feat(hook): add nan_to_num hooks

docs/source/api/api.rst

@@ -186,12 +186,16 @@ Optimizer Hooks
 
     register_hook
     zero_nan_hook
+    nan_to_zero_hook
+    nan_to_zero
 
 Hook
 ~~~~
 
 .. autofunction:: register_hook
 .. autofunction:: zero_nan_hook
+.. autofunction:: nan_to_zero_hook
+.. autofunction:: nan_to_zero
 
 ------
 

torchopt/__init__.py

@@ -31,6 +31,7 @@
 from torchopt.alias import adam, adamw, rmsprop, sgd
 from torchopt.clip import clip_grad_norm
 from torchopt.combine import chain
+from torchopt.hook import nan_to_num, register_hook
 from torchopt.optim import SGD, Adam, AdamW, Optimizer, RMSProp, RMSprop, meta
 from torchopt.optim.func import FuncOptimizer
 from torchopt.optim.meta import (
@@ -60,6 +61,8 @@
     'rmsprop',
     'sgd',
     'clip_grad_norm',
+    'nan_to_num',
+    'register_hook',
     'chain',
     'Optimizer',
     'SGD',

torchopt/hook.py

@@ -14,18 +14,61 @@
 # ==============================================================================
 """Hook utilities."""
 
+from typing import Callable, Optional
+
 import torch
 
 from torchopt import pytree
 from torchopt.base import EmptyState, GradientTransformation
 
 
-__all__ = ['zero_nan_hook', 'register_hook']
+__all__ = ['zero_nan_hook', 'nan_to_num_hook', 'nan_to_num', 'register_hook']
 
 
 def zero_nan_hook(g: torch.Tensor) -> torch.Tensor:
-    """Registers a zero nan hook to replace nan with zero."""
-    return torch.where(torch.isnan(g), torch.zeros_like(g), g)
+    """A zero ``nan`` hook to replace ``nan`` with zero."""
+    return g.nan_to_num(nan=0.0)
+
+
+def nan_to_num_hook(
+    nan: float = 0.0, posinf: Optional[float] = None, neginf: Optional[float] = None
+) -> Callable[[torch.Tensor], torch.Tensor]:
+    """Returns a ``nan`` to num hook to replace ``nan`` with given number."""
+
+    def hook(g: torch.Tensor) -> torch.Tensor:
+        """A zero ``nan`` hook to replace ``nan`` with given number."""
+        return g.nan_to_num(nan=nan, posinf=posinf, neginf=neginf)
+
+    return hook
+
+
+def nan_to_num(
+    nan: float = 0.0, posinf: Optional[float] = None, neginf: Optional[float] = None
+) -> GradientTransformation:
+    """A gradient transformation that replaces gradient values of ``nan`` with given number.
+
+    Returns:
+        An ``(init_fn, update_fn)`` tuple.
+    """
+
+    def init_fn(params):  # pylint: disable=unused-argument
+        return EmptyState()
+
+    def update_fn(updates, state, *, params=None, inplace=True):  # pylint: disable=unused-argument
+        if inplace:
+
+            def f(g):
+                return g.nan_to_num_(nan=nan, posinf=posinf, neginf=neginf)
+
+        else:
+
+            def f(g):
+                return g.nan_to_num(nan=nan, posinf=posinf, neginf=neginf)
+
+        new_updates = pytree.tree_map(f, updates)
+        return new_updates, state
+
+    return GradientTransformation(init_fn, update_fn)
 
 
 def register_hook(hook) -> GradientTransformation: