from __future__ import annotations import re from typing import Any, Callable, Iterable, TYPE_CHECKING import torch if TYPE_CHECKING: from torch import Tensor from .base import MmprojModel, ModelBase, TextModel, gguf, logger from .qwen import QwenModel @ModelBase.register("DeepseekOCRForCausalLM") class DeepseekOCRVisionModel(MmprojModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_clip_projector_type(self.clip_projector_type) # default values below are taken from HF tranformers code self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6)) self.gguf_writer.add_vision_use_gelu(True) # calculate proj_scale_factor (used by tinygemma3 test model) image_seq_length = self.preprocessor_config.get("image_seq_length", 256) n_per_side = int(image_seq_length ** 0.5) image_size = self.hparams["image_size"] patch_size = self.hparams["patch_size"] proj_scale_factor = (image_size // patch_size) // n_per_side if proj_scale_factor > 0 and proj_scale_factor != 4: # we only need to write this if it's not the default value # in this case, we are converting a test model self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor) # @bluebread: there's no window_size in config but just add it here anyway self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14)) # SAM configuration sam_hparams = hparams['sam'] self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers']) self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width']) self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads']) def get_vision_config(self) -> dict[str, Any]: vision_config: dict[str, Any] | None = self.global_config.get("vision_config") if not vision_config: raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found") vision_config['sam'] = vision_config['width']['sam_vit_b'] if vision_config['width'].get('clip-l-14-224') is not None: vision_config.update(vision_config['width']['clip-l-14-224']) if isinstance(vision_config['width'], int): vision_config['hidden_size'] = vision_config['width'] if vision_config.get('heads') is not None: vision_config['num_heads'] = vision_config['heads'] vision_config['intermediate_size'] = vision_config['heads'] * 4 return vision_config def tensor_force_quant(self, name, new_name, bid, n_dims): for nq_name in ('.embeddings.', 'pos_embed', '.rel_pos_h', '.rel_pos_w', '.neck.', '.net_'): if nq_name in name: return gguf.GGMLQuantizationType.F32 return super().tensor_force_quant(name, new_name, bid, n_dims) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if name.endswith("view_seperator"): data_torch = data_torch.unsqueeze(0) yield from super().modify_tensors(data_torch, name, bid) @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: name, gen = item # Only process vision-related tensors, skip language model tensors # Vision components: sam_model, vision_model, projector, image_newline, view_seperator # Language model components to skip: lm_head, embed_tokens, layers, norm if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")): return None if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"): name += ".weight" return super().filter_tensors((name, gen)) @ModelBase.register("DeepseekOCR2ForCausalLM") class DeepseekOCR2VisionModel(DeepseekOCRVisionModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR2 def set_gguf_parameters(self): # the vision tower's qwen2 encoder is built from fixed defaults, # see build_qwen2_decoder_as_encoder() in deepencoderv2.py if self.hparams.get("patch_size") is None: self.hparams["patch_size"] = 16 if self.hparams.get("intermediate_size") is None: self.hparams["intermediate_size"] = 4864 if self.hparams.get("num_attention_heads") is None: self.hparams["num_attention_heads"] = 14 super().set_gguf_parameters() # qwen2 encoder is GQA: 14 Q heads, 2 KV heads self.gguf_writer.add_vision_head_count_kv(2) def get_vision_config(self) -> dict[str, Any]: vision_config = super().get_vision_config() vision_config['hidden_size'] = vision_config['width']['qwen2-0-5b']['dim'] if vision_config.get('layers') is None: vision_config['layers'] = 24 return vision_config @ModelBase.register("DeepseekForCausalLM") class DeepseekModel(TextModel): model_arch = gguf.MODEL_ARCH.DEEPSEEK def set_vocab(self): try: self._set_vocab_sentencepiece() except FileNotFoundError: self._set_vocab_gpt2() def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams if (rope_dim := hparams.get("head_dim")) is None: rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) self.gguf_writer.add_expert_weights_scale(1.0) self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) _experts: list[dict[str, Tensor]] | None = None @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: n_head = n_head_kv return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) .swapaxes(1, 2) .reshape(weights.shape)) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = DeepseekModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head) # process the experts separately if name.find("mlp.experts") != -1: n_experts = self.hparams["n_routed_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: # merge the experts into a single 3d tensor for w_name in ["down_proj", "gate_proj", "up_proj"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" yield from super().modify_tensors(data_torch, merged_name, bid) return else: return yield from super().modify_tensors(data_torch, name, bid) def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @ModelBase.register( "DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM", "KimiVLForConditionalGeneration", "KimiK25ForConditionalGeneration", "YoutuForCausalLM", "YoutuVLForConditionalGeneration", ) class DeepseekV2Model(TextModel): model_arch = gguf.MODEL_ARCH.DEEPSEEK2 # TODO @ngxson : remove this when we support MTP for deepseek models skip_mtp = True merge_expert = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False) self.origin_hf_arch = hparams.get('architectures', [None])[0] # special handling for Deepseek OCR if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM"): self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch] self.gguf_writer.add_architecture() # default jinja template self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}") @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: name, _ = item # DeepSeek-OCR vision encoder (SAM + DeepSeek-OCR-2 qwen2 tower) if "sam_model" in name or "qwen2_model" in name: return None return super().filter_tensors(item) def set_vocab(self): try: self._set_vocab_gpt2() return except Exception: pass from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) tokpre = self.get_vocab_base_pre(tokenizer) if tokpre == "kimi-k2": # Build merges list using the approach similar to HunYuanMoE merges = [] vocab = {} mergeable_ranks = tokenizer.model._mergeable_ranks # ty: ignore[unresolved-attribute] for token, rank in mergeable_ranks.items(): vocab[QwenModel.token_bytes_to_string(token)] = rank if len(token) == 1: continue merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) if len(merged) == 2: merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) # Build token list vocab_size = self.hparams["vocab_size"] special_tokens = tokenizer.special_tokens # ty: ignore[unresolved-attribute] reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()} tokens: list[str] = [] toktypes: list[int] = [] for i in range(vocab_size): if i not in reverse_vocab: tokens.append(f"[PAD{i}]") toktypes.append(gguf.TokenType.UNUSED) else: token = reverse_vocab[i] tokens.append(token) if i in special_tokens.values(): toktypes.append(gguf.TokenType.CONTROL) else: toktypes.append(gguf.TokenType.NORMAL) self.gguf_writer.add_tokenizer_model("gpt2") self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) self.gguf_writer.add_token_merges(merges) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) special_vocab.add_to_gguf(self.gguf_writer) else: raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!") def set_gguf_parameters(self): is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR) if is_ocr: self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0) else: # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group) self.hparams["num_key_value_heads"] = 1 self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6) super().set_gguf_parameters() hparams = self.hparams # first_k_dense_replace: number of leading layers using dense FFN instead of MoE # For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers # For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers has_moe = hparams.get("n_routed_experts") is not None first_k_dense_replace = hparams.get("first_k_dense_replace") if first_k_dense_replace is None: # Default: if no MoE, all layers are dense; if MoE, none are dense first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0 self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace) kv_lora_rank = hparams.get("kv_lora_rank", 512) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA if not is_ocr: self.gguf_writer.add_kv_lora_rank(kv_lora_rank) self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"]) self.gguf_writer.add_value_length(kv_lora_rank) self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) self.gguf_writer.add_value_length_mla(hparams["v_head_dim"]) # MoE parameters (required by C++ code for DEEPSEEK2 arch) # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False) self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) if (n_routed_experts := hparams.get("n_routed_experts")) is not None: self.gguf_writer.add_expert_count(n_routed_experts) # expert_shared_count is required by C++ code, default to 0 for non-MoE models n_shared_experts = hparams.get("n_shared_experts", 0) self.gguf_writer.add_expert_shared_count(n_shared_experts) # When not set, C++ code will use scale_w = false to skip the no-op scaling if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None: self.gguf_writer.add_expert_weights_scale(routed_scaling_factor) if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob: self.gguf_writer.add_expert_weights_norm(norm_topk_prob) self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None: # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul # ref https://github.com/ggml-org/llama.cpp/pull/17945 self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all) _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # skip lm_head.weight if tie_word_embeddings is True if self.hparams.get("tie_word_embeddings", False): if name == "lm_head.weight" or name == "model.lm_head.weight": logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)") return # skip Multi-Token Prediction (MTP) layers if self.skip_mtp: block_count = self.hparams["num_hidden_layers"] match = re.match(r"model.layers.(\d+)", name) if match and int(match.group(1)) >= block_count: return # process the experts separately if self.merge_expert and name.find("mlp.experts") != -1: n_experts = self.hparams["n_routed_experts"] assert bid is not None if self._experts is None: self._experts = [{} for _ in range(self.block_count)] self._experts[bid][name] = data_torch if len(self._experts[bid]) >= n_experts * 3: # merge the experts into a single 3d tensor for w_name in ["down_proj", "gate_proj", "up_proj"]: datas: list[Tensor] = [] for xid in range(n_experts): ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" datas.append(self._experts[bid][ename]) del self._experts[bid][ename] data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" yield from super().modify_tensors(data_torch, merged_name, bid) return else: return # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed if name.endswith("kv_b_proj.weight"): name_kb = name.replace("kv_b_proj", "k_b_proj") name_vb = name.replace("kv_b_proj", "v_b_proj") n_head_kv = self.hparams["num_key_value_heads"] v_head_dim = self.hparams["v_head_dim"] qk_nope_head_dim = self.hparams["qk_nope_head_dim"] assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim) kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1]) k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1) k_b = k_b.transpose(1, 2) yield from super().modify_tensors(k_b, name_kb, bid) yield from super().modify_tensors(v_b, name_vb, bid) return yield from super().modify_tensors(data_torch, name, bid) def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: # flatten `list[dict[str, Tensor]]` into `list[str]` experts = [k for d in self._experts for k in d.keys()] if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts}") @ModelBase.register("DeepseekV32ForCausalLM") class DeepseekV32Model(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK32 skip_mtp = False def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) def set_vocab(self): from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(self.dir_model) assert getattr(tokenizer, "add_bos_token", False), "Change value of add_bos_token to true in tokenizer_config.json file." self._set_vocab_gpt2() def set_gguf_parameters(self): super().set_gguf_parameters() # NextN/MTP prediction layers if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) # DSA indexer parameters self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"]) self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"]) self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])