from __future__ import annotations
import math
from pathlib import Path
from typing import Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, gguf
from .qwenvl import Qwen2VLVisionModel
@ModelBase.register("ExaoneForCausalLM")
@ModelBase.example("LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct")
class ExaoneModel(TextModel):
model_arch = gguf.MODEL_ARCH.EXAONE
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
assert (hparams["activation_function"] == "silu")
rotary_factor = self.rope_parameters.get("partial_rotary_factor")
rotary_factor = rotary_factor if rotary_factor is not None else 1.0
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
if rope_params.get("rope_type", '').lower() == "llama3":
base = self.rope_parameters.get("rope_theta", 10000.0)
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_params.get("factor", 8.0)
low_freq_factor = rope_params.get("low_freq_factor", 1.0)
high_freq_factor = rope_params.get("high_freq_factor", 4.0)
old_context_len = rope_params.get("original_max_position_embeddings", 8192)
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
assert low_freq_wavelen != high_freq_wavelen
rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
@ModelBase.register("Exaone4ForCausalLM")
@ModelBase.example("LGAI-EXAONE/EXAONE-4.0-32B")
class Exaone4Model(TextModel):
model_arch = gguf.MODEL_ARCH.EXAONE4
def set_vocab(self):
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if hparams.get("sliding_window") is not None:
self.gguf_writer.add_sliding_window(hparams["sliding_window"])
if "layer_types" in hparams:
self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
elif "sliding_window_pattern" in hparams:
sliding_window_pattern = []
if isinstance(hparams["sliding_window_pattern"], str): # e.g. LLLG
for i in range(hparams["num_hidden_layers"]):
sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L")
if isinstance(hparams["sliding_window_pattern"], int): # e.g. 4
for i in range(hparams["num_hidden_layers"]):
sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0)
if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
if rope_params.get("rope_type", '').lower() == "llama3":
base = rope_params.get("rope_theta", 10_000.0)
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_params.get("factor", 16.0)
low_freq_factor = rope_params.get("low_freq_factor", 1.0)
high_freq_factor = rope_params.get("high_freq_factor", 4.0)
old_context_len = rope_params.get("original_max_position_embeddings", 8192)
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
# note: transformers >= 5.1 renamed the class to "ExaoneMoeForCausalLM" (lowercase 'e'),
# so accept both spellings - LG AI have updated the configs of already-released models
@ModelBase.register("ExaoneMoEForCausalLM", "ExaoneMoeForCausalLM")
@ModelBase.example("LGAI-EXAONE/K-EXAONE-236B-A23B")
class ExaoneMoEModel(Exaone4Model):
model_arch = gguf.MODEL_ARCH.EXAONE_MOE
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
def set_gguf_parameters(self):
super().set_gguf_parameters()
moe_intermediate_size = self.hparams["moe_intermediate_size"]
num_shared_experts = self.hparams["num_shared_experts"]
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
self.gguf_writer.add_expert_shared_count(num_shared_experts)
self.gguf_writer.add_expert_shared_feed_forward_length(moe_intermediate_size * num_shared_experts)
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.startswith("mtp."):
if name.find("layers.") != -1:
# `mtp.layers.0.[module_name]` format
name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + self.hparams['num_hidden_layers']}")
else:
# mtp fc/norm weights
remapper = {
"mtp.fc": "model.layers.{bid}.eh_proj",
"mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
"mtp.pre_fc_norm_hidden": "model.layers.{bid}.hnorm",
"mtp.norm": "model.layers.{bid}.shared_head.norm",
}
_n = Path(name)
new_name = remapper[_n.stem] + _n.suffix
# set shared weights for all NextN/MTP layers
for bid in range(self.hparams['num_hidden_layers'], self.block_count):
yield from super().modify_tensors(data_torch, new_name.format(bid=bid), bid)
return
if name.find("mlp.experts") != -1:
n_experts = self.find_hparam(["num_local_experts", "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 ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
yield from super().modify_tensors(data_torch, new_name, bid)
return
else:
return
yield from super().modify_tensors(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("Exaone4_5_ForConditionalGeneration")
@ModelBase.example("LGAI-EXAONE/EXAONE-4.5-33B")
class Exaone4_5_TextModel(Exaone4Model):
"""Text tower of EXAONE 4.5; Tensors match EXAONE4"""
model_arch = gguf.MODEL_ARCH.EXAONE4
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
if n_nextn > 0:
self.block_count = self.hparams["num_hidden_layers"] + n_nextn
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
def set_gguf_parameters(self):
super().set_gguf_parameters()
n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
if n_nextn > 0:
self.gguf_writer.add_nextn_predict_layers(n_nextn)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.startswith("mtp."):
n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
if n_nextn <= 0:
return
nh = self.hparams["num_hidden_layers"]
if ".layers." in name:
share = self.hparams.get("mtp_share_layers", False)
mtp_bid = bid if bid is not None else 0
if share:
for k in range(n_nextn):
nn = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{nh + k}")
yield from super().modify_tensors(data_torch, nn, nh + k)
return
name = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{mtp_bid + nh}")
else:
remapper = {
"mtp.fc": gguf.MODEL_TENSOR.NEXTN_EH_PROJ,
"mtp.pre_fc_norm_embedding": gguf.MODEL_TENSOR.NEXTN_ENORM,
"mtp.pre_fc_norm_hidden": gguf.MODEL_TENSOR.NEXTN_HNORM,
"mtp.norm": gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
}
_n = Path(name)
key = _n.stem
if key not in remapper:
return
for bid_mtp in range(nh, self.block_count):
mapped_name = self.format_tensor_name(remapper[key], bid_mtp, suffix=_n.suffix)
yield from ModelBase.modify_tensors(self, data_torch, mapped_name, bid_mtp)
return
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Exaone4_5_ForConditionalGeneration")
@ModelBase.example("LGAI-EXAONE/EXAONE-4.5-33B")
class Exaone4_5VisionModel(Qwen2VLVisionModel):
"""Vision tower for EXAONE 4.5; Qwen2-VL-style ViT (GQA) + patch merger"""
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
name = name.replace("model.visual.", "visual.", 1)
return super().filter_tensors((name, gen))
def set_gguf_parameters(self):
MmprojModel.set_gguf_parameters(self)
assert self.hparams_vision is not None
hparams = self.hparams_vision
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.EXAONE4_5)
self.gguf_writer.add_vision_use_silu(True)
self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
num_kv_head = self.find_vparam(["num_key_value_heads"], optional=True)
if num_kv_head is not None:
self.gguf_writer.add_vision_head_count_kv(num_kv_head)
eps = hparams.get("rms_norm_eps", self.global_config.get("rms_norm_eps", 1e-6))
self.gguf_writer.add_vision_attention_layernorm_eps(eps)
if (window_size := hparams.get("window_size")) is not None:
self.gguf_writer.add_vision_window_size(window_size)
fullatt_block_indexes = hparams.get("fullatt_block_indexes")
if fullatt_block_indexes:
n_wa_pattern = fullatt_block_indexes[0] + 1
for i in range(1, len(fullatt_block_indexes)):
if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
raise ValueError(f"Invalid EXAONE4.5 fullatt_block_indexes: {fullatt_block_indexes}")
self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if ".qkv." in name:
yield from ModelBase.modify_tensors(self, data_torch, name, bid)
return
yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)