configuration

Signed-off-by: jupyterjazz <[email protected]>

configuration_xlm_roberta.py

@@ -1,44 +1,89 @@
-from transformers import PretrainedConfig
+from typing import Any, Dict, List, Optional, Union
+
 import torch
+from transformers import PretrainedConfig
+
 
 class XLMRobertaFlashConfig(PretrainedConfig):
     def __init__(
-            self,
-            vocab_size=30522,
-            hidden_size=768,
-            num_hidden_layers=12,
-            num_attention_heads=12,
-            intermediate_size=3072,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.1,
-            attention_probs_dropout_prob=0.1,
-            max_position_embeddings=512,
-            type_vocab_size=2,
-            initializer_range=0.02,
-            layer_norm_eps=1e-12,
-            pad_token_id=1,
-            bos_token_id=0,
-            eos_token_id=2,
-            position_embedding_type="absolute",
-            rotary_emb_base=10000.0,
-            use_cache=True,
-            classifier_dropout=None,
-            lora_adaptations=None,
-            lora_prompts=None,
-            lora_rank=4,
-            lora_dropout_p=0.0,
-            lora_alpha=1,
-            lora_main_params_trainable=False,
-            load_trained_adapters=False,
-            use_flash_attn=True,
-            torch_dtype=None,
-            emb_pooler=None,
-            matryoshka_dimensions=None,
-            truncate_dim=None,
-            **kwargs,
+        self,
+        vocab_size: int = 250002,
+        hidden_size: int = 1024,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        intermediate_size: int = 4096,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 8194,
+        type_vocab_size: int = 1,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-05,
+        pad_token_id: int = 1,
+        bos_token_id: int = 0,
+        eos_token_id: int = 2,
+        position_embedding_type: str = "rotary",
+        rotary_emb_base: float = 10000.0,
+        use_cache: bool = True,
+        classifier_dropout: Optional[float] = None,
+        lora_adaptations: Optional[List[str]] = None,
+        lora_prompts: Optional[Dict[str, str]] = None,
+        lora_rank: int = 4,
+        lora_dropout_p: float = 0.0,
+        lora_alpha: int = 1,
+        lora_main_params_trainable: bool = False,
+        load_trained_adapters: bool = False,
+        use_flash_attn: bool = True,
+        torch_dtype: Optional[Union[str, torch.dtype]] = None,
+        emb_pooler: Optional[str] = None,
+        matryoshka_dimensions: Optional[List[int]] = None,
+        truncate_dim: Optional[int] = None,
+        **kwargs: Dict[str, Any],
     ):
-        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        """
+        Initialize the XLMRobertaFlashConfig configuration.
+
+        Args:
+            vocab_size (int): Size of the vocabulary.
+            hidden_size (int): Dimensionality of the encoder layers and the pooler layer.
+            num_hidden_layers (int): Number of hidden layers in the Transformer encoder.
+            num_attention_heads (int): Number of attention heads for each attention layer in the Transformer encoder.
+            intermediate_size (int): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer.
+            hidden_act (str): The activation function to use.
+            hidden_dropout_prob (float): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob (float): The dropout ratio for the attention probabilities.
+            max_position_embeddings (int): The maximum length of the position embeddings.
+            type_vocab_size (int): The vocabulary size of the token type ids.
+            initializer_range (float): The standard deviation for initializing all weight matrices.
+            layer_norm_eps (float): The epsilon used by the layer normalization layers.
+            pad_token_id (int): The ID of the padding token.
+            bos_token_id (int): The ID of the beginning-of-sequence token.
+            eos_token_id (int): The ID of the end-of-sequence token.
+            position_embedding_type (str): Type of position embeddings. Options are 'absolute', 'alibi', or 'rotary'.
+            rotary_emb_base (float): Base for rotary embeddings.
+            use_cache (bool): Whether or not the model should return the last key/values attentions (not used by all models).
+            classifier_dropout (Optional[float]): The dropout ratio for the classification head.
+            lora_adaptations (Optional[List[str]]): LoRA adaptations configuration.
+            lora_prompts (Optional[Dict[str, str]]): LoRA prompts configuration.
+            lora_rank (int): Rank for LoRA adaptations.
+            lora_dropout_p (float): Dropout probability for LoRA adaptations.
+            lora_alpha (int): Alpha parameter for LoRA.
+            lora_main_params_trainable (bool): Whether to make the main model parameters trainable when using LoRA.
+            load_trained_adapters (bool): Whether to load trained adapters.
+            use_flash_attn (bool): Whether to use FlashAttention.
+            torch_dtype (Optional[Union[str, torch.dtype]]): Data type for the tensors.
+            emb_pooler (Optional[str]): Pooling layer configuration.
+            matryoshka_dimensions (Optional[List[int]]): Configuration for matryoshka dimension reduction.
+            truncate_dim (Optional[int]): Dimension to truncate embeddings to, if any.
+            **kwargs (Dict[str, Any]): Additional keyword arguments passed to the configuration.
+        """
 
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
 
         self.vocab_size = vocab_size
         self.hidden_size = hidden_size
@@ -67,7 +112,11 @@ class XLMRobertaFlashConfig(PretrainedConfig):
         self.emb_pooler = emb_pooler
         self.matryoshka_dimensions = matryoshka_dimensions
         self.truncate_dim = truncate_dim
-        if torch_dtype and hasattr(torch, torch_dtype) and type(getattr(torch, torch_dtype)) is torch.dtype:
+        if (
+            torch_dtype
+            and hasattr(torch, torch_dtype)
+            and type(getattr(torch, torch_dtype)) is torch.dtype
+        ):
             self.torch_dtype = getattr(torch, torch_dtype)
         else:
             self.torch_dtype = torch_dtype