kvcache-ai-ktransformers/ktransformers/operators/base_operator.py

63 lines
2.9 KiB
Python

'''
Description :
Author : Boxin Zhang
Version : 0.1.0
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
'''
from typing import Any
from torch import nn, Tensor
from ktransformers.util.custom_loader import GGUFLoader
from transformers.configuration_utils import PretrainedConfig
import ktransformers.util.utils as utils
class BaseInjectedModule(nn.Module):
def __init__(self,
key: str,
gguf_loader : GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module,
prefill_device: str = "cuda",
generate_device: str = "cuda",
**kwargs):
nn.Module.__init__(self)
nn.Module.__setattr__(self, "orig_module", orig_module)
object.__setattr__(self, "key", key)
object.__setattr__(self, "gguf_loader", gguf_loader)
object.__setattr__(self, "config", config)
object.__setattr__(self, "prefill_device", prefill_device)
object.__setattr__(self, "generate_device", generate_device)
object.__setattr__(self, "device", generate_device)
def __getattr__(self, name: str) -> Any:
# __getattr__ in nn.Module doesn't call super().__getattribute__ when name is not in nn.Module.__dict__,
# but __setattr__ in nn.Module call super().__setattr__ in that case, there may be some attribute set
# but can't get using __getattr__, typically these attr is build in attr of the class, so class.attr does not
# call __getattr__.
# Example:
# ...import torch
# ...l=torch.nn.Linear(100,200)
# ...l.out_features # 200
# ...l.__getattr__("out_features") # AttributeError: 'Linear' object has no attribute 'out_features'
try:
return object.__getattribute__(self, name) # if this attr belongs to BaseInjectedModule
except:
if name == "orig_module":
return nn.Module.__getattr__(self, "orig_module")
try:
return nn.Module.__getattr__(self, "orig_module").__getattr__(name) # if this attr belongs to orig_module
except:
return super(nn.Module, nn.Module.__getattr__(self, "orig_module")).__getattribute__(name) # if this attr belongs to orig_module but not in nn.Module.__dict__
def __setattr__(self, name: str, value: Tensor | nn.Module) -> None:
if name == "orig_module":
return nn.Module.__setattr__(self, "orig_module", value)
elif hasattr(self, name):
return object.__setattr__(self, name, value)
return nn.Module.__getattr__(self, "orig_module").__setattr__(name, value)
def forward(self, *args, **kwargs):
return self.orig_module.forward(*args, **kwargs)
def load(self):
for name, child in self._modules.items():
utils.load_weights(child, self.gguf_loader, self.key+".")