from __future__ import annotations
import json
import math
import re
from typing import Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, gguf
@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
@ModelBase.example("baidu/ERNIE-4.5-0.3B-PT")
class Ernie4_5Model(TextModel):
model_arch = gguf.MODEL_ARCH.ERNIE4_5
def set_vocab(self):
self._set_vocab_sentencepiece()
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
def set_gguf_parameters(self):
super().set_gguf_parameters()
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if "ernie." in name:
name = name.replace("ernie.", "model.")
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]
num_kv_heads = self.hparams["num_key_value_heads"]
if (head_dim := self.hparams.get("head_dim")) is None:
head_dim = self.hparams["hidden_size"] // num_heads
# split the qkv weights
# qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size]
if "qkv_proj" in name:
name_q = name.replace("qkv_proj.weight", "q_proj.weight")
name_k = name.replace("qkv_proj.weight", "k_proj.weight")
name_v = name.replace("qkv_proj.weight", "v_proj.weight")
total_q_dim = num_heads * head_dim
total_k_dim = num_kv_heads * head_dim
total_v_dim = num_kv_heads * head_dim
q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0)
yield from super().modify_tensors(q_proj_weight, name_q, bid)
yield from super().modify_tensors(k_proj_weight, name_k, bid)
yield from super().modify_tensors(v_proj_weight, name_v, bid)
# split the up_gate_proj into gate and up
# up_gate_proj shape: [2 * intermediate_size, hidden_size]
elif "up_gate_proj" in name:
name_up = name.replace("up_gate_proj.weight", "up_proj.weight")
name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight")
dim_half = data_torch.shape[0] // 2
gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0)
yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
yield from super().modify_tensors(up_proj_weight, name_up, bid)
else:
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Ernie4_5_MoeForCausalLM")
@ModelBase.example("baidu/ERNIE-4.5-21B-A3B-PT")
class Ernie4_5MoeModel(Ernie4_5Model):
model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
_experts: list[dict[str, Tensor]] | None = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._experts = [{} for _ in range(self.block_count)]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None:
self.gguf_writer.add_expert_shared_count(shared_expert_count)
if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
match = re.match(r"model.mtp_block.(\d+)", name)
if match:
return None
# skip all other MTP tensors for now
match = re.match(r"model.mtp_emb_norm.(\d+)", name)
if match:
return None
match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
if match:
return None
match = re.match(r"model.mtp_linear_proj.(\d+)", name)
if match:
return None
return super().filter_tensors(item)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["moe_num_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 ["gate_proj", "up_proj", "down_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename_to_retrieve])
del self._experts[bid][ename_to_retrieve]
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)
else:
yield from ModelBase.modify_tensors(self, 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("PaddleOCRVLForConditionalGeneration")
@ModelBase.example("PaddlePaddle/PaddleOCR-VL")
class PaddleOCRModel(Ernie4_5Model):
model_arch = gguf.MODEL_ARCH.PADDLEOCR
@ModelBase.register("PaddleOCRVisionModel")
@ModelBase.example("PaddlePaddle/PaddleOCR-VL")
class PaddleOCRVisionModel(MmprojModel):
# PaddleOCR-VL uses a modified version of Siglip
min_pixels: int = 0
max_pixels: int = 0
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
self.min_pixels = self.preprocessor_config["min_pixels"]
self.max_pixels = self.preprocessor_config["max_pixels"]
self.hparams_vision["image_size"] = int(math.sqrt(self.max_pixels))
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
hparams = self.hparams_vision
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PADDLEOCR)
self.gguf_writer.add_vision_max_pixels(self.max_pixels)
self.gguf_writer.add_vision_min_pixels(self.min_pixels)
self.gguf_writer.add_vision_use_gelu(True)
self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("rms_norm_eps", 1e-6))
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if "vision_model" not in name and "mlp_AR" not in name:
return None
name = name.replace("visual.", "model.")
if "packing_position_embedding" in name:
# unused
return None
if "vision_model.head" in name:
# we don't yet support image embeddings for this model
return None
return super().filter_tensors((name, gen))