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model : add hunyuan dense (#14878)
* support hunyuan_v1_dense Signed-off-by: stevenkuang <stevenkuang@tencent.com> * update hunyuan_moe to hunyuan_v1_moe Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix rope alpha assert and bos token Signed-off-by: stevenkuang <stevenkuang@tencent.com> * add blank line Signed-off-by: stevenkuang <stevenkuang@tencent.com> * Revert "update hunyuan_moe to hunyuan_v1_moe" This reverts commit aa973ca21913aba77f6e81a935270ef7be222e75. * use hunyuan_dense instead of hunyuan_v1_dense Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix hunyuan_moe chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> * remove leftover code Signed-off-by: stevenkuang <stevenkuang@tencent.com> * update hunyuan dense chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix hunyuan dense vocab and chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> --------- Signed-off-by: stevenkuang <stevenkuang@tencent.com>
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stevenkuang
GitHub
parent
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commit
0f5ccd6fd1
10 changed files with 351 additions and 9 deletions
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@ -684,6 +684,9 @@ class TextModel(ModelBase):
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if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
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# ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
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res = "hunyuan"
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if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
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# ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
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res = "hunyuan-dense"
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if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
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# ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
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res = "falcon-h1"
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@ -7553,11 +7556,6 @@ class FalconH1Model(Mamba2Model):
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class HunYuanMoEModel(TextModel):
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model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# For handling tied embeddings
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self._tok_embd = None
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def set_vocab(self):
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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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@ -7651,9 +7649,6 @@ class HunYuanMoEModel(TextModel):
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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if name == "model.embed_tokens.weight":
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self._tok_embd = data_torch.clone()
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if name == "lm_head.weight":
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if self.hparams.get("tie_word_embeddings", False):
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logger.info("Skipping tied output layer 'lm_head.weight'")
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@ -7698,6 +7693,98 @@ class HunYuanMoEModel(TextModel):
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register("HunYuanDenseV1ForCausalLM")
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class HunYuanModel(TextModel):
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model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
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def set_vocab(self):
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if (self.dir_model / "tokenizer.json").is_file():
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self._set_vocab_gpt2()
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else:
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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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# 1. Get the pre-tokenizer identifier hash
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tokpre = self.get_vocab_base_pre(tokenizer)
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# 2. Reverse-engineer the merges list from mergeable_ranks
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.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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# 3. Generate the tokens and toktypes lists
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vocab_size = self.hparams["vocab_size"]
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assert tokenizer.vocab_size == 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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# 4. Write all vocab-related fields to the GGUF writer
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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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# 5. Add special tokens and chat templates
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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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# FIX for BOS token: Overwrite incorrect id read from config.json
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if self.hparams['hidden_size'] == 4096:
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self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
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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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# Rope
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rope_scaling = hparams.get("rope_scaling", {})
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if rope_scaling.get("type") == "dynamic":
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# HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
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# 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
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alpha = rope_scaling.get("alpha", 50)
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base = hparams.get("rope_theta", 10000.0)
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dim = hparams["head_dim"]
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scaled_base = base * (alpha ** (dim / (dim - 2)))
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self.gguf_writer.add_rope_freq_base(scaled_base)
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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self.gguf_writer.add_rope_scaling_factor(1)
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# There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
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self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
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self.gguf_writer.add_context_length(256 * 1024) # 256k context length
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# if any of our assumptions about the values are wrong, something has changed and this may need to be updated
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assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
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"HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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if name == "lm_head.weight":
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if self.hparams.get("tie_word_embeddings", False):
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logger.info("Skipping tied output layer 'lm_head.weight'")
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return []
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return [(self.map_tensor_name(name), data_torch)]
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@ModelBase.register("SmolLM3ForCausalLM")
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class SmolLM3Model(LlamaModel):
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model_arch = gguf.MODEL_ARCH.SMOLLM3
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@ -140,6 +140,7 @@ pre_computed_hashes = [
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{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
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{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
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{"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
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{"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
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# falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
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{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
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{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
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@ -376,6 +376,7 @@ class MODEL_ARCH(IntEnum):
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ERNIE4_5 = auto()
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ERNIE4_5_MOE = auto()
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HUNYUAN_MOE = auto()
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HUNYUAN_DENSE = auto()
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SMOLLM3 = auto()
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LFM2 = auto()
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DREAM = auto()
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@ -697,6 +698,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.ERNIE4_5_MOE: "ernie4_5-moe",
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MODEL_ARCH.FALCON_H1: "falcon-h1",
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MODEL_ARCH.HUNYUAN_MOE: "hunyuan-moe",
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MODEL_ARCH.HUNYUAN_DENSE: "hunyuan-dense",
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MODEL_ARCH.SMOLLM3: "smollm3",
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MODEL_ARCH.LFM2: "lfm2",
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MODEL_ARCH.DREAM: "dream",
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@ -2471,6 +2473,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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],
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MODEL_ARCH.HUNYUAN_DENSE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.SMOLLM3: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -85,6 +85,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
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{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
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{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
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{ LLM_ARCH_HUNYUAN_DENSE, "hunyuan-dense" },
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{ LLM_ARCH_SMOLLM3, "smollm3" },
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{ LLM_ARCH_LFM2, "lfm2" },
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{ LLM_ARCH_DREAM, "dream" },
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@ -1897,6 +1898,26 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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},
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},
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{
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LLM_ARCH_HUNYUAN_DENSE,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_SMOLLM3,
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{
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@ -89,6 +89,7 @@ enum llm_arch {
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LLM_ARCH_ERNIE4_5,
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LLM_ARCH_ERNIE4_5_MOE,
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LLM_ARCH_HUNYUAN_MOE,
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LLM_ARCH_HUNYUAN_DENSE,
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LLM_ARCH_SMOLLM3,
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LLM_ARCH_LFM2,
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LLM_ARCH_DREAM,
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@ -66,6 +66,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
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{ "llama4", LLM_CHAT_TEMPLATE_LLAMA4 },
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{ "smolvlm", LLM_CHAT_TEMPLATE_SMOLVLM },
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{ "hunyuan-moe", LLM_CHAT_TEMPLATE_HUNYUAN_MOE },
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{ "hunyuan-dense", LLM_CHAT_TEMPLATE_HUNYUAN_DENSE },
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{ "kimi-k2", LLM_CHAT_TEMPLATE_KIMI_K2 },
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};
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@ -193,6 +194,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
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return LLM_CHAT_TEMPLATE_DOTS1;
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} else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
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return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
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} else if (tmpl_contains("<｜hy_place▁holder▁no▁2｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
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return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
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} else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
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return LLM_CHAT_TEMPLATE_KIMI_K2;
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}
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@ -698,11 +701,27 @@ int32_t llm_chat_apply_template(
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if (role == "system") {
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ss << "<|startoftext|>" << message->content << "<|extra_4|>";
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} else if (role == "assistant") {
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ss << "<|startoftext|>" << message->content << "<|eos|>";
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ss << message->content << "<|eos|>";
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} else {
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ss << "<|startoftext|>" << message->content << "<|extra_0|>";
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}
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}
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} else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_DENSE) {
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// tencent/Hunyuan-4B-Instruct
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for (size_t i = 0; i < chat.size(); i++) {
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std::string role(chat[i]->role);
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if (i == 0) {
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if (role == "system") {
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ss << chat[i]->content << "<｜hy_place▁holder▁no▁3｜>";
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}
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}
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if (role == "assistant") {
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ss << "<｜hy_Assistant｜>" << chat[i]->content << "<｜hy_place▁holder▁no▁2｜>";
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} else if (role == "user") {
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ss << "<｜hy_User｜>" << chat[i]->content << "<｜hy_Assistant｜>";
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}
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}
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} else if (tmpl == LLM_CHAT_TEMPLATE_KIMI_K2) {
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// moonshotai/Kimi-K2-Instruct
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for (auto message : chat) {
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@ -46,6 +46,7 @@ enum llm_chat_template {
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LLM_CHAT_TEMPLATE_SMOLVLM,
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LLM_CHAT_TEMPLATE_DOTS1,
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LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
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LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
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LLM_CHAT_TEMPLATE_KIMI_K2,
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LLM_CHAT_TEMPLATE_UNKNOWN,
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};
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@ -1760,6 +1760,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_HUNYUAN_DENSE:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_embd) {
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case 1024: type = LLM_TYPE_0_5B; break;
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case 2048: type = LLM_TYPE_1_8B; break;
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case 3072: type = LLM_TYPE_4B; break;
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case 4096: type = LLM_TYPE_7B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_SMOLLM3:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -5195,6 +5207,39 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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|||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
||||
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_SMOLLM3:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
|
@ -16930,6 +16975,144 @@ struct llm_build_hunyuan_moe : public llm_graph_context {
|
|||
}
|
||||
};
|
||||
|
||||
struct llm_build_hunyuan_dense : public llm_graph_context {
|
||||
llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn = build_attn_inp_kv_unified();
|
||||
|
||||
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = build_norm(inpL,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
// self-attention
|
||||
{
|
||||
// rope freq factors for llama3; may return nullptr for llama2 and other models
|
||||
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
|
||||
|
||||
// compute Q and K and RoPE them
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
if (model.layers[il].bq) {
|
||||
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
||||
cb(Qcur, "Qcur", il);
|
||||
}
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
if (model.layers[il].bk) {
|
||||
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
||||
cb(Kcur, "Kcur", il);
|
||||
}
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
if (model.layers[il].bv) {
|
||||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
Kcur = build_norm(Kcur,
|
||||
model.layers[il].attn_k_norm, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(Kcur, "Kcur_norm", il);
|
||||
|
||||
Qcur = build_norm(Qcur,
|
||||
model.layers[il].attn_q_norm, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_norm", il);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
// feed-forward network (non-MoE)
|
||||
ggml_tensor * cur_mlp = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
model.layers[il].ffn_gate, NULL, NULL,
|
||||
model.layers[il].ffn_down, NULL, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur_mlp, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur_mlp, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
// lm_head
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_smollm3 : public llm_graph_context {
|
||||
llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
|
|
@ -17797,6 +17980,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_hunyuan_dense>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_SMOLLM3:
|
||||
{
|
||||
llm = std::make_unique<llm_build_smollm3>(*this, params);
|
||||
|
|
@ -18016,6 +18203,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_MINICPM3:
|
||||
case LLM_ARCH_DOTS1:
|
||||
case LLM_ARCH_HUNYUAN_MOE:
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
case LLM_ARCH_LFM2:
|
||||
case LLM_ARCH_SMALLTHINKER:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
|
|
|||
|
|
@ -307,6 +307,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
|
|||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
|
||||
case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
|
||||
regex_exprs = {
|
||||
"\\p{N}{1,3}",
|
||||
"[一-龥-ゟ゠-ヿ]+",
|
||||
|
|
@ -1964,6 +1965,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
tokenizer_pre == "hunyuan") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "hunyuan-dense") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "kimi-k2") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_KIMI_K2;
|
||||
|
|
|
|||
|
|
@ -46,6 +46,7 @@ enum llama_vocab_pre_type {
|
|||
LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
|
||||
LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36,
|
||||
LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37,
|
||||
LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
|
||||
};
|
||||
|
||||
struct LLM_KV;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue