[feat] Add initial Gemma 4 multimodal bridge supportFeat/gemma4 support

src/mcore_bridge/config/model_config.py

@@ -126,9 +126,11 @@ class ModelConfig(TransformerConfig):
     ffn_hidden_size: Optional[int] = None
     num_attention_heads: Optional[int] = None
     num_query_groups: Optional[int] = None
+    num_global_query_groups: Optional[int] = None
     softmax_type: Literal['vanilla', 'off-by-one', 'learnable'] = 'vanilla'
     window_size: Optional[str] = None
     window_attn_skip_freq: Optional[str] = None
+    layer_types: Optional[List[str]] = None
     max_position_embeddings: Optional[int] = None
 
     position_embedding_type: Literal['learned_absolute', 'rope', 'mrope', 'none'] = 'rope'
@@ -153,6 +155,7 @@ class ModelConfig(TransformerConfig):
     attention_dropout: float = 0.
     hidden_dropout: float = 0.
     kv_channels: Optional[int] = None
+    global_kv_channels: Optional[int] = None
     qk_layernorm: bool = False
     qk_l2_norm: bool = False
     no_rope_freq: Optional[int] = None
@@ -207,6 +210,14 @@ class ModelConfig(TransformerConfig):
     hf_config: Optional[PretrainedConfig] = None
     vit_attn_impl: Optional[str] = None  # e.g. 'flash_attention_2'
 
+    # gemma4
+    hidden_size_per_layer_input: Optional[int] = None
+    vocab_size_per_layer_input: Optional[int] = None
+    num_kv_shared_layers: int = 0
+    enable_moe_block: bool = False
+    use_double_wide_mlp: bool = False
+    top_k_experts: Optional[int] = None
+
     # Override
     perform_initialization: bool = False
     apply_query_key_layer_scaling: Optional[bool] = None

src/mcore_bridge/config/parser.py

@@ -11,6 +11,7 @@
     'ffn_hidden_size': ['intermediate_size'],
     'num_attention_heads': ['num_attention_heads'],
     'num_query_groups': ['num_key_value_heads'],
+    'num_global_query_groups': ['num_global_key_value_heads'],
     'max_position_embeddings': ['max_position_embeddings'],
     'layernorm_epsilon': ['rms_norm_eps'],
     'rotary_base': ['rope_theta'],
@@ -21,6 +22,7 @@
     'add_qkv_bias': ['attention_bias', 'qkv_bias', 'use_bias'],
     'add_bias_linear': ['mlp_bias'],
     'kv_channels': ['head_dim'],
+    'global_kv_channels': ['global_head_dim'],
     'hf_model_type': ['model_type'],
     # moe
     'moe_ffn_hidden_size': ['moe_intermediate_size'],
@@ -60,6 +62,14 @@
     'window_size': ['sliding_window'],
     'layer_types': ['layer_types'],
     'interleave_moe_layer_step': ['interleave_moe_layer_step'],
+    # gemma4
+    'hidden_size_per_layer_input': ['hidden_size_per_layer_input'],
+    'vocab_size_per_layer_input': ['vocab_size_per_layer_input'],
+    'num_kv_shared_layers': ['num_kv_shared_layers'],
+    'enable_moe_block': ['enable_moe_block'],
+    'use_double_wide_mlp': ['use_double_wide_mlp'],
+    'num_experts': ['num_experts'],
+    'top_k_experts': ['top_k_experts'],
 }
 
 
@@ -112,6 +122,9 @@ def hf_to_mcore_config(hf_config: PretrainedConfig) -> Dict[str, Any]:
     interleave_moe_layer_step = res.pop('interleave_moe_layer_step', None)
     window_size = res.pop('window_size', None)
     rope_scaling = res.get('rope_scaling') or {}
+    if 'full_attention' in rope_scaling:
+        rope_scaling = rope_scaling['full_attention']
+        res['rope_scaling'] = rope_scaling
     if llm_model_type in {'qwen3', 'qwen3_moe', 'qwen3_next'} or hf_model_type in {
             'qwen3_omni_moe', 'qwen3_omni', 'qwen3_vl', 'qwen3_vl_moe', 'qwen3_5', 'qwen3_5_moe'
     }:
@@ -192,7 +205,15 @@ def hf_to_mcore_config(hf_config: PretrainedConfig) -> Dict[str, Any]:
             res['moe_layer_freq'] = f"[{','.join(moe_layer_freq)}]"
     elif hf_model_type == 'glm4v':
         res['rotary_interleaved'] = True
+    elif hf_model_type == 'gemma4':
+        res['qk_layernorm'] = True
+        if layer_types is not None:
+            res['window_attn_skip_freq'] = f"[{','.join(['1' if lt == 'sliding_attention' else '0' for lt in layer_types])}]"
+            res['layer_types'] = layer_types
 
+    if rope_scaling.get('rope_type') is None and rope_scaling.get('type') is not None:
+        rope_scaling = {**rope_scaling, 'rope_type': rope_scaling['type']}
+        res['rope_scaling'] = rope_scaling
     if 'partial_rotary_factor' not in res and 'partial_rotary_factor' in rope_scaling:
         res['partial_rotary_factor'] = rope_scaling['partial_rotary_factor']
     if 'rotary_base' not in res and 'rope_theta' in rope_scaling:

src/mcore_bridge/model/constant.py

@@ -26,6 +26,7 @@ class MLLMModelType:
     glm4v_moe = 'glm4v_moe'
     kimi_vl = 'kimi_vl'
     llama4 = 'llama4'
+    gemma4 = 'gemma4'
 
 
 class ModelType(LLMModelType, MLLMModelType):

src/mcore_bridge/model/mm_gpts/__init__.py

@@ -1,2 +1,2 @@
 # Copyright (c) ModelScope Contributors. All rights reserved.
-from . import glm, internvl, kimi_vl, llama4, qwen, qwen3_5, qwen3_5_gdn, qwen3_omni, qwen3_vl
+from . import gemma4, glm, internvl, kimi_vl, llama4, qwen, qwen3_5, qwen3_5_gdn, qwen3_omni, qwen3_vl