from __future__ import annotations
import json
from typing import Any, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, gguf
def _unpermute_for_rope(tensor: "Tensor", n_heads: int) -> "Tensor":
"""Invert transformers' `_permute_for_rope`: HF stores Q/K in rotate_half layout,
llama.cpp consumes the interleaved (NORM) layout."""
if tensor.ndim == 2:
dim1, dim2 = tensor.shape
return tensor.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2)
if tensor.ndim == 1:
(dim1,) = tensor.shape
return tensor.view(n_heads, 2, dim1 // n_heads // 2).transpose(1, 2).reshape(dim1)
raise ValueError(f"_unpermute_for_rope: unexpected shape {tuple(tensor.shape)}")
@ModelBase.register("MuseGlimmerForConditionalGeneration")
@ModelBase.example("meta-models/Muse-Glimmer-30B")
class MuseGlimmerModel(TextModel):
model_arch = gguf.MODEL_ARCH.MUSE_GLIMMER
def norm_shift(self, name: str) -> float:
# All four layer norms use 1, the final norm uses 0.
return 1.0 if name.endswith("layernorm.weight") else 0.0
def set_vocab(self):
self._set_vocab_gpt2()
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained(self.dir_model)
eot_id = tok.convert_tokens_to_ids("<|eot|>")
if isinstance(eot_id, int) and eot_id >= 0:
self.gguf_writer.add_eot_token_id(eot_id)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_final_logit_softcapping(hparams["final_logit_softcapping"])
self.gguf_writer.add_logit_scale(hparams["output_multiplier"])
self.gguf_writer.add_sliding_window(hparams["sliding_window"])
self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
shift = self.norm_shift(name)
if shift != 0.0:
data_torch = data_torch + shift
# Invert transformers' `_permute_for_rope` on Q/K, we keep ggml's NORM (interleaved) rope
if ".self_attn.q_proj." in name:
data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_attention_heads"]))
elif ".self_attn.k_proj." in name:
data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_key_value_heads"]))
# Synthesize QK-norm weights to absorb qk_scale_factor.
# MuseGlimmer implementation: scaleless RMSNorm followed by qk_scale_factor..
if bid is not None and name.endswith(f"model.layers.{bid}.self_attn.q_proj.weight"):
head_dim = self.hparams["head_dim"]
q_scale = float(self.hparams["qk_scale_factor"])
yield (
self.map_tensor_name(f"model.layers.{bid}.self_attn.q_norm.weight"),
torch.full((head_dim,), q_scale, dtype=torch.float32),
)
yield (
self.map_tensor_name(f"model.layers.{bid}.self_attn.k_norm.weight"),
torch.ones((head_dim,), dtype=torch.float32),
)
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("MuseGlimmerForConditionalGeneration")
@ModelBase.example("meta-models/Muse-Glimmer-30B")
class MuseGlimmerVisionModel(MmprojModel):
def get_vision_config(self) -> dict[str, Any] | None:
c = self.global_config.get("vision_config")
if not c:
return None
# MuseGlimmer actually uses dynamic size, initialize with nominal size
image_size = c["pos_emb_height"] * c["patch_size"] * c["merge_size"]
return {**c, "image_size": image_size}
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
c = self.hparams_vision # enriched vision_config from get_vision_config()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSE_GLIMMER)
self.gguf_writer.add_vision_attention_layernorm_eps(float(c["layer_norm_eps"]))
self.gguf_writer.add_vision_spatial_merge_size(int(c["merge_size"]))
@classmethod
def filter_tensors(cls, item):
name, gen = item
keep = ("model.vision_tower.", "model.vision_adapter.", "model.vision_projection.")
if not any(name.startswith(k) for k in keep):
return None
return super().filter_tensors((name, gen))
# 3-layer projector MLP
_MM_MLP_MAP = {
"model.vision_adapter.fc1": (gguf.MODEL_TENSOR.V_MMPROJ, 0),
"model.vision_adapter.fc2": (gguf.MODEL_TENSOR.V_MMPROJ, 1),
"model.vision_projection": (gguf.MODEL_TENSOR.V_MMPROJ, 2),
}
def modify_tensors(self, data_torch, name, bid):
assert self.hparams_vision is not None
if ".attn.q_proj." in name or ".attn.k_proj." in name:
n_heads = int(self.hparams_vision["num_attention_heads"])
data_torch = _unpermute_for_rope(data_torch, n_heads)
# Lay out the pt=2 temporal slabs of the patch embedding as a conv2d for build_inp()
if name.endswith("patch_embedder.patch_embedding.weight"):
n_embd = data_torch.shape[0]
pt = int(self.hparams_vision["patch_temporal"])
ps = int(self.hparams_vision["patch_size"])
data_torch = data_torch.view(n_embd, pt, 3, ps, ps).sum(dim=1) # (n_embd, 3, ps, ps)
stem, _, suffix = name.rpartition(".")
if stem in self._MM_MLP_MAP:
tensor_key, idx = self._MM_MLP_MAP[stem]
yield (self.format_tensor_name(tensor_key, bid=idx, suffix="." + suffix), data_torch)
return
yield (self.map_tensor_name(name), data_torch)
@ModelBase.register("MuseGlimmerAssistantModel")
@ModelBase.example("meta-models/Muse-Glimmer-30B-assistant")
class MuseGlimmerAssistantModel(TextModel):
model_arch = gguf.MODEL_ARCH.DFLASH
def set_vocab(self):
if self.target_model_dir is None:
raise ValueError(
"MuseGlimmerAssistant (DFlash drafter) requires --target-model-dir pointing to the "
"target MuseGlimmer HF directory"
)
original_dir = self.dir_model
self.dir_model = self.target_model_dir
from . import get_model_class
with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
target_arch = json.load(f)["architectures"][0]
target_cls = get_model_class(target_arch)
if target_cls is not type(self):
target_cls.set_vocab(self) # ty: ignore[unresolved-attribute]
else:
super().set_vocab()
self.dir_model = original_dir
mask_token_id = self.hparams.get("mask_token_id")
if mask_token_id is not None:
self.gguf_writer.add_mask_token_id(int(mask_token_id))
def set_gguf_parameters(self):
super().set_gguf_parameters()
h = self.hparams
self.gguf_writer.add_block_size(int(h["block_size"]))
# dflash.target_layers[k] refers to the inputs going into the ith layer, which come from the (i-1)th layer's output.
# The transformers configuration refers to the outputs being recorded.
self.gguf_writer.add_target_layers([int(x) + 1 for x in h["target_layer_ids"]])
if h.get("sliding_window") and h.get("layer_types"):
self.gguf_writer.add_sliding_window(int(h["sliding_window"]))
self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in h["layer_types"]])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# DFlash defaults to NEOX (rotate_half) rope, matching transformers HF layout for Q/K, QK-norms
# no permutation needed.
yield (self.map_tensor_name(name), data_torch)