@@ -664,6 +664,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
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if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65" :
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# ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
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res = "roberta-bpe"
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+ if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb" :
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+ # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
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+ res = "gigachat"
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if res is None :
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logger .warning ("\n " )
@@ -3427,6 +3430,97 @@ def prepare_tensors(self):
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raise ValueError (f"Unprocessed experts: { experts } " )
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+ @Model .register ("DeepseekForCausalLM" )
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+ class DeepseekModel (Model ):
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+ model_arch = gguf .MODEL_ARCH .DEEPSEEK
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+
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+ def set_vocab (self ):
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+ try :
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+ self ._set_vocab_sentencepiece ()
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+ except FileNotFoundError :
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+ self ._set_vocab_gpt2 ()
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+
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+ def set_gguf_parameters (self ):
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+ super ().set_gguf_parameters ()
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+ hparams = self .hparams
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+ if "head_dim" in hparams :
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+ rope_dim = hparams ["head_dim" ]
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+ else :
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+ rope_dim = hparams ["hidden_size" ] // hparams ["num_attention_heads" ]
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+
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+ self .gguf_writer .add_rope_dimension_count (rope_dim )
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+ self .gguf_writer .add_rope_scaling_type (gguf .RopeScalingType .NONE )
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+ self .gguf_writer .add_leading_dense_block_count (hparams ["first_k_dense_replace" ])
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+ self .gguf_writer .add_vocab_size (hparams ["vocab_size" ])
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+ self .gguf_writer .add_expert_feed_forward_length (hparams ["moe_intermediate_size" ])
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+ self .gguf_writer .add_expert_weights_scale (1.0 )
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+ self .gguf_writer .add_expert_count (hparams ["n_routed_experts" ])
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+ self .gguf_writer .add_expert_shared_count (hparams ["n_shared_experts" ])
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+
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+ _experts : list [dict [str , Tensor ]] | None = None
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+
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+ @staticmethod
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+ def permute (weights : Tensor , n_head : int , n_head_kv : int | None ):
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+ if n_head_kv is not None and n_head != n_head_kv :
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+ n_head = n_head_kv
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+ return (weights .reshape (n_head , 2 , weights .shape [0 ] // n_head // 2 , * weights .shape [1 :])
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+ .swapaxes (1 , 2 )
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+ .reshape (weights .shape ))
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+
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+ def modify_tensors (self , data_torch : Tensor , name : str , bid : int | None ) -> Iterable [tuple [str , Tensor ]]:
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+ n_head = self .hparams ["num_attention_heads" ]
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+ n_kv_head = self .hparams .get ("num_key_value_heads" )
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+
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+ if name .endswith (("q_proj.weight" , "q_proj.bias" )):
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+ data_torch = DeepseekModel .permute (data_torch , n_head , n_head )
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+ if name .endswith (("k_proj.weight" , "k_proj.bias" )):
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+ data_torch = DeepseekModel .permute (data_torch , n_head , n_kv_head )
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+
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+ # process the experts separately
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+ if name .find ("mlp.experts" ) != - 1 :
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+ n_experts = self .hparams ["n_routed_experts" ]
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+ assert bid is not None
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+
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+ if self ._experts is None :
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+ self ._experts = [{} for _ in range (self .block_count )]
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+
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+ self ._experts [bid ][name ] = data_torch
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+
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+ if len (self ._experts [bid ]) >= n_experts * 3 :
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+ tensors : list [tuple [str , Tensor ]] = []
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+
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+ # merge the experts into a single 3d tensor
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+ for w_name in ["down_proj" , "gate_proj" , "up_proj" ]:
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+ datas : list [Tensor ] = []
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+
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+ for xid in range (n_experts ):
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+ ename = f"model.layers.{ bid } .mlp.experts.{ xid } .{ w_name } .weight"
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+ datas .append (self ._experts [bid ][ename ])
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+ del self ._experts [bid ][ename ]
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+
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+ data_torch = torch .stack (datas , dim = 0 )
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+
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+ merged_name = f"model.layers.{ bid } .mlp.experts.{ w_name } .weight"
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+
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+ new_name = self .map_tensor_name (merged_name )
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+
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+ tensors .append ((new_name , data_torch ))
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+ return tensors
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+ else :
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+ return []
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+
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+ return [(self .map_tensor_name (name ), data_torch )]
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+
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+ def prepare_tensors (self ):
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+ super ().prepare_tensors ()
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+
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+ if self ._experts is not None :
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+ # flatten `list[dict[str, Tensor]]` into `list[str]`
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+ experts = [k for d in self ._experts for k in d .keys ()]
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+ if len (experts ) > 0 :
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+ raise ValueError (f"Unprocessed experts: { experts } " )
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+
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+
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@Model .register ("DeepseekV2ForCausalLM" )
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class DeepseekV2Model (Model ):
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model_arch = gguf .MODEL_ARCH .DEEPSEEK2
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