):
+    def __init__(self, dim, codebook_size, codebook_dim, commitment, **kwargs):
+        super().__init__()
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+        self.commitment = commitment
+
+        if dim != self.codebook_dim:
+            self.in_proj = weight_norm(nn.Linear(dim, self.codebook_dim))
+            self.out_proj = weight_norm(nn.Linear(self.codebook_dim, dim))
+        else:
+            self.in_proj = nn.Identity()
+            self.out_proj = nn.Identity()
+        self._codebook = nn.Embedding(codebook_size, self.codebook_dim)
+
+    @property
+    def codebook(self):
+        return self._codebook
+
+    def forward(self, z):
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+        # transpose since we use linear
+
+        z = rearrange(z, "b d t -> b t d")
+
+        # Factorized codes project input into low-dimensional space
+        z_e = self.in_proj(z)  # z_e : (B x T x D)
+        z_e = rearrange(z_e, "b t d -> b d t")
+        z_q, indices = self.decode_latents(z_e)
+
+        if self.training:
+            commitment_loss = (
+                F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
+                * self.commitment
+            )
+            codebook_loss = F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
+            commit_loss = commitment_loss + codebook_loss
+        else:
+            commit_loss = torch.zeros(z.shape[0], device=z.device)
+
+        z_q = (
+            z_e + (z_q - z_e).detach()
+        )  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        z_q = rearrange(z_q, "b d t -> b t d")
+        z_q = self.out_proj(z_q)
+        z_q = rearrange(z_q, "b t d -> b d t")
+
+        return z_q, indices, commit_loss
+
+    def vq2emb(self, vq, proj=True):
+        emb = self.embed_code(vq)
+        if proj:
+            emb = self.out_proj(emb)
+        return emb.transpose(1, 2)
+
+    def get_emb(self):
+        return self.codebook.weight
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight  # codebook: (N x D)
+        # L2 normalize encodings and codebook
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance with codebook
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+        return z_q, indices

backend/indextts/utils/maskgct/models/codec/ns3_codec/quantize/rvq.py

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+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import torch
+from torch import nn
+from .fvq import FactorizedVectorQuantize
+
+
+class ResidualVQ(nn.Module):
+    """Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf"""
+
+    def __init__(self, *, num_quantizers, codebook_size, **kwargs):
+        super().__init__()
+        VQ = FactorizedVectorQuantize
+        if type(codebook_size) == int:
+            codebook_size = [codebook_size] * num_quantizers
+        self.layers = nn.ModuleList(
+            [VQ(codebook_size=2**size, **kwargs) for size in codebook_size]
+        )
+        self.num_quantizers = num_quantizers
+        self.quantizer_dropout = kwargs.get("quantizer_dropout", 0.0)
+        self.dropout_type = kwargs.get("dropout_type", None)
+
+    def forward(self, x, n_quantizers=None):
+        quantized_out = 0.0
+        residual = x
+
+        all_losses = []
+        all_indices = []
+        all_quantized = []
+
+        if n_quantizers is None:
+            n_quantizers = self.num_quantizers
+        if self.training:
+            n_quantizers = torch.ones((x.shape[0],)) * self.num_quantizers + 1
+            if self.dropout_type == "linear":
+                dropout = torch.randint(1, self.num_quantizers + 1, (x.shape[0],))
+            elif self.dropout_type == "exp":
+                dropout = torch.randint(
+                    1, int(math.log2(self.num_quantizers)), (x.shape[0],)
+                )
+                dropout = torch.pow(2, dropout)
+            n_dropout = int(x.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(x.device)
+
+        for idx, layer in enumerate(self.layers):
+            if not self.training and idx >= n_quantizers:
+                break
+            quantized, indices, loss = layer(residual)
+
+            mask = (
+                torch.full((x.shape[0],), fill_value=idx, device=x.device)
+                < n_quantizers
+            )
+
+            residual = residual - quantized
+
+            quantized_out = quantized_out + quantized * mask[:, None, None]
+
+            # loss
+            loss = (loss * mask).mean()
+
+            all_indices.append(indices)
+            all_losses.append(loss)
+            all_quantized.append(quantized)
+        all_losses, all_indices, all_quantized = map(
+            torch.stack, (all_losses, all_indices, all_quantized)
+        )
+        return quantized_out, all_indices, all_losses, all_quantized
+
+    def vq2emb(self, vq):
+        # vq: [n_quantizers, B, T]
+        quantized_out = 0.0
+        for idx, layer in enumerate(self.layers):
+            quantized = layer.vq2emb(vq[idx])
+            quantized_out += quantized
+        return quantized_out
+
+    def get_emb(self):
+        embs = []
+        for idx, layer in enumerate(self.layers):
+            embs.append(layer.get_emb())
+        return embs