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import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from common.utils import HiddenData
from model.decoder.interaction import BaseInteraction
class DCANetInteraction(BaseInteraction):
def __init__(self, **config):
super().__init__(**config)
self.I_S_Emb = Label_Attention()
self.T_block1 = I_S_Block(self.config["input_dim"], self.config["attention_dropout"], self.config["num_attention_heads"])
self.T_block2 = I_S_Block(self.config["input_dim"], self.config["attention_dropout"], self.config["num_attention_heads"])
def forward(self, encode_hidden: HiddenData, **kwargs):
mask = encode_hidden.inputs.attention_mask
H = encode_hidden.slot_hidden
H_I, H_S = self.I_S_Emb(H, H, kwargs["intent_emb"], kwargs["slot_emb"])
H_I, H_S = self.T_block1(H_I + H, H_S + H, mask)
H_I_1, H_S_1 = self.I_S_Emb(H_I, H_S, kwargs["intent_emb"], kwargs["slot_emb"])
H_I, H_S = self.T_block2(H_I + H_I_1, H_S + H_S_1, mask)
encode_hidden.update_intent_hidden_state(F.max_pool1d((H_I + H).transpose(1, 2), H_I.size(1)).squeeze(2))
encode_hidden.update_slot_hidden_state(H_S + H)
return encode_hidden
class Label_Attention(nn.Module):
def __init__(self):
super(Label_Attention, self).__init__()
def forward(self, input_intent, input_slot, intent_emb, slot_emb):
self.W_intent_emb = intent_emb.intent_classifier.weight
self.W_slot_emb = slot_emb.slot_classifier.weight
intent_score = torch.matmul(input_intent, self.W_intent_emb.t())
slot_score = torch.matmul(input_slot, self.W_slot_emb.t())
intent_probs = nn.Softmax(dim=-1)(intent_score)
slot_probs = nn.Softmax(dim=-1)(slot_score)
intent_res = torch.matmul(intent_probs, self.W_intent_emb)
slot_res = torch.matmul(slot_probs, self.W_slot_emb)
return intent_res, slot_res
class I_S_Block(nn.Module):
def __init__(self, hidden_size, attention_dropout, num_attention_heads):
super(I_S_Block, self).__init__()
self.I_S_Attention = I_S_SelfAttention(hidden_size, 2 * hidden_size, hidden_size, attention_dropout, num_attention_heads)
self.I_Out = SelfOutput(hidden_size, attention_dropout)
self.S_Out = SelfOutput(hidden_size, attention_dropout)
self.I_S_Feed_forward = Intermediate_I_S(hidden_size, hidden_size, attention_dropout)
def forward(self, H_intent_input, H_slot_input, mask):
H_slot, H_intent = self.I_S_Attention(H_intent_input, H_slot_input, mask)
H_slot = self.S_Out(H_slot, H_slot_input)
H_intent = self.I_Out(H_intent, H_intent_input)
H_intent, H_slot = self.I_S_Feed_forward(H_intent, H_slot)
return H_intent, H_slot
class I_S_SelfAttention(nn.Module):
def __init__(self, input_size, hidden_size, out_size, attention_dropout, num_attention_heads):
super(I_S_SelfAttention, self).__init__()
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(hidden_size / self.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.out_size = out_size
self.query = nn.Linear(input_size, self.all_head_size)
self.query_slot = nn.Linear(input_size, self.all_head_size)
self.key = nn.Linear(input_size, self.all_head_size)
self.key_slot = nn.Linear(input_size, self.all_head_size)
self.value = nn.Linear(input_size, self.out_size)
self.value_slot = nn.Linear(input_size, self.out_size)
self.dropout = nn.Dropout(attention_dropout)
def transpose_for_scores(self, x):
last_dim = int(x.size()[-1] / self.num_attention_heads)
new_x_shape = x.size()[:-1] + (self.num_attention_heads, last_dim)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, intent, slot, mask):
extended_attention_mask = mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - extended_attention_mask) * -10000.0
mixed_query_layer = self.query(intent)
mixed_key_layer = self.key(slot)
mixed_value_layer = self.value(slot)
mixed_query_layer_slot = self.query_slot(slot)
mixed_key_layer_slot = self.key_slot(intent)
mixed_value_layer_slot = self.value_slot(intent)
query_layer = self.transpose_for_scores(mixed_query_layer)
query_layer_slot = self.transpose_for_scores(mixed_query_layer_slot)
key_layer = self.transpose_for_scores(mixed_key_layer)
key_layer_slot = self.transpose_for_scores(mixed_key_layer_slot)
value_layer = self.transpose_for_scores(mixed_value_layer)
value_layer_slot = self.transpose_for_scores(mixed_value_layer_slot)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# attention_scores_slot = torch.matmul(query_slot, key_slot.transpose(1,0))
attention_scores_slot = torch.matmul(query_layer_slot, key_layer_slot.transpose(-1, -2))
attention_scores_slot = attention_scores_slot / math.sqrt(self.attention_head_size)
attention_scores_intent = attention_scores + attention_mask
attention_scores_slot = attention_scores_slot + attention_mask
# Normalize the attention scores to probabilities.
attention_probs_slot = nn.Softmax(dim=-1)(attention_scores_slot)
attention_probs_intent = nn.Softmax(dim=-1)(attention_scores_intent)
attention_probs_slot = self.dropout(attention_probs_slot)
attention_probs_intent = self.dropout(attention_probs_intent)
context_layer_slot = torch.matmul(attention_probs_slot, value_layer_slot)
context_layer_intent = torch.matmul(attention_probs_intent, value_layer)
context_layer = context_layer_slot.permute(0, 2, 1, 3).contiguous()
context_layer_intent = context_layer_intent.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.out_size,)
new_context_layer_shape_intent = context_layer_intent.size()[:-2] + (self.out_size,)
context_layer = context_layer.view(*new_context_layer_shape)
context_layer_intent = context_layer_intent.view(*new_context_layer_shape_intent)
return context_layer, context_layer_intent
class SelfOutput(nn.Module):
def __init__(self, hidden_size, hidden_dropout_prob):
super(SelfOutput, self).__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = LayerNorm(hidden_size, eps=1e-12)
self.dropout = nn.Dropout(hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class Intermediate_I_S(nn.Module):
def __init__(self, intermediate_size, hidden_size, attention_dropout):
super(Intermediate_I_S, self).__init__()
self.dense_in = nn.Linear(hidden_size * 6, intermediate_size)
self.intermediate_act_fn = nn.ReLU()
self.dense_out = nn.Linear(intermediate_size, hidden_size)
self.LayerNorm_I = LayerNorm(hidden_size, eps=1e-12)
self.LayerNorm_S = LayerNorm(hidden_size, eps=1e-12)
self.dropout = nn.Dropout(attention_dropout)
def forward(self, hidden_states_I, hidden_states_S):
hidden_states_in = torch.cat([hidden_states_I, hidden_states_S], dim=2)
batch_size, max_length, hidden_size = hidden_states_in.size()
h_pad = torch.zeros(batch_size, 1, hidden_size).to(hidden_states_I.device)
h_left = torch.cat([h_pad, hidden_states_in[:, :max_length - 1, :]], dim=1)
h_right = torch.cat([hidden_states_in[:, 1:, :], h_pad], dim=1)
hidden_states_in = torch.cat([hidden_states_in, h_left, h_right], dim=2)
hidden_states = self.dense_in(hidden_states_in)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.dense_out(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states_I_NEW = self.LayerNorm_I(hidden_states + hidden_states_I)
hidden_states_S_NEW = self.LayerNorm_S(hidden_states + hidden_states_S)
return hidden_states_I_NEW, hidden_states_S_NEW
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