Try to fix documentation build, add better docstrings to public optimizer api

hfdocs/source/reference/optimizers.mdx

@@ -6,22 +6,28 @@ This page contains the API reference documentation for learning rate optimizers
 
 ### Factory functions
 
-[[autodoc]] timm.optim.optim_factory.create_optimizer
-[[autodoc]] timm.optim.optim_factory.create_optimizer_v2
+[[autodoc]] timm.optim.create_optimizer_v2
+[[autodoc]] timm.optim.list_optimizers
+[[autodoc]] timm.optim.get_optimizer_class
 
 ### Optimizer Classes
 
 [[autodoc]] timm.optim.adabelief.AdaBelief
 [[autodoc]] timm.optim.adafactor.Adafactor
+[[autodoc]] timm.optim.adafactor_bv.AdafactorBigVision
 [[autodoc]] timm.optim.adahessian.Adahessian
 [[autodoc]] timm.optim.adamp.AdamP
 [[autodoc]] timm.optim.adamw.AdamW
+[[autodoc]] timm.optim.adopt.Adopt
 [[autodoc]] timm.optim.lamb.Lamb
 [[autodoc]] timm.optim.lars.Lars
+[[autodoc]] timm.optim.lion,Lion
 [[autodoc]] timm.optim.lookahead.Lookahead
 [[autodoc]] timm.optim.madgrad.MADGRAD
 [[autodoc]] timm.optim.nadam.Nadam
+[[autodoc]] timm.optim.nadamw.NadamW
 [[autodoc]] timm.optim.nvnovograd.NvNovoGrad
 [[autodoc]] timm.optim.radam.RAdam
 [[autodoc]] timm.optim.rmsprop_tf.RMSpropTF
 [[autodoc]] timm.optim.sgdp.SGDP
+[[autodoc]] timm.optim.sgdw.SGDW

timm/optim/_optim_factory.py

@@ -124,7 +124,7 @@ class OptimizerRegistry:
 
     def list_optimizers(
             self,
-            filter: str = '',
+            filter: Union[str, List[str]] = '',
             exclude_filters: Optional[List[str]] = None,
             with_description: bool = False
     ) -> List[Union[str, Tuple[str, str]]]:
@@ -141,7 +141,14 @@ class OptimizerRegistry:
         names = sorted(self._optimizers.keys())
 
         if filter:
-            names = [n for n in names if fnmatch(n, filter)]
+            if isinstance(filter, str):
+                filters = [filter]
+            else:
+                filters = filter
+            filtered_names = set()
+            for f in filters:
+                filtered_names.update(n for n in names if fnmatch(n, f))
+            names = sorted(filtered_names)
 
         if exclude_filters:
             for exclude_filter in exclude_filters:
@@ -149,6 +156,7 @@ class OptimizerRegistry:
 
         if with_description:
             return [(name, self._optimizers[name].description) for name in names]
+
         return names
 
     def get_optimizer_info(self, name: str) -> OptimInfo:
@@ -718,11 +726,46 @@ _register_default_optimizers()
 # Public API
 
 def list_optimizers(
-        filter: str = '',
+        filter: Union[str, List[str]] = '',
         exclude_filters: Optional[List[str]] = None,
         with_description: bool = False,
 ) -> List[Union[str, Tuple[str, str]]]:
     """List available optimizer names, optionally filtered.
+
+    List all registered optimizers, with optional filtering using wildcard patterns.
+    Optimizers can be filtered using include and exclude patterns, and can optionally
+    return descriptions with each optimizer name.
+
+    Args:
+        filter: Wildcard style filter string or list of filter strings
+            (e.g., 'adam*' for all Adam variants, or ['adam*', '*8bit'] for
+            Adam variants and 8-bit optimizers). Empty string means no filtering.
+        exclude_filters: Optional list of wildcard patterns to exclude. For example,
+            ['*8bit', 'fused*'] would exclude 8-bit and fused implementations.
+        with_description: If True, returns tuples of (name, description) instead of
+            just names. Descriptions provide brief explanations of optimizer characteristics.
+
+    Returns:
+        If with_description is False:
+            List of optimizer names as strings (e.g., ['adam', 'adamw', ...])
+        If with_description is True:
+            List of tuples of (name, description) (e.g., [('adam', 'Adaptive Moment...'), ...])
+
+    Examples:
+        >>> list_optimizers()
+        ['adam', 'adamw', 'sgd', ...]
+
+        >>> list_optimizers(['la*', 'nla*'])  # List lamb & lars
+        ['lamb', 'lambc', 'larc', 'lars', 'nlarc', 'nlars']
+
+        >>> list_optimizers('*adam*', exclude_filters=['bnb*', 'fused*'])  # Exclude bnb & apex adam optimizers
+        ['adam', 'adamax', 'adamp', 'adamw', 'nadam', 'nadamw', 'radam']
+
+        >>> list_optimizers(with_description=True)  # Get descriptions
+        [('adabelief', 'Adapts learning rate based on gradient prediction error'),
+         ('adadelta', 'torch.optim Adadelta, Adapts learning rates based on running windows of gradients'),
+         ('adafactor', 'Memory-efficient implementation of Adam with factored gradients'),
+        ...]
     """
     return default_registry.list_optimizers(filter, exclude_filters, with_description)
 
@@ -731,7 +774,38 @@ def get_optimizer_class(
         name: str,
         bind_defaults: bool = False,
 ) -> Union[Type[optim.Optimizer], OptimizerCallable]:
-    """Get optimizer class by name with any defaults applied.
+    """Get optimizer class by name with option to bind default arguments.
+
+    Retrieves the optimizer class or a partial function with default arguments bound.
+    This allows direct instantiation of optimizers with their default configurations
+    without going through the full factory.
+
+    Args:
+        name: Name of the optimizer to retrieve (e.g., 'adam', 'sgd')
+        bind_defaults: If True, returns a partial function with default arguments from OptimInfo bound.
+            If False, returns the raw optimizer class.
+
+    Returns:
+        If bind_defaults is False:
+            The optimizer class (e.g., torch.optim.Adam)
+        If bind_defaults is True:
+            A partial function with default arguments bound
+
+    Raises:
+        ValueError: If optimizer name is not found in registry
+
+    Examples:
+        >>> # Get raw optimizer class
+        >>> Adam = get_optimizer_class('adam')
+        >>> opt = Adam(model.parameters(), lr=1e-3)
+
+        >>> # Get optimizer with defaults bound
+        >>> AdamWithDefaults = get_optimizer_class('adam', bind_defaults=True)
+        >>> opt = AdamWithDefaults(model.parameters(), lr=1e-3)
+
+        >>> # Get SGD with nesterov momentum default
+        >>> SGD = get_optimizer_class('sgd', bind_defaults=True)  # nesterov=True bound
+        >>> opt = SGD(model.parameters(), lr=0.1, momentum=0.9)
     """
     return default_registry.get_optimizer_class(name, bind_defaults=bind_defaults)
 
@@ -748,7 +822,69 @@ def create_optimizer_v2(
         param_group_fn: Optional[Callable[[nn.Module], Params]] = None,
         **kwargs: Any,
 ) -> optim.Optimizer:
-    """Create an optimizer instance using the default registry."""
+    """Create an optimizer instance via timm registry.
+
+    Creates and configures an optimizer with appropriate parameter groups and settings.
+    Supports automatic parameter group creation for weight decay and layer-wise learning
+    rates, as well as custom parameter grouping.
+
+    Args:
+        model_or_params: A PyTorch model or an iterable of parameters/parameter groups.
+            If a model is provided, parameters will be automatically extracted and grouped
+            based on the other arguments.
+        opt: Name of the optimizer to create (e.g., 'adam', 'adamw', 'sgd').
+            Use list_optimizers() to see available options.
+        lr: Learning rate. If None, will use the optimizer's default.
+        weight_decay: Weight decay factor. Will be used to create param groups if model_or_params is a model.
+        momentum: Momentum factor for optimizers that support it. Only used if the
+            chosen optimizer accepts a momentum parameter.
+        foreach: Enable/disable foreach (multi-tensor) implementation if available.
+            If None, will use optimizer-specific defaults.
+        filter_bias_and_bn: If True, bias, norm layer parameters (all 1d params) will not have
+            weight decay applied. Only used when model_or_params is a model and
+            weight_decay > 0.
+        layer_decay: Optional layer-wise learning rate decay factor. If provided,
+            learning rates will be scaled by layer_decay^(max_depth - layer_depth).
+            Only used when model_or_params is a model.
+        param_group_fn: Optional function to create custom parameter groups.
+            If provided, other parameter grouping options will be ignored.
+        **kwargs: Additional optimizer-specific arguments (e.g., betas for Adam).
+
+    Returns:
+        Configured optimizer instance.
+
+    Examples:
+        >>> # Basic usage with a model
+        >>> optimizer = create_optimizer_v2(model, 'adamw', lr=1e-3)
+
+        >>> # SGD with momentum and weight decay
+        >>> optimizer = create_optimizer_v2(
+        ...     model, 'sgd', lr=0.1, momentum=0.9, weight_decay=1e-4
+        ... )
+
+        >>> # Adam with layer-wise learning rate decay
+        >>> optimizer = create_optimizer_v2(
+        ...     model, 'adam', lr=1e-3, layer_decay=0.7
+        ... )
+
+        >>> # Custom parameter groups
+        >>> def group_fn(model):
+        ...     return [
+        ...         {'params': model.backbone.parameters(), 'lr': 1e-4},
+        ...         {'params': model.head.parameters(), 'lr': 1e-3}
+        ...     ]
+        >>> optimizer = create_optimizer_v2(
+        ...     model, 'sgd', param_group_fn=group_fn
+        ... )
+
+    Note:
+        Parameter group handling precedence:
+        1. If param_group_fn is provided, it will be used exclusively
+        2. If layer_decay is provided, layer-wise groups will be created
+        3. If weight_decay > 0 and filter_bias_and_bn is True, weight decay groups will be created
+        4. Otherwise, all parameters will be in a single group
+    """
+
     return default_registry.create_optimizer(
         model_or_params,
         opt=opt,