Replace torch.jit.script with torch.compile (PyTorch 2.x migration)

cebra/models/criterions.py

@@ -33,14 +33,36 @@
 """
 
 import math
+import warnings
 from typing import Optional, Tuple
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
 
-@torch.jit.script
+def _compile(fn):
+    """Apply ``torch.compile`` when available, falling back to uncompiled.
+
+    ``torch.compile`` is the recommended replacement for ``torch.jit.script``
+    starting from PyTorch 2.0.  In environments where the compiler backend is
+    not available (e.g. certain CI configurations or incomplete installations),
+    the function is returned unchanged so that correctness is preserved.
+    A :class:`UserWarning` is emitted when the fallback path is taken.
+    """
+    try:
+        return torch.compile(fn)
+    except (ImportError, RuntimeError, TypeError) as exc:
+        warnings.warn(
+            f"torch.compile is unavailable; falling back to uncompiled "
+            f"{fn.__name__!r}.  Reason: {exc}",
+            UserWarning,
+            stacklevel=2,
+        )
+        return fn
+
+
+@_compile
 def dot_similarity(ref: torch.Tensor, pos: torch.Tensor,
                    neg: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     """Cosine similarity the ref, pos and negative pairs
@@ -59,7 +81,7 @@ def dot_similarity(ref: torch.Tensor, pos: torch.Tensor,
     return pos_dist, neg_dist
 
 
-@torch.jit.script
+@_compile
 def euclidean_similarity(
         ref: torch.Tensor, pos: torch.Tensor,
         neg: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -85,7 +107,7 @@ def euclidean_similarity(
     return pos_dist, neg_dist
 
 
-@torch.jit.script
+@_compile
 def infonce(
         pos_dist: torch.Tensor, neg_dist: torch.Tensor
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:

tests/test_criterions.py

@@ -25,16 +25,19 @@
 
 import cebra.models.criterions as cebra_criterions
 
+# Use the same _compile helper from criterions for consistency
+_compile = cebra_criterions._compile
 
-@torch.jit.script
+
+@_compile
 def ref_dot_similarity(ref: torch.Tensor, pos: torch.Tensor, neg: torch.Tensor,
                        temperature: float):
     pos_dist = torch.einsum("ni,ni->n", ref, pos) / temperature
     neg_dist = torch.einsum("ni,mi->nm", ref, neg) / temperature
     return pos_dist, neg_dist
 
 
-@torch.jit.script
+@_compile
 def ref_euclidean_similarity(ref: torch.Tensor, pos: torch.Tensor,
                              neg: torch.Tensor, temperature: float):
     ref_sq = torch.einsum("ni->n", ref**2) / temperature
@@ -48,7 +51,7 @@ def ref_euclidean_similarity(ref: torch.Tensor, pos: torch.Tensor,
     return pos_dist, neg_dist
 
 
-@torch.jit.script
+@_compile
 def ref_infonce(pos_dist: torch.Tensor, neg_dist: torch.Tensor):
     with torch.no_grad():
         c, _ = neg_dist.max(dim=1, keepdim=True)
@@ -61,7 +64,7 @@ def ref_infonce(pos_dist: torch.Tensor, neg_dist: torch.Tensor):
     return align + uniform, align, uniform
 
 
-@torch.jit.script
+@_compile
 def ref_infonce_not_stable(pos_dist: torch.Tensor, neg_dist: torch.Tensor):
     pos_dist = pos_dist
     neg_dist = neg_dist