File size: 3,229 Bytes
76513ec | 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 | 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("GPT2LMHeadModel")
class GPT2Model(TextModel):
model_arch = gguf.MODEL_ARCH.GPT2
def set_gguf_parameters(self):
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["n_ctx"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# we don't need these
if name.endswith((".attn.bias", ".attn.masked_bias")):
yield from super().modify_tensors(data_torch, name, bid)
return
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
data_torch = data_torch.transpose(1, 0)
new_name = self.map_tensor_name(name)
yield from super().modify_tensors(data_torch, new_name, bid)
@ModelBase.register("RuGPT3XLForCausalLM")
class RuGPT3XLModel(TextModel):
model_arch = gguf.MODEL_ARCH.GPT2
_qkv_parts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Fuse separate Q, K, V projections into a single QKV tensor
if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
suffix = "weight" if name.endswith(".weight") else "bias"
part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
key = f"{part}.{suffix}"
assert bid is not None
if self._qkv_parts is None:
self._qkv_parts = [{} for _ in range(self.block_count)]
self._qkv_parts[bid][key] = data_torch
q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
q = self._qkv_parts[bid].pop(q_key)
k = self._qkv_parts[bid].pop(k_key)
v = self._qkv_parts[bid].pop(v_key)
data_torch = torch.cat([q, k, v], dim=0)
name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
else:
return
yield from super().modify_tensors(data_torch, name, bid)
def prepare_tensors(self):
super().prepare_tensors()
if self._qkv_parts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
if len(parts) > 0:
raise ValueError(f"Unprocessed Q/K/V parts: {parts}")
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