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import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.cache_utils import Cache, HybridCache
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.models.llama.configuration_llama import LlamaConfig
from transformers.models.llama.modeling_llama import (
LlamaModel,
LlamaPreTrainedModel,
)
from transformers.utils import logging
logger = logging.get_logger(__name__)
class LlamaBidirectionalConfig(LlamaConfig):
model_type = "llama_bidirec"
def __init__(
self, pooling="avg", temperature=1.0, **kwargs,
):
self.pooling = pooling
self.temperature = temperature
super().__init__(**kwargs,)
class LlamaBidirectionalModel(LlamaModel):
config_class = LlamaBidirectionalConfig
def __init__(self, config: LlamaConfig):
super().__init__(config)
for layer in self.layers:
layer.self_attn.is_causal = False
self.config._attn_implementation = "eager"
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
# Generates bi-directional attention.
causal_mask = _prepare_4d_attention_mask(attention_mask, input_tensor.dtype)
return causal_mask
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