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98a9b8e | 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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import torch
from transformers.cache_utils import Cache
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
from transformers.models.llama.configuration_llama import LlamaConfig
from transformers.models.llama.modeling_llama import LlamaModel
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
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
assert self.config._attn_implementation in [
"flash_attention_2",
"eager",
], (
f"Unsupported attention implementation: "
f"{self.config._attn_implementation}, "
f"only support flash_attention_2 or eager"
)
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
elif self.config._attn_implementation == "eager":
# Generates bi-directional attention.
causal_mask = _prepare_4d_attention_mask(
attention_mask,
dtype=input_tensor.dtype,
)
return causal_mask
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