Use gelu_approximate directly in t5 presets (#1284)

We should keep `activation="gelu"` meaning the same canonical thing it
means across Keras. Let's use our string identifier for approximate
gelu directly in the preset

keras_nlp/models/t5/t5_backbone.py

@@ -58,8 +58,7 @@ class T5Backbone(Backbone):
         dropout: float. Dropout probability for the Transformer layers.
         activation: activation function (or activation string name). The
             activation to be used in the inner dense blocks of the
-            Transformer layers. The original T5 architecture used `"relu"`,
-            but more recent versions use `"gelu"`. Defaults to `"gelu"`.
+            Transformer layers. Defaults to `"relu"`.
         use_gated_activation: boolean. Whether to use activation gating in
             the inner dense blocks of the Transformer layers.
             The original T5 architecture didn't use gating, but more
@@ -80,7 +79,7 @@ def __init__(
         intermediate_dim,
         key_value_dim=None,
         dropout=0.1,
-        activation="gelu",
+        activation="relu",
         use_gated_activation=True,
         layer_norm_epsilon=1e-06,
         tie_embedding_weights=False,

keras_nlp/models/t5/t5_presets.py

@@ -106,7 +106,7 @@
             "intermediate_dim": 1024,
             "key_value_dim": 64,
             "dropout": 0.1,
-            "activation": "gelu",
+            "activation": "keras_nlp>gelu_approximate",
             "use_gated_activation": True,
             "layer_norm_epsilon": 1e-06,
         },
@@ -130,7 +130,7 @@
             "hidden_dim": 768,
             "intermediate_dim": 2048,
             "dropout": 0.1,
-            "activation": "gelu",
+            "activation": "keras_nlp>gelu_approximate",
             "use_gated_activation": True,
             "layer_norm_epsilon": 1e-06,
         },
@@ -154,7 +154,7 @@
             "hidden_dim": 1024,
             "intermediate_dim": 2816,
             "dropout": 0.1,
-            "activation": "gelu",
+            "activation": "keras_nlp>gelu_approximate",
             "use_gated_activation": True,
             "layer_norm_epsilon": 1e-06,
         },

keras_nlp/models/t5/t5_transformer_layer.py

@@ -65,11 +65,6 @@ def __init__(
             self.cross_attention_layer_norm = T5LayerNorm(layer_norm_epsilon)
             self.cross_attention_dropout = keras.layers.Dropout(dropout)
 
-        if activation == "gelu":
-            activation = keras.activations.get("keras_nlp>gelu_approximate")
-        else:
-            activation = keras.activations.get(activation)
-
         self.input_projector = keras.layers.Dense(
             intermediate_dim,
             use_bias=False,