Update configuration_alinlight.py

configuration_alinlight.py

@@ -1,63 +1,115 @@
-# -*- coding: utf-8 -*-
-# Copyright 2026 EngineerGL Research.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from transformers import PretrainedConfig
-
-class AlinlightConfig(PretrainedConfig):
-    model_type = "alinlight"
-    
-    def __init__(
-        self,
-        vocab_size=128000,
-        hidden_size=2048,
-        intermediate_size=5632,
-        num_hidden_layers=22,
-        num_attention_heads=32,
-        num_key_value_heads=8,
-        max_position_embeddings=4096,
-        sliding_window=4096,
-        attention_dropout=0.0,
-        rms_norm_eps=1e-5,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        initializer_range=0.02,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=True,
-        **kwargs,
-    ):
-        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
-        )
-        self.vocab_size = vocab_size
-        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.num_key_value_heads = num_key_value_heads
-        self.max_position_embeddings = max_position_embeddings
-        self.sliding_window = sliding_window
-        self.attention_dropout = attention_dropout
-        self.rms_norm_eps = rms_norm_eps
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
+# -*- coding: utf-8 -*-
+# Copyright 2026 EngineerGL Research.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers import PretrainedConfig
+
+class AlinlightConfig(PretrainedConfig):
+    """
+    Configuration class for Alinlight model.
+    
+    Args:
+        vocab_size (int): Vocabulary size of the model.
+        hidden_size (int): Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (int): Dimensionality of the "intermediate" (i.e., feed-forward) 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.
+        num_key_value_heads (int): Number of key/value heads for Grouped Query Attention.
+        max_position_embeddings (int): The maximum sequence length that this model might ever be used with.
+        rope_theta (float): The base period of the RoPE embeddings.
+        rope_scaling (dict, optional): Dictionary containing the scaling configuration for the RoPE embeddings.
+        sliding_window (int, optional): Sliding window size for local attention. None to disable.
+        attention_dropout (float): The dropout ratio for the attention probabilities.
+        use_qk_norm (bool): Whether to apply RMSNorm to Query and Key matrices.
+        attn_logit_softcapping (float, optional): If set, applies tanh soft-capping to attention logits (Gemma-2 style).
+        rms_norm_eps (float): The epsilon used by the rms normalization layers.
+        initializer_range (float): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        resid_pdrop (float): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        embed_pdrop (float): The dropout probability for the embedding layer.
+        embed_scale (bool): Whether to scale embeddings by sqrt(hidden_size).
+        final_logit_softcapping (float, optional): If set, applies tanh soft-capping to final LM head logits.
+        z_loss_weight (float): Coefficient for the Z-loss regularization term (stabilizes final logits).
+    """
+    model_type = "alinlight"
+    
+    def __init__(
+        self,
+        # Architecture
+        vocab_size=128000,
+        hidden_size=2048,
+        intermediate_size=5632,
+        num_hidden_layers=22,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        
+        # Positional Encoding
+        max_position_embeddings=4096,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        
+        # Attention
+        sliding_window=None,
+        attention_dropout=0.0,
+        use_qk_norm=True,
+        attn_logit_softcapping=50.0,
+        
+        # Normalization & Regularization
+        rms_norm_eps=1e-6,
+        initializer_range=0.02,
+        resid_pdrop=0.0,
+        embed_pdrop=0.0,
+        
+        # Stability Features
+        embed_scale=True,
+        final_logit_softcapping=30.0,
+        z_loss_weight=1e-4,
+        
+        # System
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        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.num_key_value_heads = num_key_value_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.sliding_window = sliding_window
+        self.attention_dropout = attention_dropout
+        self.use_qk_norm = use_qk_norm
+        self.attn_logit_softcapping = attn_logit_softcapping
+        self.rms_norm_eps = rms_norm_eps
+        self.initializer_range = initializer_range
+        self.resid_pdrop = resid_pdrop
+        self.embed_pdrop = embed_pdrop
+        self.embed_scale = embed_scale
+        self.final_logit_softcapping = final_logit_softcapping
+        self.z_loss_weight = z_loss_weight
+        self.use_cache = use_cache
+
+        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
+        )