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from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.nn import init
from copy import deepcopy
import numpy as np
ACTIVATION_POOL = ['relu', 'gelu']
def _get_activation_fn(activation):
if activation in ACTIVATION_POOL:
return getattr(F, activation)
raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
class TreeEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward, dropout, max_role_count,
activation='gelu', batch_first=False, val_position=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
self._val_position = val_position
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.InstanceNorm1d(d_model)
self.norm2 = nn.InstanceNorm1d(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.position_embedding = nn.Embedding(max_role_count, d_model)
self.activation = _get_activation_fn(activation)
def forward(self, src, src_mask=None, pos_ids=None):
"""
:param src: concatenation of task embeddings and representation for left and right.
src shape: (task_embeddings + left + right, batch_size, dim)
:param src_mask:
:param pos_ids:
:return:
"""
if len(pos_ids.shape) == 1:
sz = src.shape[1] # sz: batch_size
pos_ids = pos_ids.unsqueeze(1).expand(-1, sz) # (3, batch_size)
position_embedding = self.position_embedding(pos_ids)
src2 = self.self_attn(src + position_embedding, src + position_embedding, src,
attn_mask=src_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
class InsideEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.const_size = config.const_size
self.left_linear = nn.Sequential(nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.const_size))
self.right_linear = nn.Sequential(nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.const_size))
# self.const_linear = nn.Sequential(GroupLinear(2, config.hidden_size, config.hidden_size),
# nn.GELU(),
# GroupLinear(2, config.hidden_size, config.hidden_size))
layer = TreeEncoderLayer(config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
max_role_count=config.max_role_embeddings,
dropout=config.attention_probs_dropout_prob,
activation='gelu',
batch_first=True,
val_position=True)
self.norm = nn.InstanceNorm1d(config.hidden_size)
self.layers = nn.ModuleList([layer] + [deepcopy(layer) for _ in range(config.encoder_num_hidden_layers - 1)])
self._device = None
self._pos_ids = None
@property
def device(self):
if self._device is None:
self._device = next(self.parameters()).device
return self._device
@property
def pos_ids(self):
if self._pos_ids is None:
self._pos_ids = torch.arange(2).to(self.device)
return self._pos_ids
def forward(self, src, span_embeds=None):
"""
:param src: [batch_size, comb_size, 2, dim]
:param span_embeds: [batch_size, dim]
:return:
"""
dim = src.shape[-1]
org_shape = src.shape # (batch_size, comb_size, 2, dim)
output = src.view(-1, 2, dim)
# torch.cuda.synchronize()
# with torch.cuda.stream(self.s1):
left_const = self.left_linear(output[:, 0, :])
right_const = self.right_linear(output[:, 1, :])
for mod in self.layers:
output = mod(output, pos_ids=self.pos_ids.unsqueeze(0))
mat_scores = torch.einsum("bi,bi->b", left_const, right_const) / math.sqrt(self.const_size)
mat_scores = mat_scores.view(*org_shape[:-2]) # (batch_size, comb_size)
if span_embeds is not None:
output = output.sum(dim=1).view(*org_shape[:-2], dim) + span_embeds.unsqueeze(1)
else:
output = output.sum(dim=1).view(*org_shape[:-2], dim)
return mat_scores, self.norm(output)
class OutsideEncoder(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.parent_linear = nn.Sequential(nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.const_size))
self.left_linear = nn.Sequential(nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.const_size))
self.right_linear = nn.Sequential(nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.const_size))
# self.const_linear = nn.Sequential(GroupLinear(3, config.hidden_size, config.hidden_size),
# nn.GELU(),
# GroupLinear(3, config.hidden_size, config.hidden_size))
self.const_size = config.const_size
layer = TreeEncoderLayer(config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
max_role_count=config.max_role_embeddings,
dropout=config.attention_probs_dropout_prob,
activation='gelu',
batch_first=True,
val_position=True)
self.norm = nn.InstanceNorm1d(config.hidden_size)
self.layers = nn.ModuleList([layer] + [deepcopy(layer) for _ in range(config.decoder_num_hidden_layers - 1)])
self._device = None
self._dec_pos_ids = None
@property
def device(self):
if self._device is None:
self._device = next(self.parameters()).device
return self._device
@property
def dec_pos_ids(self):
if self._dec_pos_ids is None:
self._dec_pos_ids = torch.tensor([[0, 1], [0, 2]], device=self.device)
return self._dec_pos_ids
def forward(self, parent_ij, child_ikj, parent_scores=None, child_scores=None):
"""
:param parent_ij: (batch_size, dim)
:param parent_scores: (batch_size, 1)
:param child_ikj: (batch_size, comb_size, 2, dim)
:param child_scores: (batch_size, comb_size, 2)
:return: (batch_size, 2), (batch_size, 2, dim)
"""
# p_l = parent_ij @ self.W_outside_r # (batch_size, dim)
# out_score_ik = torch.einsum('bd, bcd->bc', p_l, child_ikj[:, :, 1, :]) # (batch_size, comb_size)
# out_score_ik = out_score_ik + parent_scores + child_scores[:, :, 1, 0] # (batch_size, comb_size)
# p_r = parent_ij @ self.W_outside_l # (batch_size, dim)
# out_score_kj = torch.einsum('bd, bcd->bc', p_r, child_ikj[:, :, 0, :]) # (batch_size, comb_size)
# out_score_kj = out_score_kj + parent_scores + child_scores[:, :, 0, 0] # (batch_size, comb_size)
batch_size = child_ikj.shape[0]
comb_size = child_ikj.shape[1]
comb_size = child_ikj.shape[1]
parent_ij_ext = parent_ij.unsqueeze(1).unsqueeze(2).repeat(1, comb_size, 2, 1) # (batch_size, comb_size, 2, dim)
inputs = torch.stack([parent_ij_ext, child_ikj.flip([2])], dim=3) # (batch_size, comb_size, 2, 2, dim)
inputs = inputs.view(batch_size * comb_size * 2, 2, -1)
outside_scores = None
if parent_scores is not None and child_scores is not None:
parent_const = self.parent_linear(inputs[:, 0, :])
right_child_const = self.right_linear(inputs[::2, 1, :])
left_child_const = self.left_linear(inputs[1::2, 1, :])
# self.dec_pos_ids: (2, 2)
pos_ids = self.dec_pos_ids.repeat(batch_size * comb_size, 1)
for mod in self.layers:
inputs = mod(inputs, pos_ids=pos_ids)
if parent_scores is not None and child_scores is not None:
parent_const_r = parent_const[::2, :]
parent_const_l = parent_const[1::2, :]
left_score = (parent_const_r * right_child_const).sum(dim=-1) / math.sqrt(self.const_size)
# right_score = torch.einsum('bi,bi->b', parent_const_l, left_child_const) / math.sqrt(self.const_size)
right_score = (parent_const_l * left_child_const).sum(dim=-1) / math.sqrt(self.const_size)
left_score = left_score.view(batch_size, comb_size)
right_score = right_score.view(batch_size, comb_size)
out_score_ik = left_score
out_score_kj = right_score
outside_scores = torch.stack([out_score_ik, out_score_kj], dim=2)
# inputs: (?, 2, dim)
out_e_ij = self.norm(inputs.sum(dim=1))
return outside_scores, \
out_e_ij.view(batch_size, comb_size, 2, -1) # (batch_size, comb_size, 2, dim) |