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import torch
import torch.nn.functional as F
from torch import nn
def create_projection_layer(hidden_size: int, dropout: float, out_dim: int = None) -> nn.Sequential:
"""
Creates a projection layer with specified configurations.
"""
if out_dim is None:
out_dim = hidden_size
return nn.Sequential(
nn.Linear(hidden_size, out_dim * 4),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(out_dim * 4, out_dim)
)
class SpanQuery(nn.Module):
def __init__(self, hidden_size, max_width, trainable=True):
super().__init__()
self.query_seg = nn.Parameter(torch.randn(hidden_size, max_width))
nn.init.uniform_(self.query_seg, a=-1, b=1)
if not trainable:
self.query_seg.requires_grad = False
self.project = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU()
)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
span_rep = torch.einsum('bld, ds->blsd', h, self.query_seg)
return self.project(span_rep)
class SpanMLP(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.mlp = nn.Linear(hidden_size, hidden_size * max_width)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
span_rep = self.mlp(h)
span_rep = span_rep.view(B, L, -1, D)
return span_rep.relu()
class SpanCAT(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.query_seg = nn.Parameter(torch.randn(128, max_width))
self.project = nn.Sequential(
nn.Linear(hidden_size + 128, hidden_size),
nn.ReLU()
)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
h = h.view(B, L, 1, D).repeat(1, 1, self.max_width, 1)
q = self.query_seg.view(1, 1, self.max_width, -1).repeat(B, L, 1, 1)
span_rep = torch.cat([h, q], dim=-1)
span_rep = self.project(span_rep)
return span_rep
class SpanConvBlock(nn.Module):
def __init__(self, hidden_size, kernel_size, span_mode='conv_normal'):
super().__init__()
if span_mode == 'conv_conv':
self.conv = nn.Conv1d(hidden_size, hidden_size,
kernel_size=kernel_size)
# initialize the weights
nn.init.kaiming_uniform_(self.conv.weight, nonlinearity='relu')
elif span_mode == 'conv_max':
self.conv = nn.MaxPool1d(kernel_size=kernel_size, stride=1)
elif span_mode == 'conv_mean' or span_mode == 'conv_sum':
self.conv = nn.AvgPool1d(kernel_size=kernel_size, stride=1)
self.span_mode = span_mode
self.pad = kernel_size - 1
def forward(self, x):
x = torch.einsum('bld->bdl', x)
if self.pad > 0:
x = F.pad(x, (0, self.pad), "constant", 0)
x = self.conv(x)
if self.span_mode == "conv_sum":
x = x * (self.pad + 1)
return torch.einsum('bdl->bld', x)
class SpanConv(nn.Module):
def __init__(self, hidden_size, max_width, span_mode):
super().__init__()
kernels = [i + 2 for i in range(max_width - 1)]
self.convs = nn.ModuleList()
for kernel in kernels:
self.convs.append(SpanConvBlock(hidden_size, kernel, span_mode))
self.project = nn.Sequential(
nn.ReLU(),
nn.Linear(hidden_size, hidden_size)
)
def forward(self, x, *args):
span_reps = [x]
for conv in self.convs:
h = conv(x)
span_reps.append(h)
span_reps = torch.stack(span_reps, dim=-2)
return self.project(span_reps)
class SpanEndpointsBlock(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
def forward(self, x):
B, L, D = x.size()
span_idx = torch.LongTensor(
[[i, i + self.kernel_size - 1] for i in range(L)]).to(x.device)
x = F.pad(x, (0, 0, 0, self.kernel_size - 1), "constant", 0)
# endrep
start_end_rep = torch.index_select(x, dim=1, index=span_idx.view(-1))
start_end_rep = start_end_rep.view(B, L, 2, D)
return start_end_rep
class ConvShare(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.conv_weigth = nn.Parameter(
torch.randn(hidden_size, hidden_size, max_width))
nn.init.kaiming_uniform_(self.conv_weigth, nonlinearity='relu')
self.project = nn.Sequential(
nn.ReLU(),
nn.Linear(hidden_size, hidden_size)
)
def forward(self, x, *args):
span_reps = []
x = torch.einsum('bld->bdl', x)
for i in range(self.max_width):
pad = i
x_i = F.pad(x, (0, pad), "constant", 0)
conv_w = self.conv_weigth[:, :, :i + 1]
out_i = F.conv1d(x_i, conv_w)
span_reps.append(out_i.transpose(-1, -2))
out = torch.stack(span_reps, dim=-2)
return self.project(out)
def extract_elements(sequence, indices):
B, L, D = sequence.shape
K = indices.shape[1]
# Expand indices to [B, K, D]
expanded_indices = indices.unsqueeze(2).expand(-1, -1, D)
# Gather the elements
extracted_elements = torch.gather(sequence, 1, expanded_indices)
return extracted_elements
class SpanMarker(nn.Module):
def __init__(self, hidden_size, max_width, dropout=0.4):
super().__init__()
self.max_width = max_width
self.project_start = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 2, bias=True),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size * 2, hidden_size, bias=True),
)
self.project_end = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 2, bias=True),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size * 2, hidden_size, bias=True),
)
self.out_project = nn.Linear(hidden_size * 2, hidden_size, bias=True)
def forward(self, h, span_idx):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
# project start and end
start_rep = self.project_start(h)
end_rep = self.project_end(h)
start_span_rep = extract_elements(start_rep, span_idx[:, :, 0])
end_span_rep = extract_elements(end_rep, span_idx[:, :, 1])
# concat start and end
cat = torch.cat([start_span_rep, end_span_rep], dim=-1).relu()
# project
cat = self.out_project(cat)
# reshape
return cat.view(B, L, self.max_width, D)
class SpanMarkerV0(nn.Module):
"""
Marks and projects span endpoints using an MLP.
"""
def __init__(self, hidden_size: int, max_width: int, dropout: float = 0.4):
super().__init__()
self.max_width = max_width
self.project_start = create_projection_layer(hidden_size, dropout)
self.project_end = create_projection_layer(hidden_size, dropout)
self.out_project = create_projection_layer(hidden_size * 2, dropout, hidden_size)
def forward(self, h: torch.Tensor, span_idx: torch.Tensor) -> torch.Tensor:
B, L, D = h.size()
start_rep = self.project_start(h)
end_rep = self.project_end(h)
start_span_rep = extract_elements(start_rep, span_idx[:, :, 0])
end_span_rep = extract_elements(end_rep, span_idx[:, :, 1])
cat = torch.cat([start_span_rep, end_span_rep], dim=-1).relu()
return self.out_project(cat).view(B, L, self.max_width, D)
class ConvShareV2(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.conv_weigth = nn.Parameter(
torch.randn(hidden_size, hidden_size, max_width)
)
nn.init.xavier_normal_(self.conv_weigth)
def forward(self, x, *args):
span_reps = []
x = torch.einsum('bld->bdl', x)
for i in range(self.max_width):
pad = i
x_i = F.pad(x, (0, pad), "constant", 0)
conv_w = self.conv_weigth[:, :, :i + 1]
out_i = F.conv1d(x_i, conv_w)
span_reps.append(out_i.transpose(-1, -2))
out = torch.stack(span_reps, dim=-2)
return out
class SpanRepLayer(nn.Module):
"""
Various span representation approaches
"""
def __init__(self, hidden_size, max_width, span_mode, **kwargs):
super().__init__()
if span_mode == 'marker':
self.span_rep_layer = SpanMarker(hidden_size, max_width, **kwargs)
elif span_mode == 'markerV0':
self.span_rep_layer = SpanMarkerV0(hidden_size, max_width, **kwargs)
elif span_mode == 'query':
self.span_rep_layer = SpanQuery(
hidden_size, max_width, trainable=True)
elif span_mode == 'mlp':
self.span_rep_layer = SpanMLP(hidden_size, max_width)
elif span_mode == 'cat':
self.span_rep_layer = SpanCAT(hidden_size, max_width)
elif span_mode == 'conv_conv':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_conv')
elif span_mode == 'conv_max':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_max')
elif span_mode == 'conv_mean':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_mean')
elif span_mode == 'conv_sum':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_sum')
elif span_mode == 'conv_share':
self.span_rep_layer = ConvShare(hidden_size, max_width)
else:
raise ValueError(f'Unknown span mode {span_mode}')
def forward(self, x, *args):
return self.span_rep_layer(x, *args)
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