Create configuration_helion.py

configuration_helion.py

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+"""
+Helion Model Configuration
+"""
+
+from transformers import PretrainedConfig
+
+
+class HelionConfig(PretrainedConfig):
+    """
+    Configuration class for Helion model.
+    
+    Args:
+        vocab_size (`int`, *optional*, defaults to 100000):
+            Vocabulary size of the Helion model.
+        hidden_size (`int`, *optional*, defaults to 6144):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 24576):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            Number of key-value heads for Grouped Query Attention.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function.
+        max_position_embeddings (`int`, *optional*, defaults to 16384):
+            Maximum sequence length that the model can handle.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            Standard deviation of the truncated_normal_initializer.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-6):
+            Epsilon value for RMSNorm layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether to use cache for faster decoding.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie input and output embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for RoPE.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in attention layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            Dropout probability for attention weights.
+    """
+    
+    model_type = "helion"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100000,
+        hidden_size=6144,
+        intermediate_size=24576,
+        num_hidden_layers=48,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=16384,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        
+        # GQA parameters
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        
+        # Validate rope_scaling
+        if self.rope_scaling is not None:
+            if not isinstance(self.rope_scaling, dict):
+                raise ValueError("`rope_scaling` must be a dictionary")
+            
+            required_keys = {"type", "factor"}
+            if not all(key in self.rope_scaling for key in required_keys):
+                raise ValueError(f"`rope_scaling` must contain keys {required_keys}")
+            
+            if self.rope_scaling["type"] not in ["linear", "dynamic"]:
+                raise ValueError("`rope_scaling.type` must be 'linear' or 'dynamic'")
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )