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