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from typing import Optional, Union
import torch
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.llama.modeling_llama import LlamaModel
from ...cache_utils import Cache
# example where we need some deps and some functions
class SuperModel(LlamaModel):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, CausalLMOutputWithPast]:
out = super().forward(
input_ids,
attention_mask,
position_ids,
past_key_values,
inputs_embeds,
use_cache,
output_attentions,
output_hidden_states,
return_dict,
cache_position,
)
out.logits *= 2**4
return out
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