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from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import Tensor, 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 (
LlamaForSequenceClassification,
LlamaModel,
LlamaPreTrainedModel,
)
from transformers.utils import logging
logger = logging.get_logger(__name__)
def pool(last_hidden_states: Tensor, attention_mask: Tensor, pool_type: str) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
if pool_type == "avg":
emb = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
elif pool_type == "weighted_avg":
emb = last_hidden.sum(dim=1)
elif pool_type == "cls":
emb = last_hidden[:, 0]
elif pool_type == "last":
left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]
if left_padding:
emb = last_hidden[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden.shape[0]
emb = last_hidden[
torch.arange(batch_size, device=last_hidden.device), sequence_lengths
]
else:
raise ValueError(f"pool_type {pool_type} not supported")
return emb
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
class LlamaBidirectionalForSequenceClassification(LlamaForSequenceClassification):
config_class = LlamaBidirectionalConfig
def __init__(self, config):
super().__init__(config)
# Releasing the parameters of LlamaModel
# created by parent LlamaForSequenceClassification
del self.model
self.model = LlamaBidirectionalModel(config)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[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,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
pooled_hidden_states = pool(
last_hidden_states=hidden_states,
attention_mask=attention_mask,
pool_type=self.config.pooling,
)
pooled_logits = self.score(pooled_hidden_states)
pooled_logits = pooled_logits / self.config.temperature
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (
labels.dtype == torch.long or labels.dtype == torch.int
):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(
pooled_logits.view(-1, self.num_labels), labels.view(-1)
)
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
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