Refactor: convert_hf_to_gguf.py (#17114)
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Piotr Wilkin (ilintar)
co-authored by
Sigbjørn Skjæret
Sigbjørn Skjæret
parent
769cc93a43
commit
cc7200bf12
@@ -0,0 +1,388 @@
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from __future__ import annotations
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import re
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from typing import Any, Callable, Iterable, TYPE_CHECKING
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import MmprojModel, ModelBase, TextModel, gguf, logger
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from .qwen import QwenModel
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@ModelBase.register("DeepseekOCRForCausalLM")
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class DeepseekOCRVisionModel(MmprojModel):
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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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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
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# default values below are taken from HF tranformers code
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self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
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self.gguf_writer.add_vision_use_gelu(True)
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# calculate proj_scale_factor (used by tinygemma3 test model)
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image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
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n_per_side = int(image_seq_length ** 0.5)
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image_size = self.hparams["image_size"]
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patch_size = self.hparams["patch_size"]
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proj_scale_factor = (image_size // patch_size) // n_per_side
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if proj_scale_factor > 0 and proj_scale_factor != 4:
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# we only need to write this if it's not the default value
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# in this case, we are converting a test model
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self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
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# @bluebread: there's no window_size in config but just add it here anyway
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self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
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# SAM configuration
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sam_hparams = hparams['sam']
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self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
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self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
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self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])
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def get_vision_config(self) -> dict[str, Any]:
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vision_config: dict[str, Any] | None = self.global_config.get("vision_config")
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if not vision_config:
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raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")
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vision_config['sam'] = vision_config['width']['sam_vit_b']
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vision_config.update(vision_config['width']['clip-l-14-224'])
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vision_config['hidden_size'] = vision_config['width']
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vision_config['num_heads'] = vision_config['heads']
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vision_config['intermediate_size'] = vision_config['heads'] * 4
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return vision_config
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def tensor_force_quant(self, name, new_name, bid, n_dims):
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if ".embeddings." in name or 'pos_embed' in name:
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return gguf.GGMLQuantizationType.F32
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if ".rel_pos_h" in name or '.rel_pos_w' in name:
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return gguf.GGMLQuantizationType.F32
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if ".neck." in name or ".net_" in name:
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return gguf.GGMLQuantizationType.F32
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return super().tensor_force_quant(name, new_name, bid, n_dims)
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@classmethod
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def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
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name, gen = item
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# Only process vision-related tensors, skip language model tensors
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# Vision components: sam_model, vision_model, projector, image_newline, view_seperator
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# Language model components to skip: lm_head, embed_tokens, layers, norm
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if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
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return None
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if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
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name += ".weight"
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return super().filter_tensors((name, gen))
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@ModelBase.register("DeepseekForCausalLM")
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class DeepseekModel(TextModel):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK
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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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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 (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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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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_experts: list[dict[str, Tensor]] | None = None
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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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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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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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# 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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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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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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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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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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yield from super().modify_tensors(data_torch, merged_name, bid)
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return
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else:
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return
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yield from super().modify_tensors(data_torch, name, bid)
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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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# 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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@ModelBase.register(
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"DeepseekV2ForCausalLM",
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"DeepseekV3ForCausalLM",
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"KimiVLForConditionalGeneration",
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"KimiK25ForConditionalGeneration",
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"YoutuForCausalLM",
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"YoutuVLForConditionalGeneration",
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)
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class DeepseekV2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK2
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# TODO @ngxson : remove this when we support MTP for deepseek models
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skip_mtp = True
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merge_expert = True
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
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self.origin_hf_arch = hparams.get('architectures', [None])[0]
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# special handling for Deepseek OCR
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if self.origin_hf_arch == "DeepseekOCRForCausalLM":
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self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
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self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
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self.gguf_writer.add_architecture()
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# default jinja template
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self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")
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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 # ty: ignore[unresolved-attribute]
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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 # ty: ignore[unresolved-attribute]
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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 set_gguf_parameters(self):
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is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)
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if is_ocr:
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self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
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else:
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# note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
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self.hparams["num_key_value_heads"] = 1
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self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)
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super().set_gguf_parameters()
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hparams = self.hparams
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# first_k_dense_replace: number of leading layers using dense FFN instead of MoE
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# For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
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# For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
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has_moe = hparams.get("n_routed_experts") is not None
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first_k_dense_replace = hparams.get("first_k_dense_replace")
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if first_k_dense_replace is None:
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# Default: if no MoE, all layers are dense; if MoE, none are dense
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first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
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self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
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kv_lora_rank = hparams.get("kv_lora_rank", 512)
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
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self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
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# note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
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if not is_ocr:
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self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
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self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length(kv_lora_rank)
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self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
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# MoE parameters (required by C++ code for DEEPSEEK2 arch)
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# For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
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moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
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self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
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if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
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self.gguf_writer.add_expert_count(n_routed_experts)
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# expert_shared_count is required by C++ code, default to 0 for non-MoE models
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n_shared_experts = hparams.get("n_shared_experts", 0)
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self.gguf_writer.add_expert_shared_count(n_shared_experts)
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# When not set, C++ code will use scale_w = false to skip the no-op scaling
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if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
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self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
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if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
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self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
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self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
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if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
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# [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
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# note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
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# ref https://github.com/ggml-org/llama.cpp/pull/17945
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self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)
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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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# skip lm_head.weight if tie_word_embeddings is True
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if self.hparams.get("tie_word_embeddings", False):
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if name == "lm_head.weight" or name == "model.lm_head.weight":
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logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
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return
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# skip Multi-Token Prediction (MTP) layers
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if self.skip_mtp:
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block_count = self.hparams["num_hidden_layers"]
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match = re.match(r"model.layers.(\d+)", name)
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if match and int(match.group(1)) >= block_count:
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return
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# process the experts separately
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if self.merge_expert and 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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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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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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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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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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yield from super().modify_tensors(data_torch, merged_name, bid)
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return
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else:
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return
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# note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
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if name.endswith("kv_b_proj.weight"):
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name_kb = name.replace("kv_b_proj", "k_b_proj")
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name_vb = name.replace("kv_b_proj", "v_b_proj")
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n_head_kv = self.hparams["num_key_value_heads"]
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v_head_dim = self.hparams["v_head_dim"]
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qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
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assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
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kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
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k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
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k_b = k_b.transpose(1, 2)
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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}")
|
||||
Reference in New Issue
Block a user