File size: 2,469 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 | from __future__ import annotations
from typing import Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
class BaichuanModel(TextModel):
model_arch = gguf.MODEL_ARCH.BAICHUAN
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
logger.info(f"Unpacking and permuting layer {bid}")
yield from [
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
self._reverse_hf_part(data_torch, 2)),
]
else:
yield from self.modify_tensors(data_torch, self.map_tensor_name(name), bid)
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
def _reverse_hf_permute_part(
self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
) -> Tensor:
r = weights.shape[0] // 3
return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
r = weights.shape[0] // 3
return weights[r * n_part:r * n_part + r, ...]
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