Add monkey patched fairseq package to run on python 3.11 (what is needed for our use of RVC at least)

modules/voice_conversion/fairseq/models/model_utils.py

@@ -0,0 +1,92 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# 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 List, Optional
+
+import torch
+from torch import Tensor
+
+
+@torch.jit.script
+def script_skip_tensor_list(x: List[Tensor], mask):
+    res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
+    outputs = []
+    for i, t in enumerate(res):
+        if t.numel() != 0:
+            outputs.append(t)
+        else:
+            outputs.append(x[i])
+    return outputs
+
+
+@torch.jit.script
+def script_skip_tensor(x: Tensor, mask):
+    # None case
+    if x.size(0) == 0:
+        return x
+    res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
+    if res.numel() == 0:
+        return x
+    else:
+        return res
+
+
+@torch.jit.script
+def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
+    """
+    Expand 2D/3D tensor on dim=1
+    """
+    if x is None:
+        return None
+
+    assert x.dim() == 2 or x.dim() == 3
+    assert trg_dim >= x.size(1), (trg_dim, x.size())
+    if trg_dim == x.size(1):
+        return x
+
+    dims = [x.size(0), trg_dim - x.size(1)]
+    if x.dim() == 3:
+        dims.append(x.size(2))
+    x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
+
+    return x
+
+
+@torch.jit.script
+def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
+    return x if x is not None else y
+
+
+@torch.jit.script
+def fill_tensors(
+    x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int
+) -> Optional[Tensor]:
+    """
+    Filling tensor x with y at masked positions (dim=0).
+    """
+    if x is None or x.size()[0] == 0 or y is None:
+        return x
+    assert x.dim() == y.dim() and mask.size(0) == x.size(0)
+    assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
+
+    n_selected = mask.sum()
+    if n_selected == 0:
+        return x
+    assert n_selected == y.size(0)
+    if n_selected == x.size(0):
+        return y
+
+    if x.size(1) < y.size(1):
+        x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
+        x[mask] = y
+    elif x.size(1) > y.size(1):
+        x[mask] = torch.tensor(padding_idx).type_as(x)
+        if x.dim() == 2:
+            x[mask, : y.size(1)] = y
+        else:
+            x[mask, : y.size(1), :] = y
+    else:
+        x[mask] = y
+    return x