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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>
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
from __future__ import annotations
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from typing import Iterable, TYPE_CHECKING
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf
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from .llama import LlamaModel
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@ModelBase.register("OlmoForCausalLM")
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@ModelBase.register("OLMoForCausalLM")
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class OlmoModel(TextModel):
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model_arch = gguf.MODEL_ARCH.OLMO
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_layer_norm_eps(1e-5)
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clip_qkv = self.hparams.get("clip_qkv")
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if clip_qkv is not None:
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self.gguf_writer.add_clamp_kqv(clip_qkv)
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# Same as super class, but permuting q_proj, k_proj
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# Copied from: LlamaModel
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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n_head = self.hparams["num_attention_heads"]
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n_kv_head = self.hparams.get("num_key_value_heads")
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if name.endswith("q_proj.weight"):
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data_torch = LlamaModel.permute(data_torch, n_head, n_head)
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if name.endswith("k_proj.weight"):
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data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
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yield from super().modify_tensors(data_torch, name, bid)
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@ModelBase.register("SeedOssForCausalLM")
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class SeedOssModel(TextModel):
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model_arch = gguf.MODEL_ARCH.SEED_OSS
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@ModelBase.register("Olmo2ForCausalLM")
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@ModelBase.register("Olmo3ForCausalLM")
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class Olmo2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.OLMO2
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if "sliding_window" in self.hparams:
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self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
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sliding_window_pattern = []
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if "layer_types" in self.hparams:
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sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
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else:
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# Olmo2 does not use sliding window attention.
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# Olmo3 defaults to using sliding window for all layers except every 4th.
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for i in range(self.hparams["num_hidden_layers"]):
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sliding_window_pattern.append((i + 1) % 4 != 0)
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self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
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@ModelBase.register("OlmoeForCausalLM")
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class OlmoeModel(TextModel):
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model_arch = gguf.MODEL_ARCH.OLMOE
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_layer_norm_rms_eps(1e-5)
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_experts: list[dict[str, Tensor]] | None = None
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# Copied from: Qwen2MoeModel
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# process the experts separately
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if name.find("experts") != -1:
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n_experts = self.find_hparam(["num_local_experts", "num_experts"])
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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# merge the experts into a single 3d tensor
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for w_name in ["down_proj", "gate_proj", "up_proj"]:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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yield from super().modify_tensors(data_torch, merged_name, bid)
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return
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else:
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return
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yield from super().modify_tensors(data_torch, name, bid)
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# Copied from: Qwen2MoeModel
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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# flatten `list[dict[str, Tensor]]` into `list[str]`
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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