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cc7200bf12
* 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>
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
from __future__ import annotations
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from typing import Callable, Iterable, TYPE_CHECKING
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import MmprojModel, ModelBase, gguf
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from .llama import LlamaModel
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@ModelBase.register("JanusForConditionalGeneration")
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class JanusProModel(LlamaModel):
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model_arch = gguf.MODEL_ARCH.LLAMA # reuse Llama arch
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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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# Skip vision, aligner, and generation tensors
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skip_prefixes = (
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'model.vision_model.',
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'model.aligner.',
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'model.vqmodel.',
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'model.generation_embeddings.',
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'model.generation_aligner.',
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'model.generation_head.',
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)
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if name.startswith(skip_prefixes):
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return None
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return super().filter_tensors(item)
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@ModelBase.register("JanusForConditionalGeneration")
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class JanusProVisionModel(MmprojModel):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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assert self.hparams_vision is not None
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if "intermediate_size" not in self.hparams_vision:
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mlp_ratio = self.hparams_vision.get("mlp_ratio")
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hidden_size = self.hparams_vision.get("hidden_size")
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if mlp_ratio is not None and hidden_size is not None:
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self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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assert self.hparams_vision is not None
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)
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self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
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hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
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if hidden_act == "gelu":
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self.gguf_writer.add_vision_use_gelu(True)
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elif hidden_act == "silu":
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self.gguf_writer.add_vision_use_silu(True)
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def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
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"""Map aligner tensors to projector format"""
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suffix = ".bias" if name.endswith(".bias") else ".weight"
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if name.startswith("model.aligner."):
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local_name = name[len("model.aligner."):]
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elif name.startswith("aligner."):
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local_name = name[len("aligner."):]
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else:
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raise ValueError(f"Unsupported Janus aligner prefix: {name}")
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if local_name.startswith("fc1."):
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mm_index = 0
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elif local_name.startswith("hidden_layers."):
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parts = local_name.split(".", 2)
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if len(parts) < 3:
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raise ValueError(f"Unexpected Janus aligner tensor name: {name}")
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mm_index = int(parts[1]) + 1
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else:
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raise ValueError(f"Unsupported Janus aligner tensor: {name}")
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tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
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return [(tensor_name, data_torch)]
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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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# Skip generation-related components
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skip_generation_prefixes = (
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'model.vqmodel.',
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'vqmodel.',
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'model.generation_embeddings.',
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'generation_embeddings.',
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'model.generation_aligner.',
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'generation_aligner.',
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'model.generation_head.',
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'generation_head.',
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)
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if name.startswith(skip_generation_prefixes):
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return None
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return super().filter_tensors(item)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# Handle aligner tensors
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if name.startswith(('model.aligner.', 'aligner.')):
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yield from self._map_aligner_tensor(data_torch, name)
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return
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# Handle vision tensors
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if name.startswith(('model.vision_model.', 'vision_model.')):
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yield from super().modify_tensors(data_torch, name, bid)
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return
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return
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