File size: 1,178 Bytes
260d670 |
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 |
# https://huggingface.co/docs/transformers/custom_models
from transformers import PreTrainedModel, GPTNeoXForCausalLM, AutoModelForCausalLM, AutoTokenizer, GPTNeoXConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from torch.nn.functional import log_softmax
from torch.nn.modules.container import ModuleList
# In the example there is also config class but we'll just use the one from GPTNeoX
# The norm is to import from PreTrainedModel but we'll take a shortcut
class CustomModel3(GPTNeoXForCausalLM):
config_class = GPTNeoXConfig
def __init__(self, config):
super().__init__(config)
def forward(self, *args, **kwargs):
# See https://huggingface.co/docs/transformers/main_classes/output
out = super().forward(*args, **kwargs)
out.logits = log_softmax(out.logits, dim=-1)
return out
@classmethod
def copy_from_neox(cls, *args, **kwargs):
m0 = GPTNeoXForCausalLM.from_pretrained(*args, **kwargs)
m1 = cls(m0.config).to(dtype=m0.dtype, device=m0.device)
m1.load_state_dict(m0.state_dict())
return m1
CustomModel3.register_for_auto_class('AutoModelForCausalLM')
|