Update configuration_HelpingAI.py

configuration_HelpingAI.py

@@ -6,55 +6,90 @@ from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
+
 class HelpingAIConfig(PretrainedConfig):
-    keys_to_ignore_at_inference = ["past_key_values"]
     model_type = "HelpingAI"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
     def __init__(
         self,
-        vocab_size=50304,
+        vocab_size=50281,
         hidden_size=2560,
-        intermediate_size=6912,
         num_hidden_layers=32,
         num_attention_heads=32,
-        num_key_value_heads=32,
         head_dim=256,
+        num_local_experts=8,
+        num_experts_per_tok=2,
+        intermediate_size=6912,
         hidden_act="silu",
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        classifier_dropout=0.1,
         max_position_embeddings=4096,
         initializer_range=0.02,
         rms_norm_eps=1e-6,
-        use_cache=True,
-        hidden_activation=None,
-        rope_theta=10000,
+        layer_norm_eps=1e-5,
+        use_cache=False,
+        bos_token_id=50278,
+        eos_token_id=50279,
+        pad_token_id=50279,
+        tie_word_embeddings=False,
         rope_pct=0.25,
-        attention_bias=False,
-        attention_dropout=0.0,
-        num_experts_per_tok=2,
-        num_local_experts=8,
-        router_aux_loss_coef=0.02,
+        rope_theta=10000,
+        partial_rotary_factor=0.25,
+        use_qkv_bias=False,
         output_router_logits=False,
-        norm_eps=1.0e-5,
+        router_aux_loss_coef=0.02,
         **kwargs,
     ):
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
         self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings        
+        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
         self.head_dim = head_dim
+        self.num_local_experts = num_local_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.intermediate_size = intermediate_size
         self.hidden_act = hidden_act
-        self.hidden_activation = hidden_activation
-        self.num_key_value_heads = num_key_value_heads
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.classifier_dropout = classifier_dropout
         self.initializer_range = initializer_range
         self.rms_norm_eps = rms_norm_eps
+        self.layer_norm_eps = layer_norm_eps
         self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.rope_pct = rope_pct
         self.rope_theta = rope_theta
-        self.attention_bias = attention_bias 
-        self.attention_dropout = attention_dropout
-        self.num_experts_per_tok = num_experts_per_tok
-        self.num_local_experts = num_local_experts
-        self.router_aux_loss_coef = router_aux_loss_coef
+        self.partial_rotary_factor = partial_rotary_factor
+        self.use_qkv_bias = use_qkv_bias
         self.output_router_logits = output_router_logits
-        self.rope_pct = rope_pct
-        self.norm_eps = norm_eps
-        super().__init__(**kwargs)
+        self.router_aux_loss_coef = router_aux_loss_coef
+
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size is not divisble by the number of attention heads! Make sure to update them!"
+            )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")