from __future__ import annotations
from typing import Callable, Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, gguf
from .llama import LlamaModel
@ModelBase.register("JanusForConditionalGeneration")
@ModelBase.example("deepseek-community/Janus-Pro-1B")
class JanusProModel(LlamaModel):
model_arch = gguf.MODEL_ARCH.LLAMA # reuse Llama arch
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# Skip vision, aligner, and generation tensors
skip_prefixes = (
'model.vision_model.',
'model.aligner.',
'model.vqmodel.',
'model.generation_embeddings.',
'model.generation_aligner.',
'model.generation_head.',
)
if name.startswith(skip_prefixes):
return None
return super().filter_tensors(item)
@ModelBase.register("JanusForConditionalGeneration")
@ModelBase.example("deepseek-community/Janus-Pro-1B")
class JanusProVisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
if "intermediate_size" not in self.hparams_vision:
mlp_ratio = self.hparams_vision.get("mlp_ratio")
hidden_size = self.hparams_vision.get("hidden_size")
if mlp_ratio is not None and hidden_size is not None:
self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)
self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
if hidden_act == "gelu":
self.gguf_writer.add_vision_use_gelu(True)
elif hidden_act == "silu":
self.gguf_writer.add_vision_use_silu(True)
def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
"""Map aligner tensors to projector format"""
suffix = ".bias" if name.endswith(".bias") else ".weight"
if name.startswith("model.aligner."):
local_name = name[len("model.aligner."):]
elif name.startswith("aligner."):
local_name = name[len("aligner."):]
else:
raise ValueError(f"Unsupported Janus aligner prefix: {name}")
if local_name.startswith("fc1."):
mm_index = 0
elif local_name.startswith("hidden_layers."):
parts = local_name.split(".", 2)
if len(parts) < 3:
raise ValueError(f"Unexpected Janus aligner tensor name: {name}")
mm_index = int(parts[1]) + 1
else:
raise ValueError(f"Unsupported Janus aligner tensor: {name}")
tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
return [(tensor_name, data_torch)]
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# Skip generation-related components
skip_generation_prefixes = (
'model.vqmodel.',
'vqmodel.',
'model.generation_embeddings.',
'generation_embeddings.',
'model.generation_aligner.',
'generation_aligner.',
'model.generation_head.',
'generation_head.',
)
if name.startswith(skip_generation_prefixes):
return None
return super().filter_tensors(item)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Handle aligner tensors
if name.startswith(('model.aligner.', 'aligner.')):
yield from self._map_aligner_tensor(data_torch, name)
return
# Handle vision tensors
if name.startswith(('model.vision_model.', 'vision_model.')):
yield from super().modify_tensors(data_torch, name, bid)
return
return