Merge branch 'main' into CML-log-uniform-sampler

i6_models/parts/best_rq/__init__.py

@@ -0,0 +1,2 @@
+from .mask import *
+from .quantizer import *

i6_models/parts/best_rq/mask.py

@@ -0,0 +1,74 @@
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+__all__ = ["RandomMask"]
+
+
+class RandomMask(nn.Module):
+    """
+    randomly mask out consecutive frames time dimension, the masked frames can be either
+    replaced with zeros or with learnable embeddings.
+    simplified version from Fairseq compute_mask_indices function,
+    C.f. https://github.com/facebookresearch/fairseq/blob/ecbf110e1eb43861214b05fa001eff584954f65a/fairseq/data/data_utils.py#L399
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        mask_replace_val: str,
+        mask_percentage: float,
+        mask_length: int,
+    ):
+        """
+        :param input_dim: number of feature dimension of input
+        :param mask_replace_val: the way to replace masked frames, either with zeros or lernable embeddings
+        :param mask_percentage: percentage of frames to be masked out
+        :param mask_length: the length of each mask span
+        """
+        super().__init__()
+
+        assert mask_replace_val in ["lernable", "zero"], "not implemented yet"
+        if mask_replace_val == "lernable":
+            self.mask_emb = nn.Parameter(torch.FloatTensor(input_dim).uniform_())
+        elif mask_replace_val == "zero":
+            self.mask_emb = torch.zeros(input_dim)
+        self.mask_percentage = mask_percentage
+        self.mask_length = mask_length
+
+    def forward(
+        self,
+        tensor: torch.tensor,
+        padding_mask: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        ndim_batch, ndim_time, _ = tensor.size()
+
+        mask = torch.zeros((ndim_batch, ndim_time), dtype=torch.bool)
+
+        mask_idcs = []
+        for i in range(ndim_batch):
+            if padding_mask is not None:
+                seq_len = ndim_time - padding_mask[i].long().sum().item()
+                assert seq_len >= 0
+            else:
+                seq_len = ndim_time
+
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                self.mask_percentage * seq_len / float(self.mask_length)
+                + np.random.rand()
+            )
+
+            min_len = self.mask_length
+            if seq_len - min_len <= num_mask:
+                min_len = seq_len - num_mask - 1
+            mask_idc = np.random.choice(seq_len - min_len, num_mask, replace=False)
+
+            for j in mask_idc:
+                mask[i, j : j + self.mask_length] = True
+
+        tensor[mask] = self.mask_emb.to(tensor.device)
+
+        return tensor, torch.tensor(mask).to(tensor.device)

i6_models/parts/best_rq/quantizer.py

@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.linalg import vector_norm
+
+__all__ = [
+    "RandomProjectionQuantizer",
+]
+
+
+class RandomProjectionQuantizer(nn.Module):
+    """
+    implement the fixed random projection quantizer from BestRQ
+    C.f. https://arxiv.org/pdf/2202.01855 for theoretic background
+    code adapted from https://github.com/speechbrain/speechbrain/blob/16b6420d4ff23210cfca2e888be8853264e0cb17/speechbrain/nnet/quantisers.py#L127
+    """
+
+    def __init__(self, input_dim, codebook_dim, codebook_num_vars):
+        """
+        :param input_dim: number of feature dimension of input
+        :param codebook_dim: number of dimension for vocab in the codebook
+        :param codebook_num_vars: vocab size of the codebook
+        """
+        super().__init__()
+
+        self.input_dim = input_dim
+
+        # projection matrix use Xavier initialization
+        P_init = torch.empty((input_dim, codebook_dim))
+        self.register_buffer("P", nn.init.xavier_uniform_(P_init))
+
+        # normalize random matrix for codebook
+        self.register_buffer("CB", F.normalize(torch.randn(codebook_num_vars, codebook_dim)))
+
+    def forward(self, x: torch.tensor) -> torch.tensor:
+        x = F.normalize(x @ self.P)
+        return vector_norm((self.CB.unsqueeze(1) - x.unsqueeze(1)), dim=-1).argmin(dim=1)