feat: Deprecate activation functions for GRU

nbs/models.gru.ipynb

@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%set_env PYTORCH_ENABLE_MPS_FALLBACK=1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -70,6 +79,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "import warnings\n",
     "from typing import Optional\n",
     "\n",
     "import torch\n",
@@ -91,7 +101,7 @@
     "    \"\"\" GRU\n",
     "\n",
     "    Multi Layer Recurrent Network with Gated Units (GRU), and\n",
-    "    MLP decoder. The network has `tanh` or `relu` non-linearities, it is trained \n",
+    "    MLP decoder. The network has non-linear activation functions, it is trained \n",
     "    using ADAM stochastic gradient descent. The network accepts static, historic \n",
     "    and future exogenous data, flattens the inputs.\n",
     "\n",
@@ -101,7 +111,7 @@
     "    `inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.<br>\n",
     "    `encoder_n_layers`: int=2, number of layers for the GRU.<br>\n",
     "    `encoder_hidden_size`: int=200, units for the GRU's hidden state size.<br>\n",
-    "    `encoder_activation`: str=`tanh`, type of GRU activation from `tanh` or `relu`.<br>\n",
+    "    `encoder_activation`: Optional[str]=None, Deprecated. Activation function in GRU is frozen in PyTorch.<br>\n",
     "    `encoder_bias`: bool=True, whether or not to use biases b_ih, b_hh within GRU units.<br>\n",
     "    `encoder_dropout`: float=0., dropout regularization applied to GRU outputs.<br>\n",
     "    `context_size`: int=10, size of context vector for each timestamp on the forecasting window.<br>\n",
@@ -143,7 +153,7 @@
     "                 inference_input_size: int = -1,\n",
     "                 encoder_n_layers: int = 2,\n",
     "                 encoder_hidden_size: int = 200,\n",
-    "                 encoder_activation: str = 'tanh',\n",
+    "                 encoder_activation: Optional[str] = None,\n",
     "                 encoder_bias: bool = True,\n",
     "                 encoder_dropout: float = 0.,\n",
     "                 context_size: int = 10,\n",
@@ -199,6 +209,14 @@
     "            **trainer_kwargs\n",
     "        )\n",
     "\n",
+    "        if encoder_activation is not None:\n",
+    "            warnings.warn(\n",
+    "                \"The 'encoder_activation' argument is deprecated and will be removed in \"\n",
+    "                \"future versions. The activation function in GRU is frozen in PyTorch and \"\n",
+    "                \"it cannot be modified.\",\n",
+    "                DeprecationWarning,\n",
+    "            )\n",
+    "\n",
     "        # RNN\n",
     "        self.encoder_n_layers = encoder_n_layers\n",
     "        self.encoder_hidden_size = encoder_hidden_size\n",
@@ -322,7 +340,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.models import GRU\n",
+    "# from neuralforecast.models import GRU\n",
     "from neuralforecast.losses.pytorch import DistributionLoss\n",
     "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
     "\n",

neuralforecast/models/gru.py

@@ -3,7 +3,8 @@
 # %% auto 0
 __all__ = ['GRU']
 
-# %% ../../nbs/models.gru.ipynb 6
+# %% ../../nbs/models.gru.ipynb 7
+import warnings
 from typing import Optional
 
 import torch
@@ -13,12 +14,12 @@
 from ..common._base_recurrent import BaseRecurrent
 from ..common._modules import MLP
 
-# %% ../../nbs/models.gru.ipynb 7
+# %% ../../nbs/models.gru.ipynb 8
 class GRU(BaseRecurrent):
     """GRU
 
     Multi Layer Recurrent Network with Gated Units (GRU), and
-    MLP decoder. The network has `tanh` or `relu` non-linearities, it is trained
+    MLP decoder. The network has non-linear activation functions, it is trained
     using ADAM stochastic gradient descent. The network accepts static, historic
     and future exogenous data, flattens the inputs.
 
@@ -28,7 +29,7 @@ class GRU(BaseRecurrent):
     `inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.<br>
     `encoder_n_layers`: int=2, number of layers for the GRU.<br>
     `encoder_hidden_size`: int=200, units for the GRU's hidden state size.<br>
-    `encoder_activation`: str=`tanh`, type of GRU activation from `tanh` or `relu`.<br>
+    `encoder_activation`: Optional[str]=None, Deprecated. Activation function in GRU is frozen in PyTorch.<br>
     `encoder_bias`: bool=True, whether or not to use biases b_ih, b_hh within GRU units.<br>
     `encoder_dropout`: float=0., dropout regularization applied to GRU outputs.<br>
     `context_size`: int=10, size of context vector for each timestamp on the forecasting window.<br>
@@ -72,7 +73,7 @@ def __init__(
         inference_input_size: int = -1,
         encoder_n_layers: int = 2,
         encoder_hidden_size: int = 200,
-        encoder_activation: str = "tanh",
+        encoder_activation: Optional[str] = None,
         encoder_bias: bool = True,
         encoder_dropout: float = 0.0,
         context_size: int = 10,
@@ -129,6 +130,14 @@ def __init__(
             **trainer_kwargs
         )
 
+        if encoder_activation is not None:
+            warnings.warn(
+                "The 'encoder_activation' argument is deprecated and will be removed in "
+                "future versions. The activation function in GRU is frozen in PyTorch and "
+                "it cannot be modified.",
+                DeprecationWarning,
+            )
+
         # RNN
         self.encoder_n_layers = encoder_n_layers
         self.encoder_hidden_size = encoder_hidden_size