[FEAT][Snake]

zeta/nn/modules/__init__.py

@@ -222,6 +222,7 @@
 from zeta.nn.modules.cope import CoPE
 from zeta.nn.modules.multi_layer_key_cache import MultiLayerKeyValueAttention
 from zeta.nn.modules.evlm_xattn import GatedMoECrossAttn, GatedXAttention
+from zeta.nn.modules.snake_act import Snake
 
 # from zeta.nn.modules.img_reshape import image_reshape
 # from zeta.nn.modules.flatten_features import flatten_features
@@ -447,4 +448,5 @@
     "MultiLayerKeyValueAttention",
     "GatedMoECrossAttn",
     "GatedXAttention",
+    "Snake",
 ]

zeta/nn/modules/pretrained_t_five.py

@@ -0,0 +1,38 @@
+import torch
+from transformers import T5Tokenizer, T5EncoderModel
+from loguru import logger
+
+
+class PretrainedT5Embedder:
+    def __init__(self, model_name: str = "t5-small", *args, **kwargs):
+        """
+        Initializes the PretrainedT5Embedder with a specified T5 model.
+
+        Args:
+            model_name (str): The name of the pre-trained T5 model to use.
+        """
+        logger.info(
+            f"Initializing the T5 tokenizer and model with {model_name}."
+        )
+        self.tokenizer = T5Tokenizer.from_pretrained(model_name)
+        self.model = T5EncoderModel.from_pretrained(model_name, *args, **kwargs)
+
+    def run(self, text: str, *args, **kwargs) -> torch.Tensor:
+        """
+        Encodes the input text using the T5 model and returns the embeddings.
+
+        Args:
+            text (str): The input text to be embedded.
+
+        Returns:
+            torch.Tensor: The embedded representation of the input text.
+        """
+        logger.info(f"Encoding the text: {text}")
+        inputs = self.tokenizer(
+            text, return_tensors="pt", padding=True, truncation=True
+        )
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        embeddings = outputs.last_hidden_state.mean(dim=1)
+        logger.info("Text successfully embedded.")
+        return embeddings

zeta/nn/modules/snake_act.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn as nn
+
+
+class Snake(nn.Module):
+    def __init__(self, alpha: float = 1.0):
+        super(Snake, self).__init__()
+        self.alpha = nn.Parameter(torch.tensor(alpha))
+
+    def forward(self, x):
+        return x + (1 / self.alpha) * torch.sin(self.alpha * x) ** 2
+
+
+# # Example usage
+# snake = Snake()
+# x = torch.randn(10, 100, 100)  # Example input tensor
+# output = snake(x)
+# print(output)