llama_bidirectional_model.py

from typing import List, Optional, Tuple, Union

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 (
    LlamaForSequenceClassification,
    LlamaModel,
    LlamaPreTrainedModel,
)
from transformers.utils import logging

from .pooling import pool

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


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,
        )