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import math
import os
from typing import Optional, List, Union, Tuple
import torch
from loguru import logger
from torch import nn
from torch.nn import functional as F, CrossEntropyLoss
from torch_geometric.nn import RGCNConv
from transformers import BartPretrainedModel, BartConfig, BartModel
from transformers.modeling_outputs import Seq2SeqLMOutput
import sys
sys.path.append("..")
from src.model.utils import SelfAttention, shift_tokens_right
class KBRDforRec(nn.Module):
def __init__(self, hidden_size, num_relations, num_bases, num_entities):
super(KBRDforRec, self).__init__()
# kg encoder
self.kg_encoder = RGCNConv(
hidden_size, hidden_size, num_relations=num_relations, num_bases=num_bases
)
self.node_embeds = nn.Parameter(torch.empty(num_entities, hidden_size))
stdv = math.sqrt(6.0 / (self.node_embeds.size(-2) + self.node_embeds.size(-1)))
self.node_embeds.data.uniform_(-stdv, stdv)
self.special_token_embeddings = nn.Parameter(
torch.zeros(1, hidden_size), requires_grad=False
)
self.attn = SelfAttention(hidden_size)
def get_node_embeds(self, edge_index, edge_type):
node_embeds = self.kg_encoder(self.node_embeds, edge_index, edge_type)
node_embeds = torch.cat([node_embeds, self.special_token_embeddings], dim=0)
return node_embeds
def forward(
self,
entity_embeds=None,
entity_ids=None,
edge_index=None,
edge_type=None,
node_embeds=None,
entity_mask=None,
labels=None,
reduction="none",
):
if node_embeds is None:
node_embeds = self.get_node_embeds(edge_index, edge_type)
if entity_embeds is None:
entity_embeds = node_embeds[entity_ids] # (bs, seq_len, hs)
user_embeds = self.attn(entity_embeds, entity_mask)
logits = user_embeds @ node_embeds.T # (bs, n_node)
loss = None
if labels is not None:
loss = F.cross_entropy(logits, labels, reduction=reduction)
return {"loss": loss, "logit": logits, "user_embeds": user_embeds}
def save(self, save_dir):
os.makedirs(save_dir, exist_ok=True)
save_path = os.path.join(save_dir, "model.pt")
torch.save(self.state_dict(), save_path)
def load(self, load_dir):
load_path = os.path.join(load_dir, "model.pt")
missing_keys, unexpected_keys = self.load_state_dict(
torch.load(load_path, map_location=torch.device("cpu"))
)
class KBRDforConv(BartPretrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head.weight"]
def __init__(self, config: BartConfig, user_hidden_size):
super().__init__(config)
self.model = BartModel(config)
self.register_buffer(
"final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))
)
self.lm_head = nn.Linear(
config.d_model, self.model.shared.num_embeddings, bias=False
)
self.rec_proj = nn.Linear(user_hidden_size, self.model.shared.num_embeddings)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros(
(1, new_num_tokens - old_num_tokens),
device=self.final_logits_bias.device,
)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_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,
decoder_user_embeds=None,
) -> Union[Tuple, Seq2SeqLMOutput]:
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if labels is not None:
if use_cache:
logger.warning(
"The `use_cache` argument is changed to `False` since `labels` is provided."
)
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = (
self.lm_head(outputs[0])
+ self.final_logits_bias
+ self.rec_proj(decoder_user_embeds).unsqueeze(1)
)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
lm_logits.view(-1, self.config.vocab_size), labels.view(-1)
)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return (
((masked_lm_loss,) + output) if masked_lm_loss is not None else output
)
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
decoder_user_embeds=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
"decoder_user_embeds": decoder_user_embeds,
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(
past_state.index_select(0, beam_idx)
for past_state in layer_past[:2]
)
+ layer_past[2:],
)
return reordered_past
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