Revert "option to modify configure_optimizers"

This reverts commit 7d83799.
Nixtla · Jan 28, 2025 · 5765c20 · 5765c20
1 parent 0266daf
commit 5765c20
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diff --git a/nbs/common.base_model.ipynb b/nbs/common.base_model.ipynb
@@ -162,9 +162,6 @@
     "        self.lr_scheduler = lr_scheduler\n",
     "        self.lr_scheduler_kwargs = lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {}\n",
     "\n",
-    "        # customized by set_configure_optimizers()\n",
-    "        self.config_optimizers = None\n",
-    "\n",
     "        # Variables\n",
     "        self.futr_exog_list = list(futr_exog_list) if futr_exog_list is not None else []\n",
     "        self.hist_exog_list = list(hist_exog_list) if hist_exog_list is not None else []\n",
@@ -401,9 +398,6 @@
     "        random.seed(self.random_seed)\n",
     "\n",
     "    def configure_optimizers(self):\n",
-    "        if self.config_optimizers is not None:\n",
-    "            return self.config_optimizers\n",
-    "                \n",
     "        if self.optimizer:\n",
     "            optimizer_signature = inspect.signature(self.optimizer)\n",
     "            optimizer_kwargs = deepcopy(self.optimizer_kwargs)\n",
@@ -438,47 +432,6 @@
     "            )\n",
     "        return {'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n",
     "\n",
-    "    def set_configure_optimizers(\n",
-    "            self, \n",
-    "            optimizer=None,\n",
-    "            scheduler=None,\n",
-    "            interval='step',\n",
-    "            frequency=1,\n",
-    "            monitor='val_loss',\n",
-    "            strict=True,\n",
-    "            name=None\n",
-    "        ):\n",
-    "        \"\"\"Helper function to customize the lr_scheduler_config as detailed in \n",
-    "        https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers\n",
-    "\n",
-    "        Calling set_configure_optimizers() with valid `optimizer`, `scheduler` shall modify the returned \n",
-    "        dictionary of key='optimizer', key='lr_scheduler' in configure_optimizers().\n",
-    "        Note that the default choice of `interval` in set_configure_optiizers() is 'step',\n",
-    "        which differs from the choice of 'epoch' used in lightning_module. \n",
-    "        \"\"\"\n",
-    "        lr_scheduler_config = {\n",
-    "            'interval': interval,\n",
-    "            'frequency': frequency,\n",
-    "            'monitor': monitor,\n",
-    "            'strict': strict,\n",
-    "            'name': name,\n",
-    "        }\n",
-    "\n",
-    "        if scheduler is not None and optimizer is not None:\n",
-    "            if not isinstance(scheduler, torch.optim.lr_scheduler.LRScheduler):\n",
-    "                raise TypeError(\"scheduler is not a valid instance of torch.optim.lr_scheduler.LRScheduler\")\n",
-    "            if not isinstance(optimizer, torch.optim.Optimizer):\n",
-    "                raise TypeError(\"optimizer is not a valid instance of torch.optim.Optimizer\")                \n",
-    "            \n",
-    "            lr_scheduler_config[\"scheduler\"] = scheduler\n",
-    "            self.config_optimizers = {\n",
-    "                'optimizer': optimizer,\n",
-    "                'lr_scheduler': lr_scheduler_config,\n",
-    "            }\n",
-    "        else:\n",
-    "            # falls back to default option as specified in configure_optimizers()\n",
-    "            self.config_optimizers = None\n",
-    "\n",
     "    def get_test_size(self):\n",
     "        return self.test_size\n",
     "\n",

diff --git a/neuralforecast/common/_base_model.py b/neuralforecast/common/_base_model.py
@@ -121,9 +121,6 @@ def __init__(
             lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {}
         )
 
-        # customized by set_configure_optimizers()
-        self.config_optimizers = None
-
         # Variables
         self.futr_exog_list = list(futr_exog_list) if futr_exog_list is not None else []
         self.hist_exog_list = list(hist_exog_list) if hist_exog_list is not None else []
@@ -378,9 +375,6 @@ def on_fit_start(self):
         random.seed(self.random_seed)
 
     def configure_optimizers(self):
-        if self.config_optimizers is not None:
-            return self.config_optimizers
-
         if self.optimizer:
             optimizer_signature = inspect.signature(self.optimizer)
             optimizer_kwargs = deepcopy(self.optimizer_kwargs)
@@ -421,51 +415,6 @@ def configure_optimizers(self):
             )
         return {"optimizer": optimizer, "lr_scheduler": lr_scheduler}
 
-    def set_configure_optimizers(
-        self,
-        optimizer=None,
-        scheduler=None,
-        interval="step",
-        frequency=1,
-        monitor="val_loss",
-        strict=True,
-        name=None,
-    ):
-        """Helper function to customize the lr_scheduler_config as detailed in
-        https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers
-
-        Calling set_configure_optimizers() with valid `optimizer`, `scheduler` shall modify the returned
-        dictionary of key='optimizer', key='lr_scheduler' in configure_optimizers().
-        Note that the default choice of `interval` in set_configure_optiizers() is 'step',
-        which differs from the choice of 'epoch' used in lightning_module.
-        """
-        lr_scheduler_config = {
-            "interval": interval,
-            "frequency": frequency,
-            "monitor": monitor,
-            "strict": strict,
-            "name": name,
-        }
-
-        if scheduler is not None and optimizer is not None:
-            if not isinstance(scheduler, torch.optim.lr_scheduler.LRScheduler):
-                raise TypeError(
-                    "scheduler is not a valid instance of torch.optim.lr_scheduler.LRScheduler"
-                )
-            if not isinstance(optimizer, torch.optim.Optimizer):
-                raise TypeError(
-                    "optimizer is not a valid instance of torch.optim.Optimizer"
-                )
-
-            lr_scheduler_config["scheduler"] = scheduler
-            self.config_optimizers = {
-                "optimizer": optimizer,
-                "lr_scheduler": lr_scheduler_config,
-            }
-        else:
-            # falls back to default option as specified in configure_optimizers()
-            self.config_optimizers = None
-
     def get_test_size(self):
         return self.test_size