wehub-resource-sync
1179 lines
44 KiB
Python
1179 lines
44 KiB
Python
import copy
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from transformers import __version__ as TRANSFORMERS_VERSION
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class ModelMapper:
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def __init__(self):
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self.attrs = []
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self.mapper = dict()
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self.init_models()
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def get_map(self, config):
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model_type = config.model_type
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if model_type == 'chatglm':
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if hasattr(config, 'vocab_size') and config.vocab_size == 130528:
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model_type = 'chatglm'
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else:
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model_type = 'chatglm2'
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if model_type in self.mapper:
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return model_type, self.mapper[model_type]
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return model_type, self.default_map
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def regist(self, model_type, model_map):
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assert('config' in model_map and
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'decoder' in model_map and
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'attention' in model_map)
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self.mapper[model_type] = model_map
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def init_models(self):
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self.init_default_map()
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for method_name in dir(self):
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if callable(getattr(self, method_name)) and method_name.startswith("regist_"):
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method = getattr(self, method_name)
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method()
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def regist_llama(self):
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llama_map = self.default_map
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self.regist('llama', llama_map)
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self.regist('qwen2', llama_map)
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self.regist('internlm', llama_map)
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self.regist('mobilellm', llama_map)
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# baichuan
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baichuan_map = copy.deepcopy(self.default_map)
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baichuan_map[self.attention_key] = {
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'qkv_proj': 'W_pack',
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'o_proj': 'o_proj'
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}
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self.regist('baichuan', baichuan_map)
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def regist_deepseek_vl(self):
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deepseek_vlmap = {
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'config': {
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'hidden_size': 'language_config.hidden_size',
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'num_attention_heads': 'language_config.num_attention_heads',
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'num_hidden_layers': 'language_config.num_hidden_layers',
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'rope_theta': 'language_config.rope_theta',
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'head_dim': 'language_config.head_dim',
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'num_key_value_heads': 'language_config.num_key_value_heads',
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},
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'model': {
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'lm': 'language_model.lm_head',
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'embed': 'language_model.model.embed_tokens',
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'blocks': 'language_model.model.layers',
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'final_layernorm': 'language_model.model.norm',
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'visual': 'vision_model'
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},
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'decoder': {
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'self_attn': 'self_attn',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm'
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},
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'attention': {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj'
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}
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}
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self.regist('deepseek-vl', deepseek_vlmap)
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def regist_qwen_omni(self):
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omni_map = {
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'config': {
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'hidden_size': 'thinker_config.text_config.hidden_size',
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'head_dim': 'thinker_config.text_config.head_dim',
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'num_attention_heads': 'thinker_config.text_config.num_attention_heads',
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'num_hidden_layers': 'thinker_config.text_config.num_hidden_layers',
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'num_key_value_heads': 'thinker_config.text_config.num_key_value_heads',
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'rope_theta': 'thinker_config.text_config.rope_theta',
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'rope_scaling': 'thinker_config.text_config.rope_scaling'
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},
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'model': {
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'lm': 'thinker.lm_head',
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'embed': 'thinker.model.embed_tokens',
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'blocks': 'thinker.model.layers',
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'final_layernorm': 'thinker.model.norm',
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'visual': 'thinker.visual',
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'audio': 'thinker.audio_tower',
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'talker': 'talker',
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'token2wav': 'token2wav'
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},
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'decoder': self.default_decoder,
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'attention': self.default_attention
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}
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self.regist('qwen2_5_omni', omni_map)
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def regist_qwen(self):
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qwen_map = {
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'config': {
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'hidden_size': 'hidden_size',
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'num_attention_heads': 'num_attention_heads',
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'num_hidden_layers': 'num_hidden_layers',
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'rope_theta': 'rotary_emb_base',
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},
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'model': {
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'lm': 'lm_head',
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'embed': 'transformer.wte',
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'blocks': 'transformer.h',
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'final_layernorm': 'transformer.ln_f',
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'visual': 'transformer.visual'
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},
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'decoder': {
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'self_attn': 'attn',
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'mlp': 'mlp',
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'input_layernorm': 'ln_1',
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'post_attention_layernorm': 'ln_2'
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},
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'attention': {
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'qkv_proj': 'c_attn',
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'o_proj': 'c_proj'
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}
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}
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self.regist('qwen', qwen_map)
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def regist_qwen3(self):
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qwen3_attention = {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj',
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'q_norm': 'q_norm',
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'k_norm': 'k_norm'
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}
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qwen3_map = {
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'config': self.default_config,
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'model': self.default_model,
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'decoder': self.default_decoder,
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'attention': qwen3_attention
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}
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self.regist('qwen3', qwen3_map)
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def regist_llama4_text(self):
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llama4_text_attention = {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj',
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'qk_norm': 'qk_norm'
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}
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llama4_text_decoder = copy.deepcopy(self.default_decoder)
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llama4_text_decoder['mlp'] = 'feed_forward'
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llama4_text_map = {
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'config': self.default_config,
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'model': self.default_model,
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'decoder': llama4_text_decoder,
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'attention': llama4_text_attention
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}
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self.regist('llama4_text', llama4_text_map)
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def regist_qwen3_moe(self):
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qwen3_attention = {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj',
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'q_norm': 'q_norm',
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'k_norm': 'k_norm'
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}
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qwen3_mlp = {
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'num_experts': 'experts.num_experts',
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'top_k': 'gate.top_k',
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'norm_topk_prob': 'gate.norm_topk_prob',
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'gate': 'gate',
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'experts': 'experts'
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}
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qwen3_moe_map = {
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'config': self.default_config,
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'model': self.default_model,
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'decoder': self.default_decoder,
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'attention': qwen3_attention,
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'mlp': qwen3_mlp,
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}
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self.regist('qwen3_moe', qwen3_moe_map)
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def regist_mimo(self):
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mimo_model = copy.deepcopy(self.default_model)
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mimo_model['mtp'] = 'model.mtp_layers'
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mimo_map = {
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'config': self.default_config,
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'model': mimo_model,
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'decoder': self.default_decoder,
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'attention': self.default_attention
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}
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self.regist('mimo', mimo_map)
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def regist_poi_qwen2_mtp(self):
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poi_qwen2_mtp_model = copy.deepcopy(self.default_model)
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poi_qwen2_mtp_model['mtp1'] = 'MTP1'
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poi_qwen2_mtp_model['mtp2'] = 'MTP2'
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poi_qwen2_mtp_map = {
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'config': self.default_config,
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'model': poi_qwen2_mtp_model,
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'decoder': self.default_decoder,
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'attention': self.default_attention
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}
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self.regist('poi_qwen2_mtp', poi_qwen2_mtp_map)
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def regist_glm(self):
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glm_map = {
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'config': {
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'hidden_size': 'hidden_size',
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'num_attention_heads': 'num_attention_heads',
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'num_hidden_layers': 'num_layers'
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},
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'model': {
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'lm': 'lm_head',
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'embed': 'transformer.word_embeddings',
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'blocks': 'transformer.layers',
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'final_layernorm': 'transformer.final_layernorm',
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},
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'decoder': {
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'self_attn': 'attention',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm'
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},
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'attention': {
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'qkv_proj': 'query_key_value',
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'o_proj': 'dense'
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}
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}
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self.regist('chatglm', glm_map)
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def regist_glm2(self):
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glm2_map = {
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'config': {
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'hidden_size': 'hidden_size',
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'num_attention_heads': 'num_attention_heads',
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'num_key_value_heads': 'multi_query_group_num',
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'num_hidden_layers': 'num_layers',
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'rope_ratio': 'rope_ratio'
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},
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'model': {
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'lm': 'transformer.output_layer',
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'embed': 'transformer.embedding.word_embeddings',
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'blocks': 'transformer.encoder.layers',
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'final_layernorm': 'transformer.encoder.final_layernorm',
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},
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'decoder': {
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'self_attn': 'self_attention',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm'
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},
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'attention': {
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'qkv_proj': 'query_key_value',
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'o_proj': 'dense'
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}
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}
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self.regist('chatglm2', glm2_map)
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def regist_phi(self):
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phi_map = {
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'config': {
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'hidden_size': 'n_embd',
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'num_attention_heads': 'n_head',
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'num_hidden_layers': 'n_layer',
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'rotary_dim': 'rotary_dim'
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},
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'model': {
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'lm': 'lm_head.linear',
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'embed': 'transformer.embd.wte',
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'blocks': 'transformer.h',
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'final_layernorm': 'lm_head.ln',
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},
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'decoder': {
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'self_attn': 'mixer',
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'mlp': 'mlp',
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'input_layernorm': 'ln',
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},
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'attention': {
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'qkv_proj': 'Wqkv',
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'o_proj': 'out_proj'
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}
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}
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self.regist('phi-msft', phi_map)
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phi2_map = {
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'config': self.default_config,
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'model': {
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'lm': 'lm_head',
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'embed': 'model.embed_tokens',
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'blocks': 'model.layers',
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'final_layernorm': 'model.final_layernorm'
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},
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'decoder': {
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'self_attn': 'self_attn',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm'
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},
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'attention': {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'dense'
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}
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}
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self.regist('phi', phi2_map)
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def regist_intervl(self):
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intervl_map = {
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'config': {
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'hidden_size': 'llm_config.hidden_size',
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'num_attention_heads': 'llm_config.num_attention_heads',
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'num_hidden_layers': 'llm_config.num_hidden_layers',
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'rope_theta': 'llm_config.rope_theta',
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'head_dim': 'llm_config.head_dim',
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'num_key_value_heads': 'llm_config.num_key_value_heads',
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},
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'model': {
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'lm': 'language_model.lm_head',
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'embed': 'language_model.model.embed_tokens',
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'blocks': 'language_model.model.layers',
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'final_layernorm': 'language_model.model.norm',
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'visual': 'vision_model',
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'visual.mlp1': 'mlp1',
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'visual.select_layer': 'select_layer'
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},
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'decoder': {
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'self_attn': 'self_attn',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm'
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},
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'attention': {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj'
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}
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}
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self.regist('internvl_chat', intervl_map)
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def regist_gemma2(self):
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gemma2_decoder = copy.deepcopy(self.default_decoder)
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gemma2_decoder['pre_feedforward_layernorm'] = 'pre_feedforward_layernorm'
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gemma2_decoder['post_feedforward_layernorm'] = 'post_feedforward_layernorm'
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gemma2_map = {
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'config': self.default_config,
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'model': self.default_model,
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'decoder': gemma2_decoder,
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'attention': self.default_attention
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}
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self.regist('gemma2', gemma2_map)
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def regist_gemma3(self):
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gemma3_map = {
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'config': {
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'hidden_size': 'text_config.hidden_size',
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'head_dim': 'text_config.head_dim',
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'num_attention_heads': 'text_config.num_attention_heads',
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'num_hidden_layers': 'text_config.num_hidden_layers',
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'num_key_value_heads': 'text_config.num_key_value_heads',
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'rope_theta': 'text_config.rope_theta',
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'rope_parameters': 'text_config.rope_parameters',
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'max_position_embeddings': 'text_config.max_position_embeddings',
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'layer_types': 'text_config.layer_types',
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'sliding_window': 'text_config.sliding_window',
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'image_size': 'vision_config.image_size',
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'num_channels': 'vision_config.num_channels',
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'model_type': 'model_type',
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'image_token_index': 'image_token_index', #'<image_soft_token>'
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'boi_token_index': 'boi_token_index', #'<start_of_image>'
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'eoi_token_index': 'eoi_token_index', #'<end_of_image>'
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},
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'model': {
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'lm': 'language_model.lm_head',
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'embed': 'language_model.model.embed_tokens',
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'blocks': 'language_model.model.layers',
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'final_layernorm': 'language_model.model.norm',
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'vision_tower': 'vision_tower',
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'visual': 'vision_tower.vision_model',
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'multi_modal_projector': 'multi_modal_projector'
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},
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'decoder': {
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'self_attn': 'self_attn',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm',
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'pre_feedforward_layernorm': 'pre_feedforward_layernorm',
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'post_feedforward_layernorm': 'post_feedforward_layernorm'
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},
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'attention': {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj',
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'q_norm': 'q_norm',
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'k_norm': 'k_norm'
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}
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}
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self.regist('gemma3', gemma3_map)
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def regist_gemma3_text(self):
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gemma3_text_map = {
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'config': {
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'hidden_size': 'hidden_size',
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'head_dim': 'head_dim',
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'num_attention_heads': 'num_attention_heads',
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'num_hidden_layers': 'num_hidden_layers',
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'num_key_value_heads': 'num_key_value_heads',
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'rope_theta': 'rope_theta',
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'rope_parameters': 'rope_parameters',
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'max_position_embeddings': 'max_position_embeddings',
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'model_type': 'model_type',
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'vocab_size': 'vocab_size',
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'bos_token_id': 'bos_token_id',
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'eos_token_id': 'eos_token_id',
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'max_position_embeddings': 'max_position_embeddings',
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'pad_token_id': 'pad_token_id',
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'layer_types': 'layer_types',
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'sliding_window': 'sliding_window'
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},
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'model': {
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'lm': 'lm_head',
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'embed': 'model.embed_tokens',
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'blocks': 'model.layers',
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'final_layernorm': 'model.norm',
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'rotary_emb': 'model.rotary_emb',
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'rotary_emb_local': 'model.rotary_emb_local'
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},
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'decoder': {
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'self_attn': 'self_attn',
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'mlp': 'mlp',
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'input_layernorm': 'input_layernorm',
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'post_attention_layernorm': 'post_attention_layernorm',
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'pre_feedforward_layernorm': 'pre_feedforward_layernorm',
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'post_feedforward_layernorm': 'post_feedforward_layernorm'
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},
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'attention': {
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'q_proj': 'q_proj',
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'k_proj': 'k_proj',
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'v_proj': 'v_proj',
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'o_proj': 'o_proj',
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'q_norm': 'q_norm',
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'k_norm': 'k_norm'
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}
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}
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self.regist('gemma3_text', gemma3_text_map)
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def regist_gemma4(self):
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gemma4_config = {
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'hidden_size': 'text_config.hidden_size',
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'head_dim': 'text_config.head_dim',
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'num_attention_heads': 'text_config.num_attention_heads',
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'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_parameters': 'text_config.rope_parameters',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings',
|
|
'layer_types': 'text_config.layer_types',
|
|
'sliding_window': 'text_config.sliding_window',
|
|
'tie_word_embeddings': 'tie_word_embeddings',
|
|
}
|
|
gemma4_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.language_model.embed_tokens',
|
|
'blocks': 'model.language_model.layers',
|
|
'final_layernorm': 'model.language_model.norm',
|
|
'rotary_emb': 'model.language_model.rotary_emb',
|
|
'visual': 'model.vision_tower',
|
|
'audio': 'model.audio_tower',
|
|
'embed_vision': 'model.embed_vision',
|
|
'embed_audio': 'model.embed_audio',
|
|
# PLE (Per-Layer Embeddings) components
|
|
'embed_tokens_per_layer': 'model.language_model.embed_tokens_per_layer',
|
|
'per_layer_model_projection': 'model.language_model.per_layer_model_projection',
|
|
'per_layer_projection_norm': 'model.language_model.per_layer_projection_norm',
|
|
}
|
|
gemma4_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'mlp': 'mlp',
|
|
'input_layernorm': 'input_layernorm',
|
|
'post_attention_layernorm': 'post_attention_layernorm',
|
|
'pre_feedforward_layernorm': 'pre_feedforward_layernorm',
|
|
'post_feedforward_layernorm': 'post_feedforward_layernorm',
|
|
'layer_scalar': 'layer_scalar',
|
|
'per_layer_input_gate': 'per_layer_input_gate',
|
|
'per_layer_projection': 'per_layer_projection',
|
|
'post_per_layer_input_norm': 'post_per_layer_input_norm',
|
|
'act_fn': 'act_fn',
|
|
# MoE components (gemma4 26B-A4B)
|
|
'router': 'router',
|
|
'experts': 'experts',
|
|
'post_feedforward_layernorm_1': 'post_feedforward_layernorm_1',
|
|
'post_feedforward_layernorm_2': 'post_feedforward_layernorm_2',
|
|
'pre_feedforward_layernorm_2': 'pre_feedforward_layernorm_2',
|
|
}
|
|
gemma4_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'o_proj',
|
|
'q_norm': 'q_norm',
|
|
'k_norm': 'k_norm',
|
|
'v_norm': 'v_norm',
|
|
'k_eq_v': 'use_alternative_attention',
|
|
}
|
|
gemma4_map = {
|
|
'config': gemma4_config,
|
|
'model': gemma4_model,
|
|
'decoder': gemma4_decoder,
|
|
'attention': gemma4_attention,
|
|
}
|
|
self.regist('gemma4', gemma4_map)
|
|
|
|
def register_openelm(self):
|
|
openelm_config = {
|
|
'hidden_size': 'model_dim',
|
|
'head_dim': 'head_dim',
|
|
'num_attention_heads': 'num_query_heads',
|
|
'num_hidden_layers': 'num_transformer_layers',
|
|
'num_key_value_heads': 'num_kv_heads',
|
|
'rope_theta': 'rope_freq_constant'
|
|
}
|
|
openelm_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'transformer.token_embeddings',
|
|
'blocks': 'transformer.layers',
|
|
'final_layernorm': 'transformer.norm'
|
|
}
|
|
openelm_decoder = {
|
|
'self_attn': 'attn',
|
|
'mlp': 'ffn',
|
|
'input_layernorm': 'attn_norm',
|
|
'post_attention_layernorm': 'ffn_norm'
|
|
}
|
|
openelm_attention = {
|
|
'qkv_proj': 'qkv_proj',
|
|
'o_proj': 'out_proj',
|
|
'q_norm': 'q_norm',
|
|
'k_norm': 'k_norm'
|
|
}
|
|
openelm_map = {
|
|
'config': openelm_config,
|
|
'model': openelm_model,
|
|
'decoder': openelm_decoder,
|
|
'attention': openelm_attention
|
|
}
|
|
self.regist('openelm', openelm_map)
|
|
|
|
def regist_idefics3(self):
|
|
idefics3_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_theta': 'text_config.rope_theta',
|
|
'rope_scaling': 'text_config.rope_scaling'
|
|
}
|
|
idefics3_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.text_model.embed_tokens',
|
|
'blocks': 'model.text_model.layers',
|
|
'final_layernorm': 'model.text_model.norm',
|
|
'visual': 'model.vision_model',
|
|
'visual.connector': 'model.connector'
|
|
}
|
|
idefics3_map = {
|
|
'config': idefics3_config,
|
|
'model': idefics3_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('idefics3', idefics3_map)
|
|
self.regist('smolvlm', idefics3_map)
|
|
|
|
def regist_fastvlm(self):
|
|
fastvlm_model = copy.deepcopy(self.default_model)
|
|
fastvlm_model['visual'] = 'model.vision_tower'
|
|
fastvlm_model['visual.mm_projector'] = 'model.mm_projector'
|
|
fastvlm_map = {
|
|
'config': self.default_config,
|
|
'model': fastvlm_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('llava_qwen2', fastvlm_map)
|
|
|
|
def regist_qwen2audio(self):
|
|
qwen2audio_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_theta': 'text_config.rope_theta',
|
|
'rope_scaling': 'text_config.rope_scaling',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings'
|
|
}
|
|
qwen2audio_model = {
|
|
'lm': 'language_model.lm_head',
|
|
'embed': 'language_model.model.embed_tokens',
|
|
'blocks': 'language_model.model.layers',
|
|
'final_layernorm': 'language_model.model.norm',
|
|
'audio': 'audio_tower',
|
|
'audio.multi_modal_projector': 'multi_modal_projector'
|
|
}
|
|
qwen2audio_map = {
|
|
'config': qwen2audio_config,
|
|
'model': qwen2audio_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('qwen2_audio', qwen2audio_map)
|
|
|
|
def regist_qwenvl(self):
|
|
if TRANSFORMERS_VERSION <= '4.52.1':
|
|
return
|
|
qwen2vl_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.language_model.embed_tokens',
|
|
'blocks': 'model.language_model.layers',
|
|
'final_layernorm': 'model.language_model.norm',
|
|
'visual': 'model.visual'
|
|
}
|
|
qwen2vl_map = {
|
|
'config': self.default_config,
|
|
'model': qwen2vl_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('qwen2_vl', qwen2vl_map)
|
|
self.regist('qwen2_5_vl', qwen2vl_map)
|
|
qwen3vl_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_theta': 'text_config.rope_theta',
|
|
'rope_scaling': 'text_config.rope_scaling',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings'
|
|
}
|
|
qwen3_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'o_proj',
|
|
'q_norm': 'q_norm',
|
|
'k_norm': 'k_norm'
|
|
}
|
|
qwen3vl_map = {
|
|
'config': qwen3vl_config,
|
|
'model': qwen2vl_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': qwen3_attention
|
|
}
|
|
qwen3vlmoe_mlp = {
|
|
'num_experts': 'experts.num_experts',
|
|
'top_k': 'gate.top_k',
|
|
'gate': 'gate',
|
|
'experts': 'experts'
|
|
}
|
|
qwen3vlmoe_map = {
|
|
'config': qwen3vl_config,
|
|
'model': qwen2vl_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': qwen3_attention,
|
|
'mlp': qwen3vlmoe_mlp
|
|
}
|
|
self.regist('qwen3_vl', qwen3vl_map)
|
|
self.regist('qwen3_vl_moe', qwen3vlmoe_map)
|
|
|
|
def regist_hunyuan_v1_dense(self):
|
|
hunyuan_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'o_proj',
|
|
'q_norm': 'query_layernorm',
|
|
'k_norm': 'key_layernorm'
|
|
}
|
|
hunyuan_map = {
|
|
'config': self.default_config,
|
|
'model': self.default_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': hunyuan_attention
|
|
}
|
|
self.regist('hunyuan_v1_dense', hunyuan_map)
|
|
|
|
def regist_gpt_oss(self):
|
|
gpt_oss_config = {
|
|
'hidden_size': 'hidden_size',
|
|
'head_dim': 'head_dim',
|
|
'num_attention_heads': 'num_attention_heads',
|
|
'num_hidden_layers': 'num_hidden_layers',
|
|
'num_key_value_heads': 'num_key_value_heads',
|
|
'rope_theta': 'rope_theta',
|
|
'rope_scaling': 'rope_scaling',
|
|
'max_position_embeddings': 'max_position_embeddings',
|
|
'sliding_window': 'sliding_window',
|
|
'layer_types': 'layer_types'
|
|
}
|
|
gpt_oss_attention = copy.deepcopy(self.default_attention)
|
|
gpt_oss_attention['sinks'] = 'sinks'
|
|
gpt_oss_mlp = {
|
|
'num_experts': 'router.num_experts',
|
|
'top_k': 'router.top_k',
|
|
'router': 'router',
|
|
'experts': 'experts'
|
|
}
|
|
gpt_osss_map = {
|
|
'config': gpt_oss_config,
|
|
'model': self.default_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': gpt_oss_attention,
|
|
'mlp': gpt_oss_mlp
|
|
}
|
|
self.regist('gpt_oss', gpt_osss_map)
|
|
|
|
def regist_minicpm(self):
|
|
minicpm_config = copy.deepcopy(self.default_config)
|
|
minicpm_config['scale_emb'] = 'scale_emb'
|
|
minicpm_decoder = copy.deepcopy(self.default_decoder)
|
|
minicpm_decoder['scale_depth'] = 'scale_depth'
|
|
minicpm_map = {
|
|
'config': minicpm_config,
|
|
'model': self.default_model,
|
|
'decoder': minicpm_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('minicpm', minicpm_map)
|
|
|
|
def regist_minicpmv(self):
|
|
minicpmv_config = copy.deepcopy(self.default_config)
|
|
minicpmv_config['scale_emb'] = 'scale_emb'
|
|
minicpmv_config['patch_size'] = 'vision_config.patch_size'
|
|
minicpmv_config['image_size'] = 'vision_config.image_size'
|
|
minicpmv_model = {
|
|
'lm': 'llm.lm_head',
|
|
'embed': 'llm.model.embed_tokens',
|
|
'blocks': 'llm.model.layers',
|
|
'final_layernorm': 'llm.model.norm',
|
|
'visual': 'vpm',
|
|
'visual.resampler': 'resampler'
|
|
}
|
|
minicpmv_map = {
|
|
'config': minicpmv_config,
|
|
'model': minicpmv_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('minicpmv', minicpmv_map)
|
|
|
|
def regist_funaudiochat(self):
|
|
funaudiochat_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_theta': 'text_config.rope_theta',
|
|
'rope_scaling': 'text_config.rope_scaling',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings'
|
|
}
|
|
funaudiochat_model = {
|
|
'lm': 'language_model.lm_head',
|
|
'embed': 'language_model.model.embed_tokens',
|
|
'blocks': 'language_model.model.layers',
|
|
'final_layernorm': 'language_model.model.norm',
|
|
'audio': 'continuous_audio_tower',
|
|
'audio.audio_tower': 'audio_tower',
|
|
'audio.audio_invert_tower': 'audio_invert_tower'
|
|
}
|
|
qwen3_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'o_proj',
|
|
'q_norm': 'q_norm',
|
|
'k_norm': 'k_norm'
|
|
}
|
|
funaudiochat_map = {
|
|
'config': funaudiochat_config,
|
|
'model': funaudiochat_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': qwen3_attention
|
|
}
|
|
self.regist('funaudiochat', funaudiochat_map)
|
|
|
|
def regist_glm_ocr(self):
|
|
glm_ocr_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_parameters': 'text_config.rope_parameters',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings'
|
|
}
|
|
glm_ocr_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.language_model.embed_tokens',
|
|
'blocks': 'model.language_model.layers',
|
|
'final_layernorm': 'model.language_model.norm',
|
|
'visual': 'model.visual'
|
|
}
|
|
# GLM-OCR has same residual pattern as Gemma2:
|
|
# input_layernorm -> attn -> post_self_attn_layernorm -> residual
|
|
# -> post_attention_layernorm -> mlp -> post_mlp_layernorm -> residual
|
|
glm_ocr_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'mlp': 'mlp',
|
|
'input_layernorm': 'input_layernorm',
|
|
'post_attention_layernorm': 'post_self_attn_layernorm',
|
|
'pre_feedforward_layernorm': 'post_attention_layernorm',
|
|
'post_feedforward_layernorm': 'post_mlp_layernorm'
|
|
}
|
|
glm_ocr_map = {
|
|
'config': glm_ocr_config,
|
|
'model': glm_ocr_model,
|
|
'decoder': glm_ocr_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
self.regist('glm_ocr', glm_ocr_map)
|
|
|
|
def regist_lfm2(self):
|
|
lfm2_config = {
|
|
'hidden_size': 'hidden_size',
|
|
'num_attention_heads': 'num_attention_heads',
|
|
'num_hidden_layers': 'num_hidden_layers',
|
|
'num_key_value_heads': 'num_key_value_heads',
|
|
'rope_theta': 'rope_theta',
|
|
'rope_parameters': 'rope_parameters',
|
|
'max_position_embeddings': 'max_position_embeddings',
|
|
'layer_types': 'layer_types',
|
|
'conv_L_cache': 'conv_L_cache',
|
|
}
|
|
lfm2_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.embed_tokens',
|
|
'blocks': 'model.layers',
|
|
'final_layernorm': 'model.embedding_norm',
|
|
}
|
|
lfm2_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'linear_attn': 'conv',
|
|
'mlp': 'feed_forward',
|
|
'input_layernorm': 'operator_norm',
|
|
'post_attention_layernorm': 'ffn_norm',
|
|
}
|
|
lfm2_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'out_proj',
|
|
'q_norm': 'q_layernorm',
|
|
'k_norm': 'k_layernorm',
|
|
}
|
|
lfm2_linear_attention = {
|
|
'in_proj': 'in_proj',
|
|
'conv': 'conv',
|
|
'out_proj': 'out_proj',
|
|
}
|
|
lfm2_map = {
|
|
'config': lfm2_config,
|
|
'model': lfm2_model,
|
|
'decoder': lfm2_decoder,
|
|
'attention': lfm2_attention,
|
|
'linear_attention': lfm2_linear_attention,
|
|
}
|
|
self.regist('lfm2', lfm2_map)
|
|
|
|
def regist_lfm2_moe(self):
|
|
lfm2_moe_config = {
|
|
'hidden_size': 'hidden_size',
|
|
'num_attention_heads': 'num_attention_heads',
|
|
'num_hidden_layers': 'num_hidden_layers',
|
|
'num_key_value_heads': 'num_key_value_heads',
|
|
'rope_theta': 'rope_theta',
|
|
'rope_parameters': 'rope_parameters',
|
|
'max_position_embeddings': 'max_position_embeddings',
|
|
'layer_types': 'layer_types',
|
|
'conv_L_cache': 'conv_L_cache',
|
|
}
|
|
lfm2_moe_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.embed_tokens',
|
|
'blocks': 'model.layers',
|
|
'final_layernorm': 'model.embedding_norm',
|
|
}
|
|
lfm2_moe_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'linear_attn': 'conv',
|
|
'mlp': 'feed_forward',
|
|
'input_layernorm': 'operator_norm',
|
|
'post_attention_layernorm': 'ffn_norm',
|
|
}
|
|
lfm2_moe_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'out_proj',
|
|
'q_norm': 'q_layernorm',
|
|
'k_norm': 'k_layernorm',
|
|
}
|
|
lfm2_moe_linear_attention = {
|
|
'in_proj': 'in_proj',
|
|
'conv': 'conv',
|
|
'out_proj': 'out_proj',
|
|
}
|
|
lfm2_moe_mlp = {
|
|
'num_experts': 'experts.num_experts',
|
|
'top_k': 'top_k',
|
|
'norm_topk_prob': 'norm_topk_prob',
|
|
'gate': 'gate',
|
|
'experts': 'experts',
|
|
'expert_bias': 'expert_bias',
|
|
'routed_scaling_factor': 'routed_scaling_factor',
|
|
}
|
|
lfm2_moe_map = {
|
|
'config': lfm2_moe_config,
|
|
'model': lfm2_moe_model,
|
|
'decoder': lfm2_moe_decoder,
|
|
'attention': lfm2_moe_attention,
|
|
'linear_attention': lfm2_moe_linear_attention,
|
|
'mlp': lfm2_moe_mlp,
|
|
}
|
|
self.regist('lfm2_moe', lfm2_moe_map)
|
|
|
|
def regist_lfm2_vl(self):
|
|
lfm2_vl_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_theta': 'text_config.rope_theta',
|
|
'rope_parameters': 'text_config.rope_parameters',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings',
|
|
'layer_types': 'text_config.layer_types',
|
|
'conv_L_cache': 'text_config.conv_L_cache',
|
|
}
|
|
lfm2_vl_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.language_model.embed_tokens',
|
|
'blocks': 'model.language_model.layers',
|
|
'final_layernorm': 'model.language_model.embedding_norm',
|
|
'visual': 'model.vision_tower',
|
|
'multi_modal_projector': 'model.multi_modal_projector',
|
|
}
|
|
lfm2_vl_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'linear_attn': 'conv',
|
|
'mlp': 'feed_forward',
|
|
'input_layernorm': 'operator_norm',
|
|
'post_attention_layernorm': 'ffn_norm',
|
|
}
|
|
lfm2_vl_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'out_proj',
|
|
'q_norm': 'q_layernorm',
|
|
'k_norm': 'k_layernorm',
|
|
}
|
|
lfm2_vl_linear_attention = {
|
|
'in_proj': 'in_proj',
|
|
'conv': 'conv',
|
|
'out_proj': 'out_proj',
|
|
}
|
|
lfm2_vl_map = {
|
|
'config': lfm2_vl_config,
|
|
'model': lfm2_vl_model,
|
|
'decoder': lfm2_vl_decoder,
|
|
'attention': lfm2_vl_attention,
|
|
'linear_attention': lfm2_vl_linear_attention,
|
|
}
|
|
self.regist('lfm2_vl', lfm2_vl_map)
|
|
|
|
def regist_lfm2_audio(self):
|
|
# Config fields come directly from the nested 'lfm' config (no prefix needed,
|
|
# because LlmConfig.from_pretrained extracts the nested config for lfm2_audio)
|
|
lfm2_audio_config = {
|
|
'hidden_size': 'hidden_size',
|
|
'num_attention_heads': 'num_attention_heads',
|
|
'num_hidden_layers': 'num_hidden_layers',
|
|
'num_key_value_heads': 'num_key_value_heads',
|
|
'rope_theta': 'rope_theta',
|
|
'rope_parameters': 'rope_parameters',
|
|
'max_position_embeddings': 'max_position_embeddings',
|
|
'layer_types': 'layer_types',
|
|
'conv_L_cache': 'conv_L_cache',
|
|
}
|
|
# Weight paths use 'lfm.' prefix (the LFM backbone is under model.lfm)
|
|
lfm2_audio_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'lfm.embed_tokens',
|
|
'blocks': 'lfm.layers',
|
|
'final_layernorm': 'lfm.embedding_norm',
|
|
'audio': 'conformer',
|
|
'audio_adapter': 'audio_adapter',
|
|
}
|
|
lfm2_audio_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'linear_attn': 'conv',
|
|
'mlp': 'feed_forward',
|
|
'input_layernorm': 'operator_norm',
|
|
'post_attention_layernorm': 'ffn_norm',
|
|
}
|
|
lfm2_audio_attention = {
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'out_proj',
|
|
'q_norm': 'q_layernorm',
|
|
'k_norm': 'k_layernorm',
|
|
}
|
|
lfm2_audio_linear_attention = {
|
|
'in_proj': 'in_proj',
|
|
'conv': 'conv',
|
|
'out_proj': 'out_proj',
|
|
}
|
|
lfm2_audio_map = {
|
|
'config': lfm2_audio_config,
|
|
'model': lfm2_audio_model,
|
|
'decoder': lfm2_audio_decoder,
|
|
'attention': lfm2_audio_attention,
|
|
'linear_attention': lfm2_audio_linear_attention,
|
|
}
|
|
self.regist('lfm2_audio', lfm2_audio_map)
|
|
|
|
def regist_qwen3_5(self):
|
|
qwen3_5_config = {
|
|
'hidden_size': 'text_config.hidden_size',
|
|
'head_dim': 'text_config.head_dim',
|
|
'num_attention_heads': 'text_config.num_attention_heads',
|
|
'num_hidden_layers': 'text_config.num_hidden_layers',
|
|
'num_key_value_heads': 'text_config.num_key_value_heads',
|
|
'rope_parameters': 'text_config.rope_parameters',
|
|
'max_position_embeddings': 'text_config.max_position_embeddings',
|
|
'layer_types': 'text_config.layer_types',
|
|
'sliding_window': 'text_config.full_attention_interval',
|
|
'rms_norm_eps': 'text_config.rms_norm_eps',
|
|
'linear_conv_kernel_dim': 'text_config.linear_conv_kernel_dim',
|
|
'linear_key_head_dim': 'text_config.linear_key_head_dim',
|
|
'linear_num_key_heads': 'text_config.linear_num_key_heads',
|
|
'linear_num_value_heads': 'text_config.linear_num_value_heads',
|
|
'linear_value_head_dim': 'text_config.linear_value_head_dim'
|
|
}
|
|
qwen3_5_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.language_model.embed_tokens',
|
|
'blocks': 'model.language_model.layers',
|
|
'final_layernorm': 'model.language_model.norm',
|
|
'visual': 'model.visual'
|
|
}
|
|
qwen3_5_linear_attention = {
|
|
'in_proj_qkv': 'in_proj_qkv',
|
|
'in_proj_z': 'in_proj_z',
|
|
'in_proj_b': 'in_proj_b',
|
|
'in_proj_a': 'in_proj_a',
|
|
'out_proj': 'out_proj',
|
|
'conv1d': 'conv1d',
|
|
'norm': 'norm',
|
|
'act': 'act',
|
|
'dt_bias': 'dt_bias',
|
|
'A_log': 'A_log'
|
|
}
|
|
qwen3_5_map = {
|
|
'config': qwen3_5_config,
|
|
'model': qwen3_5_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention,
|
|
'linear_attention': qwen3_5_linear_attention
|
|
}
|
|
self.regist('qwen3_5', qwen3_5_map)
|
|
qwen3_5_moe_mlp = {
|
|
'num_experts': 'experts.num_experts',
|
|
'top_k': 'gate.top_k',
|
|
'gate': 'gate',
|
|
'experts': 'experts',
|
|
'shared_expert_gate': 'shared_expert_gate',
|
|
'shared_expert': 'shared_expert'
|
|
}
|
|
qwen3_5_moe_map = copy.deepcopy(qwen3_5_map)
|
|
qwen3_5_moe_map['mlp'] = qwen3_5_moe_mlp
|
|
self.regist('qwen3_5_moe', qwen3_5_moe_map)
|
|
|
|
def init_default_map(self):
|
|
# default map is `LlamaForCausalLM`
|
|
self.config_key = 'config'
|
|
self.model_key = 'model'
|
|
self.decoder_key = 'decoder'
|
|
self.attention_key = 'attention'
|
|
self.default_config = {
|
|
'hidden_size': 'hidden_size',
|
|
'head_dim': 'head_dim',
|
|
'num_attention_heads': 'num_attention_heads',
|
|
'num_hidden_layers': 'num_hidden_layers',
|
|
'num_key_value_heads': 'num_key_value_heads',
|
|
'rope_theta': 'rope_theta',
|
|
'rope_scaling': 'rope_scaling',
|
|
'max_position_embeddings': 'max_position_embeddings'
|
|
}
|
|
self.default_model = {
|
|
'lm': 'lm_head',
|
|
'embed': 'model.embed_tokens',
|
|
'blocks': 'model.layers',
|
|
'final_layernorm': 'model.norm',
|
|
'visual': 'visual'
|
|
}
|
|
self.default_decoder = {
|
|
'self_attn': 'self_attn',
|
|
'linear_attn': 'linear_attn',
|
|
'mlp': 'mlp',
|
|
'input_layernorm': 'input_layernorm',
|
|
'post_attention_layernorm': 'post_attention_layernorm'
|
|
}
|
|
self.default_attention = {
|
|
'qkv_proj': 'qkv_proj',
|
|
'q_proj': 'q_proj',
|
|
'k_proj': 'k_proj',
|
|
'v_proj': 'v_proj',
|
|
'o_proj': 'o_proj',
|
|
'q_norm': 'q_norm',
|
|
'k_norm': 'k_norm'
|
|
}
|
|
self.default_map = {
|
|
'config': self.default_config,
|
|
'model': self.default_model,
|
|
'decoder': self.default_decoder,
|
|
'attention': self.default_attention
|
|
}
|
|
|
|
@staticmethod
|
|
def do_map(dst, src, mapping):
|
|
# Sort mapping by key to ensure parents are set before children
|
|
# e.g., 'visual' is processed before 'visual.connector' for SmolVLM
|
|
for dst_path, src_path in sorted(mapping.items(), key=lambda x: x[0]):
|
|
# --- 1. Retrieve value from source ---
|
|
val = src
|
|
for attr in src_path.split('.'):
|
|
if hasattr(val, attr):
|
|
val = getattr(val, attr)
|
|
else:
|
|
val = None
|
|
break
|
|
|
|
# --- 2. Navigate to destination parent node ---
|
|
dst_parts = dst_path.split('.')
|
|
target = dst
|
|
|
|
# Traverse to the second-to-last object
|
|
path_valid = True
|
|
for attr in dst_parts[:-1]:
|
|
if hasattr(target, attr):
|
|
target = getattr(target, attr)
|
|
if target is None:
|
|
path_valid = False
|
|
break
|
|
else:
|
|
path_valid = False
|
|
break
|
|
|
|
# --- 3. Set value ---
|
|
if path_valid and target:
|
|
setattr(target, dst_parts[-1], val)
|