import math
from typing import List, Optional, Tuple, Union
import dependency_decoding
import ftfy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import checkpoint
from transformers.modeling_utils import PreTrainedModel
from transformers.activations import gelu_new
from transformers.modeling_outputs import (
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
BaseModelOutput
)
from transformers.pytorch_utils import softmax_backward_data
from transformers.configuration_utils import PretrainedConfig
from dataset import Dataset
class NorbertConfig(PretrainedConfig):
"""Configuration class to store the configuration of a `NorbertModel`.
"""
def __init__(
self,
vocab_size=50000,
attention_probs_dropout_prob=0.1,
hidden_dropout_prob=0.1,
hidden_size=768,
intermediate_size=2048,
max_position_embeddings=512,
position_bucket_size=32,
num_attention_heads=12,
num_hidden_layers=12,
layer_norm_eps=1.0e-7,
output_all_encoded_layers=True,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.output_all_encoded_layers = output_all_encoded_layers
self.position_bucket_size = position_bucket_size
self.layer_norm_eps = layer_norm_eps
class Encoder(nn.Module):
def __init__(self, config, activation_checkpointing=False):
super().__init__()
self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)])
for i, layer in enumerate(self.layers):
layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
self.activation_checkpointing = activation_checkpointing
def forward(self, hidden_states, attention_mask, relative_embedding):
hidden_states, attention_probs = [hidden_states], []
for layer in self.layers:
if self.activation_checkpointing:
hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding)
else:
hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding)
hidden_states.append(hidden_state)
attention_probs.append(attention_p)
return hidden_states, attention_probs
class MaskClassifier(nn.Module):
def __init__(self, config, subword_embedding):
super().__init__()
self.nonlinearity = nn.Sequential(
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(subword_embedding.size(1), subword_embedding.size(0))
)
self.initialize(config.hidden_size, subword_embedding)
def initialize(self, hidden_size, embedding):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.nonlinearity[-1].weight = embedding
self.nonlinearity[1].bias.data.zero_()
self.nonlinearity[-1].bias.data.zero_()
def forward(self, x, masked_lm_labels=None):
if masked_lm_labels is not None:
x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze())
x = self.nonlinearity(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = Attention(config)
self.mlp = FeedForward(config)
def forward(self, x, padding_mask, relative_embedding):
attention_output, attention_probs = self.attention(x, padding_mask, relative_embedding)
x = x + attention_output
x = x + self.mlp(x)
return x, attention_probs
class GeGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
x = x * gelu_new(gate)
return x
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.mlp = nn.Sequential(
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
GeGLU(),
nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
nn.Dropout(config.hidden_dropout_prob)
)
self.initialize(config.hidden_size)
def initialize(self, hidden_size):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std)
def forward(self, x):
return self.mlp(x)
class MaskedSoftmax(torch.autograd.Function):
@staticmethod
def forward(self, x, mask, dim):
self.dim = dim
x.masked_fill_(mask, float('-inf'))
x = torch.softmax(x, self.dim)
x.masked_fill_(mask, 0.0)
self.save_for_backward(x)
return x
@staticmethod
def backward(self, grad_output):
output, = self.saved_tensors
input_grad = softmax_backward_data(self, grad_output, output, self.dim, output)
return input_grad, None, None
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}")
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_size = config.hidden_size // config.num_attention_heads
self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
position_indices = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \
- torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0)
position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings)
position_indices = config.position_bucket_size - 1 + position_indices
self.register_buffer("position_indices", position_indices, persistent=True)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.scale = 1.0 / math.sqrt(3 * self.head_size)
self.initialize()
def make_log_bucket_position(self, relative_pos, bucket_size, max_position):
sign = torch.sign(relative_pos)
mid = bucket_size // 2
abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
return bucket_pos
def initialize(self):
std = math.sqrt(2.0 / (5.0 * self.hidden_size))
nn.init.trunc_normal_(self.in_proj_qk.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.in_proj_qk.bias.data.zero_()
self.in_proj_v.bias.data.zero_()
self.out_proj.bias.data.zero_()
def compute_attention_scores(self, hidden_states, relative_embedding):
key_len, batch_size, _ = hidden_states.size()
query_len = key_len
if self.position_indices.size(0) < query_len:
position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
- torch.arange(query_len, dtype=torch.long).unsqueeze(0)
position_indices = self.make_log_bucket_position(position_indices, self.position_bucket_size, 512)
position_indices = self.position_bucket_size - 1 + position_indices
self.position_indices = position_indices.to(hidden_states.device)
hidden_states = self.pre_layer_norm(hidden_states)
query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2) # shape: [T, B, D]
value = self.in_proj_v(hidden_states) # shape: [T, B, D]
query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale)
pos = self.in_proj_qk(self.dropout(relative_embedding)) # shape: [2T-1, 2D]
query_pos, key_pos = pos.view(-1, self.num_heads, 2*self.head_size).chunk(2, dim=2)
query = query.view(batch_size, self.num_heads, query_len, self.head_size)
key = key.view(batch_size, self.num_heads, query_len, self.head_size)
attention_c_p = torch.einsum("bhqd,khd->bhqk", query, key_pos.squeeze(1) * self.scale)
attention_p_c = torch.einsum("bhkd,qhd->bhqk", key * self.scale, query_pos.squeeze(1))
position_indices = self.position_indices[:query_len, :key_len].expand(batch_size, self.num_heads, -1, -1)
attention_c_p = attention_c_p.gather(3, position_indices)
attention_p_c = attention_p_c.gather(2, position_indices)
attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len)
attention_scores.add_(attention_c_p)
attention_scores.add_(attention_p_c)
return attention_scores, value
def compute_output(self, attention_probs, value):
attention_probs = self.dropout(attention_probs)
context = torch.bmm(attention_probs.flatten(0, 1), value) # shape: [B*H, Q, D]
context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size) # shape: [Q, B, H*D]
context = self.out_proj(context)
context = self.post_layer_norm(context)
context = self.dropout(context)
return context
def forward(self, hidden_states, attention_mask, relative_embedding):
attention_scores, value = self.compute_attention_scores(hidden_states, relative_embedding)
attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
return self.compute_output(attention_probs, value), attention_probs.detach()
class Embedding(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.initialize()
def initialize(self):
std = math.sqrt(2.0 / (5.0 * self.hidden_size))
nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
def forward(self, input_ids):
word_embedding = self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
relative_embeddings = self.relative_layer_norm(self.relative_embedding)
return word_embedding, relative_embeddings
#
# HuggingFace wrappers
#
class NorbertPreTrainedModel(PreTrainedModel):
config_class = NorbertConfig
base_model_prefix = "norbert3"
supports_gradient_checkpointing = True
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, Encoder):
module.activation_checkpointing = value
def _init_weights(self, module):
pass # everything is already initialized
class NorbertModel(NorbertPreTrainedModel):
def __init__(self, config, add_mlm_layer=False, gradient_checkpointing=False, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.embedding = Embedding(config)
self.transformer = Encoder(config, activation_checkpointing=gradient_checkpointing)
self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None
def get_input_embeddings(self):
return self.embedding.word_embedding
def set_input_embeddings(self, value):
self.embedding.word_embedding = value
def get_contextualized_embeddings(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None
) -> List[torch.Tensor]:
if input_ids is not None:
input_shape = input_ids.size()
else:
raise ValueError("You have to specify input_ids")
batch_size, seq_length = input_shape
device = input_ids.device
if attention_mask is None:
attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
else:
attention_mask = ~attention_mask.bool()
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
static_embeddings, relative_embedding = self.embedding(input_ids.t())
contextualized_embeddings, attention_probs = self.transformer(static_embeddings, attention_mask, relative_embedding)
contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
last_layer = contextualized_embeddings[-1]
contextualized_embeddings = [contextualized_embeddings[0]] + [
contextualized_embeddings[i] - contextualized_embeddings[i - 1]
for i in range(1, len(contextualized_embeddings))
]
return last_layer, contextualized_embeddings, attention_probs
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
if not return_dict:
return (
sequence_output,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class Classifier(nn.Module):
def __init__(self, hidden_size, vocab_size, dropout):
super().__init__()
self.transform = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.GELU(),
nn.LayerNorm(hidden_size, elementwise_affine=False),
nn.Dropout(dropout),
nn.Linear(hidden_size, vocab_size)
)
self.initialize(hidden_size)
def initialize(self, hidden_size):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.transform[0].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.transform[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.transform[0].bias.data.zero_()
self.transform[-1].bias.data.zero_()
def forward(self, x):
return self.transform(x)
class ZeroClassifier(nn.Module):
def forward(self, x):
output = torch.zeros(x.size(0), x.size(1), 2, device=x.device, dtype=x.dtype)
output[:, :, 0] = 1.0
output[:, :, 1] = -1.0
return output
class EdgeClassifier(nn.Module):
def __init__(self, hidden_size, dep_hidden_size, vocab_size, dropout):
super().__init__()
self.head_dep_transform = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.GELU(),
nn.LayerNorm(hidden_size, elementwise_affine=False),
nn.Dropout(dropout)
)
self.head_root_transform = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.GELU(),
nn.LayerNorm(hidden_size, elementwise_affine=False),
nn.Dropout(dropout)
)
self.head_bilinear = nn.Parameter(torch.zeros(hidden_size, hidden_size))
self.head_linear_dep = nn.Linear(hidden_size, 1, bias=False)
self.head_linear_root = nn.Linear(hidden_size, 1, bias=False)
self.head_bias = nn.Parameter(torch.zeros(1))
self.dep_dep_transform = nn.Sequential(
nn.Linear(hidden_size, dep_hidden_size),
nn.GELU(),
nn.LayerNorm(dep_hidden_size, elementwise_affine=False),
nn.Dropout(dropout)
)
self.dep_root_transform = nn.Sequential(
nn.Linear(hidden_size, dep_hidden_size),
nn.GELU(),
nn.LayerNorm(dep_hidden_size, elementwise_affine=False),
nn.Dropout(dropout)
)
self.dep_bilinear = nn.Parameter(torch.zeros(dep_hidden_size, dep_hidden_size, vocab_size))
self.dep_linear_dep = nn.Linear(dep_hidden_size, vocab_size, bias=False)
self.dep_linear_root = nn.Linear(dep_hidden_size, vocab_size, bias=False)
self.dep_bias = nn.Parameter(torch.zeros(vocab_size))
self.hidden_size = hidden_size
self.dep_hidden_size = dep_hidden_size
self.mask_value = float("-inf")
self.initialize(hidden_size)
def initialize(self, hidden_size):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.head_dep_transform[0].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.head_root_transform[0].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.dep_dep_transform[0].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.dep_root_transform[0].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.head_linear_dep.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.head_linear_root.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.dep_linear_dep.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.dep_linear_root.weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.head_dep_transform[0].bias.data.zero_()
self.head_root_transform[0].bias.data.zero_()
self.dep_dep_transform[0].bias.data.zero_()
self.dep_root_transform[0].bias.data.zero_()
def forward(self, head_x, dep_x, lengths, head_gold=None):
head_dep = self.head_dep_transform(head_x[:, 1:, :])
head_root = self.head_root_transform(head_x)
head_prediction = torch.einsum("bkn,nm,blm->bkl", head_dep, self.head_bilinear, head_root / math.sqrt(self.hidden_size)) \
+ self.head_linear_dep(head_dep) + self.head_linear_root(head_root).transpose(1, 2) + self.head_bias
mask = (torch.arange(head_x.size(1)).unsqueeze(0) >= lengths.unsqueeze(1)).unsqueeze(1).to(head_x.device)
mask = mask | (torch.ones(head_x.size(1) - 1, head_x.size(1), dtype=torch.bool, device=head_x.device).tril(1) & torch.ones(head_x.size(1) - 1, head_x.size(1), dtype=torch.bool, device=head_x.device).triu(1))
head_prediction = head_prediction.masked_fill(mask, self.mask_value)
if head_gold is None:
head_logp = torch.log_softmax(head_prediction, dim=-1)
head_logp = F.pad(head_logp, (0, 0, 1, 0), value=torch.nan).cpu()
head_gold = []
for i, length in enumerate(lengths.tolist()):
head = self.max_spanning_tree(head_logp[i, :length, :length])
head = head + ((head_x.size(1) - 1) - len(head)) * [0]
head_gold.append(torch.tensor(head))
head_gold = torch.stack(head_gold).to(head_x.device)
dep_dep = self.dep_dep_transform(dep_x[:, 1:])
dep_root = dep_x.gather(1, head_gold.unsqueeze(-1).expand(-1, -1, dep_x.size(-1)).clamp(min=0))
dep_root = self.dep_root_transform(dep_root)
dep_prediction = torch.einsum("btm,mnl,btn->btl", dep_dep, self.dep_bilinear, dep_root / math.sqrt(self.dep_hidden_size)) \
+ self.dep_linear_dep(dep_dep) + self.dep_linear_root(dep_root) + self.dep_bias
return head_prediction, dep_prediction, head_gold
def max_spanning_tree(self, weight_matrix):
weight_matrix = weight_matrix.clone()
# weight_matrix[:, 0] = torch.nan
# we need to make sure that the root is the parent of a single node
# first, we try to use the default weights, it should work in most cases
parents, _ = dependency_decoding.chu_liu_edmonds(weight_matrix.numpy().astype(float))
assert parents[0] == -1, f"{parents}\n{weight_matrix}"
parents = parents[1:]
# check if the root is the parent of a single node
if parents.count(0) == 1:
return parents
# if not, we need to modify the weights and try all possibilities
# we try to find the node that is the parent of the root
best_score = float("-inf")
best_parents = None
for i in range(len(parents)):
weight_matrix_mod = weight_matrix.clone()
weight_matrix_mod[:i+1, 0] = torch.nan
weight_matrix_mod[i+2:, 0] = torch.nan
parents, score = dependency_decoding.chu_liu_edmonds(weight_matrix_mod.numpy().astype(float))
parents = parents[1:]
if score > best_score:
best_score = score
best_parents = parents
def print_whole_matrix(matrix):
for i in range(matrix.shape[0]):
print(" ".join([str(x) for x in matrix[i]]))
assert best_parents is not None, f"{best_parents}\n{print_whole_matrix(weight_matrix)}"
return best_parents
class Model(nn.Module):
def __init__(self, dataset):
super().__init__()
# config = BertConfig("../../configs/base.json")
# self.bert = Bert(config)
# checkpoint = torch.load("../../checkpoints/test_wd=0.01/model.bin", map_location="cpu")
# self.bert.load_state_dict(checkpoint["model"], strict=False)
config = NorbertConfig.from_json_file("config.json")
self.bert = NorbertModel(config)
self.n_layers = config.num_hidden_layers
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.layer_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False)
self.upos_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.xpos_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.feats_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.lemma_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.head_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.dep_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.ner_layer_score = nn.Parameter(torch.zeros(self.n_layers + 1, dtype=torch.float))
self.lemma_classifier = Classifier(config.hidden_size, len(dataset.lemma_vocab), config.hidden_dropout_prob)
self.upos_classifier = Classifier(config.hidden_size, len(dataset.upos_vocab), config.hidden_dropout_prob) if len(dataset.upos_vocab) > 2 else ZeroClassifier()
self.xpos_classifier = Classifier(config.hidden_size, len(dataset.xpos_vocab), config.hidden_dropout_prob) if len(dataset.xpos_vocab) > 2 else ZeroClassifier()
self.feats_classifier = Classifier(config.hidden_size, len(dataset.feats_vocab), config.hidden_dropout_prob) if len(dataset.feats_vocab) > 2 else ZeroClassifier()
self.edge_classifier = EdgeClassifier(config.hidden_size, 128, len(dataset.arc_dep_vocab), config.hidden_dropout_prob)
self.ner_classifier = Classifier(config.hidden_size, len(dataset.ne_vocab), config.hidden_dropout_prob) if len(dataset.ne_vocab) > 2 else ZeroClassifier()
def forward(self, x, alignment_mask, subword_lengths, word_lengths, head_gold=None):
padding_mask = (torch.arange(x.size(1)).unsqueeze(0) < subword_lengths.unsqueeze(1)).to(x.device)
x = self.bert(x, padding_mask, output_hidden_states=True).hidden_states
x = torch.stack(x, dim=0)
upos_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.upos_layer_score, dim=0))
xpos_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.xpos_layer_score, dim=0))
feats_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.feats_layer_score, dim=0))
lemma_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.lemma_layer_score, dim=0))
head_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.head_layer_score, dim=0))
dep_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.dep_layer_score, dim=0))
ne_x = torch.einsum("lbtd, l -> btd", x, torch.softmax(self.ner_layer_score, dim=0))
upos_x = torch.einsum("bsd,bst->btd", upos_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
xpos_x = torch.einsum("bsd,bst->btd", xpos_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
feats_x = torch.einsum("bsd,bst->btd", feats_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
lemma_x = torch.einsum("bsd,bst->btd", lemma_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
head_x = torch.einsum("bsd,bst->btd", head_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
dep_x = torch.einsum("bsd,bst->btd", dep_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
ne_x = torch.einsum("bsd, bst -> btd", ne_x, alignment_mask) / alignment_mask.sum(1).unsqueeze(-1).clamp(min=1.0)
upos_x = self.dropout(self.layer_norm(upos_x[:, 1:-1, :]))
xpos_x = self.dropout(self.layer_norm(xpos_x[:, 1:-1, :]))
feats_x = self.dropout(self.layer_norm(feats_x[:, 1:-1, :]))
lemma_x = self.dropout(self.layer_norm(lemma_x[:, 1:-1, :]))
head_x = self.dropout(self.layer_norm(head_x[:, 0:-1, :]))
dep_x = self.dropout(self.layer_norm(dep_x[:, 0:-1, :]))
ne_x = self.dropout(self.layer_norm(ne_x[:, 1:-1, :]))
lemma_preds = self.lemma_classifier(lemma_x)
upos_preds = self.upos_classifier(upos_x)
xpos_preds = self.xpos_classifier(xpos_x)
feats_preds = self.feats_classifier(feats_x)
ne_preds = self.ner_classifier(feats_x)
head_prediction, dep_prediction, head_liu = self.edge_classifier(head_x, dep_x, word_lengths, head_gold)
return lemma_preds, upos_preds, xpos_preds, feats_preds, head_prediction, dep_prediction, ne_preds, head_liu
class Parser:
def __init__(self):
checkpoint = torch.load("checkpoint.bin", map_location="cpu")
self.dataset = Dataset()
self.dataset.load_state_dict(checkpoint["dataset"])
self.model = Model(self.dataset)
self.model.load_state_dict(checkpoint["model"])
self.model.eval()
del checkpoint
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def parse(self, sentence):
sentence = ftfy.fix_text(sentence.strip())
forms, subwords, alignment = self.dataset.prepare_input(sentence)
with torch.no_grad():
output = self.model(
subwords.to(self.device),
alignment.to(self.device),
torch.tensor([len(forms) + 1], device=self.device),
torch.tensor([subwords.size(1)], device=self.device)
)
lemma_p, upos_p, xpos_p, feats_p, _, dep_p, ne_p, head_p = output
lemmas, upos, xpos, feats, heads, deprel, ne = self.dataset.decode_output(
forms, lemma_p, upos_p, xpos_p, feats_p, dep_p, ne_p, head_p
)
return {
"forms": forms,
"lemmas": lemmas,
"upos": upos,
"xpos": xpos,
"feats": feats,
"heads": heads,
"deprel": deprel,
"ne": ne
}