mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-05-17 04:09:19 +00:00
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>
108 lines
4.7 KiB
Python
108 lines
4.7 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import Iterable, TYPE_CHECKING
|
|
|
|
import torch
|
|
|
|
if TYPE_CHECKING:
|
|
from torch import Tensor
|
|
|
|
from .base import ModelBase, TextModel, gguf, logger
|
|
|
|
|
|
@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
|
|
class GroveMoeModel(TextModel):
|
|
model_arch = gguf.MODEL_ARCH.GROVEMOE
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
|
|
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
|
|
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
|
|
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
|
|
self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
|
|
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
|
|
self.gguf_writer.add_experts_per_group(2)
|
|
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
|
|
self.gguf_writer.add_expert_group_scale(0.05)
|
|
|
|
_experts: list[dict[str, Tensor]] | None = None
|
|
_chunk_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.endswith(".expert_bias"):
|
|
# FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
|
|
return
|
|
|
|
# process the experts separately
|
|
if name.find("chunk_experts") != -1:
|
|
n_experts = self.find_hparam(["num_local_experts", "num_experts"]) // 2 # see add_experts_per_group
|
|
assert bid is not None
|
|
|
|
if self._chunk_experts is None:
|
|
self._chunk_experts = [{} for _ in range(self.block_count)]
|
|
|
|
self._chunk_experts[bid][name] = data_torch
|
|
|
|
if len(self._chunk_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.chunk_experts.{xid}.{w_name}.weight"
|
|
datas.append(self._chunk_experts[bid][ename])
|
|
del self._chunk_experts[bid][ename]
|
|
|
|
data_torch = torch.stack(datas, dim=0)
|
|
|
|
merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"
|
|
|
|
yield from super().modify_tensors(data_torch, merged_name, bid)
|
|
return
|
|
else:
|
|
return
|
|
elif name.find("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"
|
|
|
|
yield from super().modify_tensors(data_torch, merged_name, bid)
|
|
return
|
|
else:
|
|
return
|
|
|
|
yield from super().modify_tensors(data_torch, name, bid)
|
|
|
|
def prepare_tensors(self):
|
|
super().prepare_tensors()
|
|
|
|
if self._chunk_experts is not None:
|
|
# flatten `list[dict[str, Tensor]]` into `list[str]`
|
|
chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
|
|
if len(chunk_experts) > 0:
|
|
raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")
|
|
|
|
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}")
|