“SufurElite”
added unzipped gz predictions, the checkpoint with values, and the tree output possibility in the model
0bdc170
# coding=utf-8 | |
# Copyright 2023 Language Technology Group from University of Oslo and The HuggingFace Inc. team. | |
# And Copyright 2024 The Google Research Authors. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# Base implementation of the LTG-BERT/ELC-BERT Model is from Language Technology Group from University of Oslo and The HuggingFace Inc., Team | |
# The StructFormer components is from The Google Research Authors - the authors were Yikang Shen and Yi Tay and Che Zheng and Dara Bahri and Donald Metzler and Aaron Courville | |
# (and the code can be from here: https://github.com/google-research/google-research/tree/master/structformer), both were using Apache license, Version 2.0 | |
""" PyTorch LTG-(ELC)-ParserBERT model.""" | |
import math | |
from typing import List, Optional, Tuple, Union | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.utils import checkpoint | |
from .configuration_ltgbert import LtgBertConfig | |
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.utils import ( | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
) | |
_CHECKPOINT_FOR_DOC = "ltg/bnc-bert-span" | |
_CONFIG_FOR_DOC = "LtgBertConfig" | |
LTG_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"bnc-bert-span", | |
"bnc-bert-span-2x", | |
"bnc-bert-span-0.5x", | |
"bnc-bert-span-0.25x", | |
"bnc-bert-span-order", | |
"bnc-bert-span-document", | |
"bnc-bert-span-word", | |
"bnc-bert-span-subword", | |
"norbert3-xs", | |
"norbert3-small", | |
"norbert3-base", | |
"norbert3-large", | |
"norbert3-oversampled-base", | |
"norbert3-ncc-base", | |
"norbert3-nak-base", | |
"norbert3-nb-base", | |
"norbert3-wiki-base", | |
"norbert3-c4-base", | |
] | |
class Conv1d(nn.Module): | |
"""1D convolution layer.""" | |
def __init__(self, hidden_size, kernel_size, dilation=1): | |
"""Initialization. | |
Args: | |
hidden_size: dimension of input embeddings | |
kernel_size: convolution kernel size | |
dilation: the spacing between the kernel points | |
""" | |
super(Conv1d, self).__init__() | |
if kernel_size % 2 == 0: | |
padding = (kernel_size // 2) * dilation | |
self.shift = True | |
else: | |
padding = ((kernel_size - 1) // 2) * dilation | |
self.shift = False | |
self.conv = nn.Conv1d( | |
hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation | |
) | |
def forward(self, x): | |
"""Compute convolution. | |
Args: | |
x: input embeddings | |
Returns: | |
conv_output: convolution results | |
""" | |
if self.shift: | |
return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] | |
else: | |
return self.conv(x.transpose(1, 2)).transpose(1, 2) | |
def cumprod(x, reverse=False, exclusive=False): | |
"""cumulative product.""" | |
if reverse: | |
x = x.flip([-1]) | |
if exclusive: | |
x = F.pad(x[:, :, :-1], (1, 0), value=1) | |
cx = x.cumprod(-1) | |
if reverse: | |
cx = cx.flip([-1]) | |
return cx | |
def cumsum(x, reverse=False, exclusive=False): | |
"""cumulative sum.""" | |
bsz, _, length = x.size() | |
device = x.device | |
if reverse: | |
if exclusive: | |
w = torch.ones([bsz, length, length], device=device).tril(-1) | |
else: | |
w = torch.ones([bsz, length, length], device=device).tril(0) | |
cx = torch.bmm(x, w) | |
else: | |
if exclusive: | |
w = torch.ones([bsz, length, length], device=device).triu(1) | |
else: | |
w = torch.ones([bsz, length, length], device=device).triu(0) | |
cx = torch.bmm(x, w) | |
return cx | |
def cummin(x, reverse=False, exclusive=False, max_value=1e4): | |
"""cumulative min.""" | |
if reverse: | |
if exclusive: | |
x = F.pad(x[:, :, 1:], (0, 1), value=max_value) | |
x = x.flip([-1]).cummin(-1)[0].flip([-1]) | |
else: | |
if exclusive: | |
x = F.pad(x[:, :, :-1], (1, 0), value=max_value) | |
x = x.cummin(-1)[0] | |
return x | |
class ParserNetwork(nn.Module): | |
def __init__( | |
self, | |
config, | |
pad=0, | |
n_parser_layers=4, | |
conv_size=9, | |
relations=("head", "child"), | |
weight_act="softmax", | |
): | |
""" | |
hidden_size: dimension of input embeddings | |
nlayers: number of layers | |
ntokens: number of output categories | |
nhead: number of self-attention heads | |
dropout: dropout rate | |
pad: pad token index | |
n_parser_layers: number of parsing layers | |
conv_size: convolution kernel size for parser | |
relations: relations that are used to compute self attention | |
weight_act: relations distribution activation function | |
""" | |
super(ParserNetwork, self).__init__() | |
self.hidden_size = config.hidden_size | |
self.num_hidden_layers = config.num_hidden_layers | |
self.num_attention_heads = config.num_attention_heads | |
self.parser_layers = nn.ModuleList( | |
[ | |
nn.Sequential( | |
Conv1d(self.hidden_size, conv_size), | |
nn.LayerNorm(self.hidden_size, elementwise_affine=False), | |
nn.Tanh(), | |
) | |
for _ in range(n_parser_layers) | |
] | |
) | |
self.distance_ff = nn.Sequential( | |
Conv1d(self.hidden_size, 2), | |
nn.LayerNorm(self.hidden_size, elementwise_affine=False), | |
nn.Tanh(), | |
nn.Linear(self.hidden_size, 1), | |
) | |
self.height_ff = nn.Sequential( | |
nn.Linear(self.hidden_size, self.hidden_size), | |
nn.LayerNorm(self.hidden_size, elementwise_affine=False), | |
nn.Tanh(), | |
nn.Linear(self.hidden_size, 1), | |
) | |
n_rel = len(relations) | |
self._rel_weight = nn.Parameter( | |
torch.zeros((self.num_hidden_layers, self.num_attention_heads, n_rel)) | |
) | |
self._rel_weight.data.normal_(0, 0.1) | |
self._scaler = nn.Parameter(torch.zeros(2)) | |
self.n_parse_layers = n_parser_layers | |
self.weight_act = weight_act | |
self.relations = relations | |
self.pad = pad | |
def scaler(self): | |
return self._scaler.exp() | |
def rel_weight(self): | |
if self.weight_act == "sigmoid": | |
return torch.sigmoid(self._rel_weight) | |
elif self.weight_act == "softmax": | |
return torch.softmax(self._rel_weight, dim=-1) | |
def parse(self, x, h): | |
""" | |
Parse input sentence. | |
Args: | |
x: input tokens (required). | |
h: static embeddings | |
Returns: | |
distance: syntactic distance | |
height: syntactic height | |
""" | |
mask = x != self.pad | |
mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) | |
for i in range(self.n_parse_layers): | |
h = h.masked_fill(~mask[:, :, None], 0) | |
h = self.parser_layers[i](h) | |
height = self.height_ff(h).squeeze(-1) | |
height.masked_fill_(~mask, -1e4) | |
distance = self.distance_ff(h).squeeze(-1) | |
distance.masked_fill_(~mask_shifted, 1e4) | |
# Calbrating the distance and height to the same level | |
length = distance.size(1) | |
height_max = height[:, None, :].expand(-1, length, -1) | |
height_max = torch.cummax( | |
height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e4, dim=-1 | |
)[0].triu(0) | |
margin_left = torch.relu( | |
F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e4) - height_max | |
) | |
margin_right = torch.relu(distance[:, None, :] - height_max) | |
margin = torch.where( | |
margin_left > margin_right, margin_right, margin_left | |
).triu(0) | |
margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) | |
margin.masked_fill_(~margin_mask, 0) | |
margin = margin.max() | |
distance = distance - margin | |
return distance, height | |
def compute_block(self, distance, height): | |
"""Compute constituents from distance and height.""" | |
beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] | |
gamma = torch.sigmoid(-beta_logits) | |
ones = torch.ones_like(gamma) | |
block_mask_left = cummin( | |
gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 | |
) | |
block_mask_left = block_mask_left - F.pad( | |
block_mask_left[:, :, :-1], (1, 0), value=0 | |
) | |
block_mask_left.tril_(0) | |
block_mask_right = cummin( | |
gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 | |
) | |
block_mask_right = block_mask_right - F.pad( | |
block_mask_right[:, :, 1:], (0, 1), value=0 | |
) | |
block_mask_right.triu_(0) | |
block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] | |
block = cumsum(block_mask_left).tril(0) + cumsum( | |
block_mask_right, reverse=True | |
).triu(1) | |
return block_p, block | |
def compute_head(self, height): | |
"""Estimate head for each constituent.""" | |
_, length = height.size() | |
head_logits = height * self.scaler[1] | |
index = torch.arange(length, device=height.device) | |
mask = (index[:, None, None] <= index[None, None, :]) * ( | |
index[None, None, :] <= index[None, :, None] | |
) | |
head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) | |
head_logits.masked_fill_(~mask[None, :, :, :], -1e4) | |
head_p = torch.softmax(head_logits, dim=-1) | |
return head_p | |
def generate_mask(self, x, distance, height): | |
"""Compute head and cibling distribution for each token.""" | |
batch_size, length = x.size() | |
eye = torch.eye(length, device=x.device, dtype=torch.bool) | |
eye = eye[None, :, :].expand((batch_size, -1, -1)) | |
block_p, block = self.compute_block(distance, height) | |
head_p = self.compute_head(height) | |
head = torch.einsum("blij,bijh->blh", block_p, head_p) | |
head = head.masked_fill(eye, 0) | |
child = head.transpose(1, 2) | |
cibling = torch.bmm(head, child).masked_fill(eye, 0) | |
rel_list = [] | |
if "head" in self.relations: | |
rel_list.append(head) | |
if "child" in self.relations: | |
rel_list.append(child) | |
if "cibling" in self.relations: | |
rel_list.append(cibling) | |
rel = torch.stack(rel_list, dim=1) | |
rel_weight = self.rel_weight | |
dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) | |
att_mask = dep.reshape( | |
self.num_hidden_layers, batch_size, self.num_attention_heads, length, length | |
) | |
return att_mask, cibling, head, block | |
def forward(self, x, embeddings): | |
""" | |
Pass the x tokens through the parse network, get the syntactic height and distances | |
and compute the distribution for each token | |
""" | |
x = torch.transpose(x, 0, 1) | |
embeddings = torch.transpose(embeddings, 0, 1) | |
distance, height = self.parse(x, embeddings) | |
att_mask, cibling, head, block = self.generate_mask(x, distance, height) | |
return att_mask, cibling, head, block, distance, height | |
class Encoder(nn.Module): | |
def __init__(self, config, activation_checkpointing=False): | |
super().__init__() | |
self.layers = nn.ModuleList( | |
[EncoderLayer(config, i) for i 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 i in range(len(self.layers)): | |
if self.activation_checkpointing: | |
hidden_state, attention_p = checkpoint.checkpoint( | |
self.layers[i], hidden_states, attention_mask, relative_embedding | |
) | |
else: | |
hidden_state, attention_p = self.layers[i]( | |
hidden_states, attention_mask[i], 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, layer_num): | |
super().__init__() | |
self.attention = Attention(config) | |
self.mlp = FeedForward(config) | |
temp = torch.zeros(layer_num + 1) | |
temp[-1] = 1 | |
self.prev_layer_weights = nn.Parameter(temp) | |
def forward(self, hidden_states, padding_mask, relative_embedding): | |
prev_layer_weights = F.softmax(self.prev_layer_weights, dim=-1) | |
x = prev_layer_weights[0] * hidden_states[0] | |
for i, hidden_state in enumerate(hidden_states[1:]): | |
x = x + prev_layer_weights[i + 1] * hidden_state | |
attention_output, attention_probs = self.attention( | |
x, padding_mask, relative_embedding | |
) | |
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): | |
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 | |
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) | |
query_pos, key_pos = self.in_proj_qk(self.dropout(relative_embedding)).chunk( | |
2, dim=-1 | |
) # shape: [2T-1, D] | |
query_pos = query_pos.view( | |
-1, self.num_heads, self.head_size | |
) # shape: [2T-1, H, D] | |
key_pos = key_pos.view( | |
-1, self.num_heads, self.head_size | |
) # shape: [2T-1, H, D] | |
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 = torch.sigmoid(attention_scores) * attention_mask | |
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 LtgBertPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = LtgBertConfig | |
base_model_prefix = "bnc-bert" | |
supports_gradient_checkpointing = True | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, Encoder): | |
module.activation_checkpointing = value | |
def _init_weights(self, _): | |
pass # everything is already initialized | |
LTG_BERT_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | |
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage | |
and behavior. | |
Parameters: | |
config ([`LtgBertConfig`]): Model configuration class with all the parameters of the model. | |
Initializing with a config file does not load the weights associated with the model, only the | |
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
LTG_BERT_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `({0})`): | |
Indices of input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
class LtgBertModel(LtgBertPreTrainedModel): | |
def __init__(self, config, add_mlm_layer=False, tree_output=False): | |
super().__init__(config) | |
self.config = config | |
self.tree_output=tree_output | |
self.embedding = Embedding(config) | |
self.parser_network = ParserNetwork(config, pad=config.pad_token_id) | |
self.transformer = Encoder(config, activation_checkpointing=False) | |
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 | |
static_embeddings, relative_embedding = self.embedding(input_ids.t()) | |
att_mask, cibling, head, block, distance, height = self.parser_network( | |
input_ids.t(), static_embeddings | |
) | |
contextualized_embeddings, attention_probs = self.transformer( | |
static_embeddings, att_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)) | |
] | |
if self.tree_output: | |
return last_layer, contextualized_embeddings, attention_probs, {'distance': distance, 'height': height, | |
'cibling': cibling, 'head': head, 'block': block} | |
return last_layer, contextualized_embeddings, attention_probs | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
output_hidden_states: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.Tensor], BaseModelOutput]: | |
output_attentions = ( | |
output_attentions | |
if output_attentions is not None | |
else self.config.output_attentions | |
) | |
output_hidden_states = ( | |
output_hidden_states | |
if output_hidden_states is not None | |
else self.config.output_hidden_states | |
) | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
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 LtgBertForMaskedLM(LtgBertModel): | |
_keys_to_ignore_on_load_unexpected = ["head"] | |
def __init__(self, config, tree_output=False): | |
super().__init__(config, add_mlm_layer=True, tree_output=tree_output) | |
def get_output_embeddings(self): | |
return self.classifier.nonlinearity[-1].weight | |
def set_output_embeddings(self, new_embeddings): | |
self.classifier.nonlinearity[-1].weight = new_embeddings | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
output_hidden_states: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: | |
r""" | |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., | |
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the | |
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` | |
""" | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
subword_prediction = self.classifier(sequence_output) | |
masked_lm_loss = None | |
if labels is not None: | |
masked_lm_loss = F.cross_entropy( | |
subword_prediction.flatten(0, 1), labels.flatten() | |
) | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
if not return_dict: | |
output = ( | |
subword_prediction, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
return ( | |
((masked_lm_loss,) + output) if masked_lm_loss is not None else output | |
) | |
return MaskedLMOutput( | |
loss=masked_lm_loss, | |
logits=subword_prediction, | |
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, config, num_labels: int): | |
super().__init__() | |
drop_out = getattr(config, "classifier_dropout", config.hidden_dropout_prob) | |
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(drop_out), | |
nn.Linear(config.hidden_size, num_labels), | |
) | |
self.initialize(config.hidden_size) | |
def initialize(self, hidden_size): | |
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 | |
) | |
nn.init.trunc_normal_( | |
self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2 * std, b=2 * std | |
) | |
self.nonlinearity[1].bias.data.zero_() | |
self.nonlinearity[-1].bias.data.zero_() | |
def forward(self, x): | |
x = self.nonlinearity(x) | |
return x | |
class LtgBertForSequenceClassification(LtgBertModel): | |
_keys_to_ignore_on_load_unexpected = ["classifier"] | |
_keys_to_ignore_on_load_missing = ["head"] | |
def __init__(self, config): | |
super().__init__(config, add_mlm_layer=False) | |
self.num_labels = config.num_labels | |
self.head = Classifier(config, self.num_labels) | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: | |
r""" | |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): | |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., | |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If | |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | |
""" | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
logits = self.head(sequence_output[:, 0, :]) | |
loss = None | |
if labels is not None: | |
if self.config.problem_type is None: | |
if self.num_labels == 1: | |
self.config.problem_type = "regression" | |
elif self.num_labels > 1 and ( | |
labels.dtype == torch.long or labels.dtype == torch.int | |
): | |
self.config.problem_type = "single_label_classification" | |
else: | |
self.config.problem_type = "multi_label_classification" | |
if self.config.problem_type == "regression": | |
loss_fct = nn.MSELoss() | |
if self.num_labels == 1: | |
loss = loss_fct(logits.squeeze(), labels.squeeze()) | |
else: | |
loss = loss_fct(logits, labels) | |
elif self.config.problem_type == "single_label_classification": | |
loss_fct = nn.CrossEntropyLoss() | |
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
elif self.config.problem_type == "multi_label_classification": | |
loss_fct = nn.BCEWithLogitsLoss() | |
loss = loss_fct(logits, labels) | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
if not return_dict: | |
output = ( | |
logits, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
return ((loss,) + output) if loss is not None else output | |
return SequenceClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=contextualized_embeddings if output_hidden_states else None, | |
attentions=attention_probs if output_attentions else None, | |
) | |
class LtgBertForTokenClassification(LtgBertModel): | |
_keys_to_ignore_on_load_unexpected = ["classifier"] | |
_keys_to_ignore_on_load_missing = ["head"] | |
def __init__(self, config): | |
super().__init__(config, add_mlm_layer=False) | |
self.num_labels = config.num_labels | |
self.head = Classifier(config, self.num_labels) | |
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_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
logits = self.head(sequence_output) | |
loss = None | |
if labels is not None: | |
loss_fct = nn.CrossEntropyLoss() | |
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
if not return_dict: | |
output = ( | |
logits, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
return ((loss,) + output) if loss is not None else output | |
return TokenClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=contextualized_embeddings if output_hidden_states else None, | |
attentions=attention_probs if output_attentions else None, | |
) | |
class LtgBertForQuestionAnswering(LtgBertModel): | |
_keys_to_ignore_on_load_unexpected = ["classifier"] | |
_keys_to_ignore_on_load_missing = ["head"] | |
def __init__(self, config): | |
super().__init__(config, add_mlm_layer=False) | |
self.num_labels = config.num_labels | |
self.head = Classifier(config, self.num_labels) | |
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_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
start_positions: Optional[torch.Tensor] = None, | |
end_positions: Optional[torch.Tensor] = None, | |
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
logits = self.head(sequence_output) | |
start_logits, end_logits = logits.split(1, dim=-1) | |
start_logits = start_logits.squeeze(-1).contiguous() | |
end_logits = end_logits.squeeze(-1).contiguous() | |
total_loss = None | |
if start_positions is not None and end_positions is not None: | |
# If we are on multi-GPU, split add a dimension | |
if len(start_positions.size()) > 1: | |
start_positions = start_positions.squeeze(-1) | |
if len(end_positions.size()) > 1: | |
end_positions = end_positions.squeeze(-1) | |
# sometimes the start/end positions are outside our model inputs, we ignore these terms | |
ignored_index = start_logits.size(1) | |
start_positions = start_positions.clamp(0, ignored_index) | |
end_positions = end_positions.clamp(0, ignored_index) | |
loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index) | |
start_loss = loss_fct(start_logits, start_positions) | |
end_loss = loss_fct(end_logits, end_positions) | |
total_loss = (start_loss + end_loss) / 2 | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
if not return_dict: | |
output = ( | |
start_logits, | |
end_logits, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
return ((total_loss,) + output) if total_loss is not None else output | |
return QuestionAnsweringModelOutput( | |
loss=total_loss, | |
start_logits=start_logits, | |
end_logits=end_logits, | |
hidden_states=contextualized_embeddings if output_hidden_states else None, | |
attentions=attention_probs if output_attentions else None, | |
) | |
class LtgBertForMultipleChoice(LtgBertModel): | |
_keys_to_ignore_on_load_unexpected = ["classifier"] | |
_keys_to_ignore_on_load_missing = ["head"] | |
def __init__(self, config): | |
super().__init__(config, add_mlm_layer=False) | |
self.num_labels = getattr(config, "num_labels", 2) | |
self.head = Classifier(config, self.num_labels) | |
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, | |
labels: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: | |
return_dict = ( | |
return_dict if return_dict is not None else self.config.use_return_dict | |
) | |
num_choices = input_ids.shape[1] | |
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) | |
flat_attention_mask = ( | |
attention_mask.view(-1, attention_mask.size(-1)) | |
if attention_mask is not None | |
else None | |
) | |
tree_values = {} if self.tree_output else None | |
if self.tree_output: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs, | |
tree_values | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
else: | |
( | |
sequence_output, | |
contextualized_embeddings, | |
attention_probs | |
) = self.get_contextualized_embeddings(input_ids, attention_mask) | |
logits = self.head(sequence_output) | |
reshaped_logits = logits.view(-1, num_choices) | |
loss = None | |
if labels is not None: | |
loss_fct = nn.CrossEntropyLoss() | |
loss = loss_fct(reshaped_logits, labels) | |
if self.tree_output: | |
return ( | |
sequence_output, | |
tree_values, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
if not return_dict: | |
output = ( | |
reshaped_logits, | |
*([contextualized_embeddings] if output_hidden_states else []), | |
*([attention_probs] if output_attentions else []), | |
) | |
return ((loss,) + output) if loss is not None else output | |
return MultipleChoiceModelOutput( | |
loss=loss, | |
logits=reshaped_logits, | |
hidden_states=contextualized_embeddings if output_hidden_states else None, | |
attentions=attention_probs if output_attentions else None, | |
) | |