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llama : Support intern-s1 (#14875)
* support internvl * support interns1 * resolve comments * put interns1 in tensor mapping * resolve comment * move tokenizer changes to sub class
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2 changed files with 122 additions and 2 deletions
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@ -3328,7 +3328,13 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
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@ModelBase.register("InternVisionModel")
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class InternVisionModel(MmprojModel):
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def set_gguf_parameters(self):
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assert self.hparams_vision is not None
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if isinstance(self.hparams_vision['image_size'], list):
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self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
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if isinstance(self.hparams_vision['patch_size'], list):
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self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
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super().set_gguf_parameters()
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hparams = self.hparams
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
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self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
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@ -3352,14 +3358,30 @@ class InternVisionModel(MmprojModel):
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return gguf.GGMLQuantizationType.F32
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return False
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def _mapping_interns1_name(self, name):
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names_map = {
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"model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
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"model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
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"model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
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"model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
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"model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
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"model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
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}
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if name in names_map:
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name = names_map[name]
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return name
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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if name.startswith("vision_model") or name.startswith("mlp"):
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vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
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# deal with intern-s1 special case
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name = self._mapping_interns1_name(name)
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if any([name.startswith(prefix) for prefix in vision_prefix]):
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# process visual tensors
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# correct name
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if name.startswith("vision_model"):
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name = "vision_tower." + name
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if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
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if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
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name += ".weight"
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# split QKV tensors if needed
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if ".qkv." in name:
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@ -3445,6 +3467,10 @@ class Qwen2MoeModel(TextModel):
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# process the experts separately
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name = name.replace("language_model.", "") # InternVL
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if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
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# skip visual tensors
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return []
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if name.find("experts") != -1:
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n_experts = self.hparams["num_experts"]
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assert bid is not None
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@ -3498,6 +3524,85 @@ class Qwen3Model(Qwen2Model):
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class Qwen3MoeModel(Qwen2MoeModel):
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model_arch = gguf.MODEL_ARCH.QWEN3MOE
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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hparams = ModelBase.load_hparams(self.dir_model)
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self.origin_hf_arch = hparams.get('architectures', [None])[0]
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def set_vocab(self):
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# deal with intern-s1
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if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
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self._set_vocab_interns1()
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return
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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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def _set_vocab_interns1(self):
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tokens: list[str] = []
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toktypes: list[int] = []
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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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vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
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vocab_size = self.hparams.get("vocab_size", len(vocab))
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assert max(vocab.values()) < vocab_size
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tokpre = self.get_vocab_base_pre(tokenizer)
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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added_tokens_decoder = tokenizer.added_tokens_decoder
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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: str = reverse_vocab[i]
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if token in added_vocab:
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# The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
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# To avoid unexpected issues - we make sure to normalize non-normalized tokens
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if not added_tokens_decoder[i].normalized:
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previous_token = token
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token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
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if previous_token != token:
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logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
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if added_tokens_decoder[i].special or self.does_token_look_special(token):
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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toktypes.append(gguf.TokenType.NORMAL)
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tokens.append(token)
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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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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
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special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
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additional_special_tokens = []
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if special_tokens_map_file.is_file():
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with open(special_tokens_map_file, encoding = 'utf-8') as f:
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additional_special_tokens = json.load(f).get('additional_special_tokens', [])
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tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
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if tokenizer_cfg_file.is_file():
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with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
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added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
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token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
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for token in additional_special_tokens:
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if token in token2ids_map:
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special_vocab._set_special_token(token, token2ids_map[token])
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special_vocab._set_special_token('eos', 151645)
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special_vocab._set_special_token("bos", 151643)
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special_vocab.add_to_gguf(self.gguf_writer)
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@ModelBase.register("GPT2LMHeadModel")
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class GPT2Model(TextModel):
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@ -1110,11 +1110,13 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_EMBD_CLS: (
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"vision_tower.vision_model.embeddings.class_embedding",
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"model.vision_tower.embeddings.cls_token", # Intern-S1
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"vision_model.class_embedding", # llama 4
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),
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MODEL_TENSOR.V_ENC_EMBD_PATCH: (
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"vision_tower.vision_model.embeddings.patch_embedding",
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"model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
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"vpm.embeddings.patch_embedding",
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"model.vision_model.embeddings.patch_embedding", # SmolVLM
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"vision_tower.patch_conv", # pixtral
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@ -1124,6 +1126,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_EMBD_POS: (
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"vision_tower.vision_model.embeddings.position_embedding",
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"model.vision_tower.embeddings.position_embeddings", # Intern-S1
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"vpm.embeddings.position_embedding",
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"model.vision_model.embeddings.position_embedding", # SmolVLM
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"vision_model.positional_embedding_vlm", # llama 4
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@ -1131,6 +1134,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_ATTN_Q: (
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"vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
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"model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
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"vpm.encoder.layers.{bid}.self_attn.q_proj",
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"model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
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"vision_model.model.layers.{bid}.self_attn.q_proj", # llama4
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@ -1140,10 +1144,12 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
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"vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL
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"model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1
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),
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MODEL_TENSOR.V_ENC_ATTN_K: (
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"vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
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"model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1
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"vpm.encoder.layers.{bid}.self_attn.k_proj",
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"model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
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"vision_model.model.layers.{bid}.self_attn.k_proj", # llama4
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@ -1153,10 +1159,12 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
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"vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL
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"model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1
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),
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MODEL_TENSOR.V_ENC_ATTN_V: (
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"vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
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"model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1
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"vpm.encoder.layers.{bid}.self_attn.v_proj",
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"model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
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"vision_model.model.layers.{bid}.self_attn.v_proj", # llama4
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@ -1167,6 +1175,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_INPUT_NORM: (
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"vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
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"vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
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"model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
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"vpm.encoder.layers.{bid}.layer_norm1",
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"model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
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"vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
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@ -1177,6 +1186,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_ATTN_O: (
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"vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
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"vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
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"model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1
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"vpm.encoder.layers.{bid}.self_attn.out_proj",
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"model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
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"vision_model.model.layers.{bid}.self_attn.o_proj", # llama4
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@ -1187,6 +1197,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
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"vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
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"vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
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"model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1
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"vpm.encoder.layers.{bid}.layer_norm2",
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"model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
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"vision_model.model.layers.{bid}.post_attention_layernorm", # llama4
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@ -1196,6 +1207,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_FFN_UP: (
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"vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
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"model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
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"vpm.encoder.layers.{bid}.mlp.fc1",
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"model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
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"vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral
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@ -1211,6 +1223,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_ENC_FFN_DOWN: (
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"vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
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"model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
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"vpm.encoder.layers.{bid}.mlp.fc2",
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"model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
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"vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral
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@ -1221,10 +1234,12 @@ class TensorNameMap:
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MODEL_TENSOR.V_LAYER_SCALE_1: (
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"vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL
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"model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1
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),
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MODEL_TENSOR.V_LAYER_SCALE_2: (
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"vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL
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"model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1
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),
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MODEL_TENSOR.V_PRE_NORM: (
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