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# https://huggingface.co/docs/transformers/custom_models
from transformers import PreTrainedModel, AutoModelForCausalLM, AutoTokenizer, AutoModel, AutoConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from torch.nn.functional import log_softmax
from torch.nn.modules.container import ModuleList
from .configuration_custom4 import CustomConfig4
class CustomModel4(PreTrainedModel):
config_class = CustomConfig4
def __init__(self, config):
super().__init__(config)
def forward(self, *args, labels=None, **kwargs):
loss = None
logits = None
for model, coeff in zip(self.models, self.coeffs):
logp = log_softmax(model.forward(*args, **kwargs).logits, dim=-1)
logits = coeff * logp if logits is None else logits + coeff * logp
# The rest copied from modeling_llama.py:
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
return CausalLMOutputWithPast(loss=loss, logits=logits)
@classmethod
def combine_models(cls, *args, coeffs = [], **kwargs):
models = []
for model in args:
models.append(AutoModelForCausalLM.from_pretrained(model, **kwargs).eval())
if coeffs == []:
coeffs = [1/len(args)] * len(args)
m = cls(models[0].config)
m.models = ModuleList(models)
m.coeffs = coeffs
return m
CustomConfig4.register_for_auto_class()
CustomModel4.register_for_auto_class('AutoModelForCausalLM')
CustomModel4.register_for_auto_class('AutoModel')
AutoConfig.register("custom4", CustomConfig4)
AutoModel.register(CustomConfig4, CustomModel4)
AutoModelForCausalLM.register(CustomConfig4, CustomModel4)
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