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79b4c43 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 | from __future__ import annotations
from typing import Any, Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, gguf
from .gemma import ConformerAudioModel
@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
class LFM2Model(TextModel):
model_arch = gguf.MODEL_ARCH.LFM2
def _add_feed_forward_length(self):
ff_dim = self.find_hparam(["block_ff_dim", "intermediate_size"])
auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"]
ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"]
multiple_of = self.hparams["block_multiple_of"]
if auto_adjust_ff_dim:
ff_dim = int(2 * ff_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
ff_dim = int(ffn_dim_multiplier * ff_dim)
ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
self.gguf_writer.add_feed_forward_length(ff_dim)
def set_gguf_parameters(self):
# set num_key_value_heads only for attention layers
self.hparams["num_key_value_heads"] = [
self.hparams["num_key_value_heads"] if layer_type != "conv" else 0
for layer_type in self.hparams["layer_types"]
]
super().set_gguf_parameters()
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"])
self._add_feed_forward_length()
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if ConformerAudioModel.is_audio_tensor(name):
# skip multimodal tensors
return None
name = name.replace("lfm.", "model.") # audio
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# conv op requires 2d tensor
if 'conv.conv' in name:
data_torch = data_torch.squeeze(1)
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Lfm2Model", "Lfm2BidirectionalModel")
class LFM2ColBertModel(LFM2Model):
model_arch = gguf.MODEL_ARCH.LFM2
dense_tensor_name = "dense_2"
def set_gguf_parameters(self):
super().set_gguf_parameters()
if self.hf_arch == "Lfm2BidirectionalModel":
self.gguf_writer.add_causal_attention(False)
self._try_set_pooling_type()
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if not name.startswith(self.dense_tensor_name):
name = "model." + name
yield from super().modify_tensors(data_torch, name, bid)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# optional dense tensor is stored in a separate safetensors file
from safetensors.torch import load_file
tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
if not tensors_file.is_file():
return
tensor = load_file(tensors_file)["linear.weight"]
self.gguf_writer.add_embedding_length_out(tensor.shape[0])
yield f"{self.dense_tensor_name}.weight", tensor.clone()
@ModelBase.register("Lfm2MoeForCausalLM")
class LFM2MoeModel(TextModel):
model_arch = gguf.MODEL_ARCH.LFM2MOE
def set_gguf_parameters(self):
# set num_key_value_heads only for attention layers
self.hparams["num_key_value_heads"] = [
self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
for layer_type in self.hparams["layer_types"]
]
super().set_gguf_parameters()
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
self.gguf_writer.add_leading_dense_block_count(self.hparams["num_dense_layers"])
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
# cache for experts weights for merging
_experts_cache: dict[int, dict[str, Tensor]] = {}
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if name.endswith(".expert_bias"):
name = name.replace(".expert_bias", ".expert_bias.bias")
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# conv op requires 2d tensor
if 'conv.conv' in name:
data_torch = data_torch.squeeze(1)
# merge expert weights
if 'experts' in name:
n_experts = self.find_hparam(["num_local_experts", "num_experts"])
assert bid is not None
expert_cache = self._experts_cache.setdefault(bid, {})
expert_cache[name] = data_torch
expert_weights = ["w1", "w2", "w3"]
# not enough expert weights to merge
if len(expert_cache) < n_experts * len(expert_weights):
return
for w_name in expert_weights:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{w_name}.weight"
datas.append(expert_cache[ename])
del expert_cache[ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"layers.{bid}.feed_forward.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid)
del self._experts_cache[bid]
return
yield from super().modify_tensors(data_torch, name, bid)
def prepare_tensors(self):
super().prepare_tensors()
assert not self._experts_cache
@ModelBase.register("Lfm2VlForConditionalGeneration")
class LFM2VLModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
# TODO(tarek): for dynamic resolution image_size is not specified, setting here for compatibility
self.hparams_vision["image_size"] = 256
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2)
self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["layer_norm_eps"]))
self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("downsample_factor", 2))
self.gguf_writer.add_vision_use_gelu(True)
# python notation, e.g. for vision_feature_layer == -1, we pick last layer -> vision_feature_layers_to_drop = 0
vision_feature_layers_to_drop = -(self.global_config.get("vision_feature_layer", -1) + 1)
self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys) - vision_feature_layers_to_drop)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
name = name.replace("model.vision_tower.", "vision_tower.")
name = name.replace("model.multi_modal_projector.", "multi_modal_projector.")
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if "patch_embedding.weight" in name:
data_torch = data_torch.view(data_torch.shape[0], 16, 16, 3).permute(0, 3, 1, 2)
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Lfm2AudioForConditionalGeneration")
class LFM2AudioModel(ConformerAudioModel):
has_vision_encoder = False
has_audio_encoder = True
model_name = "Lfm2AudioEncoder"
def get_audio_config(self) -> dict[str, Any] | None:
return self.global_config.get("encoder")
def set_gguf_parameters(self):
assert self.hparams_audio is not None
self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
self.hparams_audio["intermediate_size"] = self.hparams_audio["d_model"]
self.hparams_audio["num_attention_heads"] = self.hparams_audio["n_heads"]
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2A)
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# skip language model tensors
if name.startswith("lfm."):
return None
# for training only
if any(p in name for p in ["audio_loss_weight"]):
return None
# for audio output
if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
return None
return super().filter_tensors(item)
@ModelBase.register("Lfm25AudioTokenizer")
class LFM25AudioTokenizer(LFM2Model):
model_arch = gguf.MODEL_ARCH.LFM2
def set_vocab(self):
self._set_vocab_none()
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
self.gguf_writer.add_embedding_length_out(self.hparams["output_size"])
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# skip language model tensors
if name == "istft.window" or name.startswith("emb.emb"):
return None
if name.startswith("lin"):
name = name.replace("lin", "dense_2_out")
return super().filter_tensors((name, gen))
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