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import math
import os
import torch
from loguru import logger
from torch import nn
from torch.nn import functional as F
from torch_geometric.nn import RGCNConv
class KGPrompt(nn.Module):
def __init__(
self,
hidden_size,
token_hidden_size,
n_head,
n_layer,
n_block,
n_entity,
num_relations,
num_bases,
edge_index,
edge_type,
n_prefix_rec=None,
n_prefix_conv=None,
):
super(KGPrompt, self).__init__()
self.hidden_size = hidden_size
self.n_head = n_head
self.head_dim = hidden_size // n_head
self.n_layer = n_layer
self.n_block = n_block
self.n_prefix_rec = n_prefix_rec
self.n_prefix_conv = n_prefix_conv
entity_hidden_size = hidden_size // 2
self.kg_encoder = RGCNConv(
entity_hidden_size,
entity_hidden_size,
num_relations=num_relations,
num_bases=num_bases,
)
self.node_embeds = nn.Parameter(torch.empty(n_entity, entity_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.edge_index = nn.Parameter(edge_index, requires_grad=False)
self.edge_type = nn.Parameter(edge_type, requires_grad=False)
self.entity_proj1 = nn.Sequential(
nn.Linear(entity_hidden_size, entity_hidden_size // 2),
nn.ReLU(),
nn.Linear(entity_hidden_size // 2, entity_hidden_size),
)
self.entity_proj2 = nn.Linear(entity_hidden_size, hidden_size)
self.token_proj1 = nn.Sequential(
nn.Linear(token_hidden_size, token_hidden_size // 2),
nn.ReLU(),
nn.Linear(token_hidden_size // 2, token_hidden_size),
)
self.token_proj2 = nn.Linear(token_hidden_size, hidden_size)
self.cross_attn = nn.Linear(hidden_size, hidden_size, bias=False)
self.prompt_proj1 = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Linear(hidden_size // 2, hidden_size),
)
self.prompt_proj2 = nn.Linear(hidden_size, n_layer * n_block * hidden_size)
if self.n_prefix_rec is not None:
self.rec_prefix_embeds = nn.Parameter(
torch.empty(n_prefix_rec, hidden_size)
)
nn.init.normal_(self.rec_prefix_embeds)
self.rec_prefix_proj = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Linear(hidden_size // 2, hidden_size),
)
if self.n_prefix_conv is not None:
self.conv_prefix_embeds = nn.Parameter(
torch.empty(n_prefix_conv, hidden_size)
)
nn.init.normal_(self.conv_prefix_embeds)
self.conv_prefix_proj = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Linear(hidden_size // 2, hidden_size),
)
def set_and_fix_node_embed(self, node_embeds: torch.Tensor):
self.node_embeds.data = node_embeds
self.node_embeds.requires_grad_(False)
def get_entity_embeds(self):
node_embeds = self.node_embeds
entity_embeds = (
self.kg_encoder(node_embeds, self.edge_index, self.edge_type) + node_embeds
)
entity_embeds = self.entity_proj1(entity_embeds) + entity_embeds
entity_embeds = self.entity_proj2(entity_embeds)
return entity_embeds
def forward(
self,
entity_ids=None,
token_embeds=None,
output_entity=False,
use_rec_prefix=False,
use_conv_prefix=False,
entity_embeds=None,
):
batch_size, entity_len, token_len = None, None, None
if entity_embeds is not None:
batch_size, entity_len = entity_embeds.shape[:2]
elif entity_ids is not None:
batch_size, entity_len = entity_ids.shape[:2]
entity_embeds = self.get_entity_embeds()
entity_embeds = entity_embeds[
entity_ids
] # (batch_size, entity_len, hidden_size)
if token_embeds is not None:
batch_size, token_len = token_embeds.shape[:2]
token_embeds = (
self.token_proj1(token_embeds) + token_embeds
) # (batch_size, token_len, hidden_size)
token_embeds = self.token_proj2(token_embeds)
if entity_embeds is not None and token_embeds is not None:
attn_weights = self.cross_attn(token_embeds) @ entity_embeds.permute(
0, 2, 1
) # (batch_size, token_len, entity_len)
attn_weights /= self.hidden_size
if output_entity:
token_weights = F.softmax(attn_weights, dim=1).permute(0, 2, 1)
prompt_embeds = token_weights @ token_embeds + entity_embeds
prompt_len = entity_len
else:
entity_weights = F.softmax(attn_weights, dim=2)
prompt_embeds = entity_weights @ entity_embeds + token_embeds
prompt_len = token_len
elif entity_embeds is not None:
prompt_embeds = entity_embeds
prompt_len = entity_len
else:
prompt_embeds = token_embeds
prompt_len = token_len
if self.n_prefix_rec is not None and use_rec_prefix:
prefix_embeds = (
self.rec_prefix_proj(self.rec_prefix_embeds) + self.rec_prefix_embeds
)
prefix_embeds = prefix_embeds.expand(prompt_embeds.shape[0], -1, -1)
prompt_embeds = torch.cat([prefix_embeds, prompt_embeds], dim=1)
prompt_len += self.n_prefix_rec
if self.n_prefix_conv is not None and use_conv_prefix:
prefix_embeds = (
self.conv_prefix_proj(self.conv_prefix_embeds) + self.conv_prefix_embeds
)
prefix_embeds = prefix_embeds.expand(prompt_embeds.shape[0], -1, -1)
prompt_embeds = torch.cat([prefix_embeds, prompt_embeds], dim=1)
prompt_len += self.n_prefix_conv
prompt_embeds = self.prompt_proj1(prompt_embeds) + prompt_embeds
prompt_embeds = self.prompt_proj2(prompt_embeds)
prompt_embeds = prompt_embeds.reshape(
batch_size,
prompt_len,
self.n_layer,
self.n_block,
self.n_head,
self.head_dim,
).permute(
2, 3, 0, 4, 1, 5
) # (n_layer, n_block, batch_size, n_head, prompt_len, head_dim)
return prompt_embeds
def save(self, save_dir):
os.makedirs(save_dir, exist_ok=True)
state_dict = {k: v for k, v in self.state_dict().items() if "edge" not in k}
save_path = os.path.join(save_dir, "model.pt")
torch.save(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")), strict=False
)
logger.info(f"missing_keys: {missing_keys}, unexpected_keys: {unexpected_keys}")
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