set n_embd_head_k/v to ensure kv cache works
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Yee Man Chan
parent
f85e5c73b9
commit
8bd617eb1c
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@ -4987,10 +4987,65 @@ class KimiLinearModel(TextModel):
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_experts: list[dict[str, Tensor]] | None = None
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def set_vocab(self):
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try:
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self._set_vocab_gpt2()
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return
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except Exception:
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pass
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
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tokpre = self.get_vocab_base_pre(tokenizer)
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if tokpre == "kimi-k2":
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# Build merges list using the approach similar to HunYuanMoE
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.model._mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[QwenModel.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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continue
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merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
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if len(merged) == 2:
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merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
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# Build token list
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vocab_size = self.hparams["vocab_size"]
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special_tokens = tokenizer.special_tokens
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
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tokens: list[str] = []
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toktypes: list[int] = []
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.UNUSED)
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else:
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token = reverse_vocab[i]
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tokens.append(token)
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if i in special_tokens.values():
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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self.gguf_writer.add_token_merges(merges)
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
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special_vocab.add_to_gguf(self.gguf_writer)
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# override eos id in config.json with tiktoken eos id
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self.gguf_writer.add_eos_token_id(tokenizer.eos_id)
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else:
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raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
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# Use find_hparam for context length
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@ -5043,8 +5098,9 @@ class KimiLinearModel(TextModel):
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# Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
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qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
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qk_rope_head_dim = self.hparams.get("qk_rope_head_dim")
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self.gguf_writer.add_key_length(qk_nope_head_dim + qk_rope_head_dim)
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v_head_dim = self.hparams.get("v_head_dim")
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self.gguf_writer.add_rope_dimension_count(self.hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length(v_head_dim)
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# Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
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if "n_embd_head_k_mla" in self.hparams:
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@ -5106,60 +5162,6 @@ class KimiLinearModel(TextModel):
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if routed_scaling_factor is not None:
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self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
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def set_vocab(self):
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try:
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self._set_vocab_gpt2()
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return
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except Exception:
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pass
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
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tokpre = self.get_vocab_base_pre(tokenizer)
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if tokpre == "kimi-k2":
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# Build merges list using the approach similar to HunYuanMoE
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.model._mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[QwenModel.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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continue
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merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
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if len(merged) == 2:
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merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
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# Build token list
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vocab_size = self.hparams["vocab_size"]
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special_tokens = tokenizer.special_tokens
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
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tokens: list[str] = []
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toktypes: list[int] = []
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.UNUSED)
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else:
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token = reverse_vocab[i]
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tokens.append(token)
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if i in special_tokens.values():
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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self.gguf_writer.add_token_merges(merges)
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
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special_vocab.add_to_gguf(self.gguf_writer)
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else:
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raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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