[MNT] isolate pytorch-optimizer as soft dependency (#1641)

Isolates `pytorch-optimizer` as soft dependency in a new soft dep set `all_extras`. #1616

The imports happen only in `BaseModel`, when resolving aliases for `optimizer`.

Isolation consists of two steps:
* replacing resolution of the alias with a request to install the package, and removing from resolution scope if not installed
* replacing the default optimizer `"ranger"` with `"adam"` if `pytorch-optimizer` is not installed (left at `"ranger"` if installed)

Deprecation messages and actions are added, to changne the default to `"adam"` from 1.2.0, in order to minimize the number of dependencies in default parameter settings

pyproject.toml

@@ -61,7 +61,6 @@ dependencies = [
   "scipy >=1.8,<2.0",
   "pandas >=1.3.0,<3.0.0",
   "scikit-learn >=1.2,<2.0",
-  "pytorch-optimizer >=2.5.1,<4.0.0",
 ]
 
 [project.optional-dependencies]
@@ -86,6 +85,7 @@ all_extras = [
   "matplotlib",
   "optuna >=3.1.0,<4.0.0",
   "optuna-integration",
+  "pytorch_optimizer >=2.5.1,<4.0.0",
   "statsmodels",
 ]
 

pytorch_forecasting/models/base_model.py

@@ -18,8 +18,6 @@
 import numpy as np
 from numpy import iterable
 import pandas as pd
-import pytorch_optimizer
-from pytorch_optimizer import Ranger21
 import scipy.stats
 import torch
 import torch.nn as nn
@@ -54,7 +52,7 @@
     groupby_apply,
     to_list,
 )
-from pytorch_forecasting.utils._dependencies import _check_matplotlib
+from pytorch_forecasting.utils._dependencies import _check_matplotlib, _get_installed_packages
 
 # todo: compile models
 
@@ -411,7 +409,7 @@ def __init__(
         optimizer_params: Dict[str, Any] = None,
         monotone_constaints: Dict[str, int] = {},
         output_transformer: Callable = None,
-        optimizer="Ranger",
+        optimizer=None,
     ):
         """
         BaseModel for timeseries forecasting from which to inherit from
@@ -445,12 +443,44 @@ def __init__(
                 or ``pytorch_optimizer``.
                 Alternatively, a class or function can be passed which takes parameters as first argument and
                 a `lr` argument (optionally also `weight_decay`). Defaults to
-                `"ranger" <https://pytorch-optimizers.readthedocs.io/en/latest/optimizer_api.html#ranger21>`_.
+                `"ranger" <https://pytorch-optimizers.readthedocs.io/en/latest/optimizer_api.html#ranger21>`_,
+                if pytorch_optimizer is installed, otherwise "adam".
         """
         super().__init__()
         # update hparams
         frame = inspect.currentframe()
         init_args = get_init_args(frame)
+
+        # TODO 1.2.0: remove warnings and change default optimizer to "adam"
+        if init_args["optimizer"] is None:
+            ptopt_in_env = "pytorch_optimizer" in _get_installed_packages()
+            if ptopt_in_env:
+                init_args["optimizer"] = "ranger"
+                warnings.warn(
+                    "In pytorch-forecasting models, from version 1.2.0, "
+                    "the default optimizer will be 'adam', in order to "
+                    "minimize the number of dependencies in default parameter settings. "
+                    "Users who wish to ensure their code continues using 'ranger' as optimizer "
+                    "should ensure that pytorch_optimizer is installed, and set the optimizer "
+                    "parameter explicitly to 'ranger'.",
+                    stacklevel=2,
+                )
+            else:
+                init_args["optimizer"] = "adam"
+                warnings.warn(
+                    "In pytorch-forecasting models, on versions 1.1.X, "
+                    "the default optimizer defaults to 'adam', "
+                    "if pytorch_optimizer is not installed, "
+                    "otherwise it defaults to 'ranger' from pytorch_optimizer. "
+                    "From version 1.2.0, the default optimizer will be 'adam' "
+                    "regardless of whether pytorch_optimizer is installed, in order to "
+                    "minimize the number of dependencies in default parameter settings. "
+                    "Users who wish to ensure their code continues using 'ranger' as optimizer "
+                    "should ensure that pytorch_optimizer is installed, and set the optimizer "
+                    "parameter explicitly to 'ranger'.",
+                    stacklevel=2,
+                )
+
         self.save_hyperparameters(
             {name: val for name, val in init_args.items() if name not in self.hparams and name not in ["self"]}
         )
@@ -1150,6 +1180,7 @@ def configure_optimizers(self):
         Returns:
             Tuple[List]: first entry is list of optimizers and second is list of schedulers
         """
+        ptopt_in_env = "pytorch_optimizer" in _get_installed_packages()
         # either set a schedule of lrs or find it dynamically
         if self.hparams.optimizer_params is None:
             optimizer_params = {}
@@ -1177,6 +1208,13 @@ def configure_optimizers(self):
                 self.parameters(), lr=lr, weight_decay=self.hparams.weight_decay, **optimizer_params
             )
         elif self.hparams.optimizer == "ranger":
+            if not ptopt_in_env:
+                raise ImportError(
+                    "optimizer 'ranger' requires pytorch_optimizer in the evironment. "
+                    "Please install pytorch_optimizer with `pip install pytorch_optimizer`."
+                )
+            from pytorch_optimizer import Ranger21
+
             if any([isinstance(c, LearningRateFinder) for c in self.trainer.callbacks]):
                 # if finding learning rate, switch off warm up and cool down
                 optimizer_params.setdefault("num_warm_up_iterations", 0)
@@ -1203,15 +1241,20 @@ def configure_optimizers(self):
                 )
             except TypeError:  # in case there is no weight decay
                 optimizer = getattr(torch.optim, self.hparams.optimizer)(self.parameters(), lr=lr, **optimizer_params)
-        elif hasattr(pytorch_optimizer, self.hparams.optimizer):
-            try:
-                optimizer = getattr(pytorch_optimizer, self.hparams.optimizer)(
-                    self.parameters(), lr=lr, weight_decay=self.hparams.weight_decay, **optimizer_params
-                )
-            except TypeError:  # in case there is no weight decay
-                optimizer = getattr(pytorch_optimizer, self.hparams.optimizer)(
-                    self.parameters(), lr=lr, **optimizer_params
-                )
+        elif ptopt_in_env:
+            import pytorch_optimizer
+
+            if hasattr(pytorch_optimizer, self.hparams.optimizer):
+                try:
+                    optimizer = getattr(pytorch_optimizer, self.hparams.optimizer)(
+                        self.parameters(), lr=lr, weight_decay=self.hparams.weight_decay, **optimizer_params
+                    )
+                except TypeError:  # in case there is no weight decay
+                    optimizer = getattr(pytorch_optimizer, self.hparams.optimizer)(
+                        self.parameters(), lr=lr, **optimizer_params
+                    )
+            else:
+                raise ValueError(f"Optimizer of self.hparams.optimizer={self.hparams.optimizer} unknown")
         else:
             raise ValueError(f"Optimizer of self.hparams.optimizer={self.hparams.optimizer} unknown")