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375 lines
15 KiB
Python
375 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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PyTorch DilBERT model.
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import json
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import logging
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import math
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import sys
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from io import open
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import itertools
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import numpy as np
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import torch
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import torch.nn as nn
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from pytorch_transformers.modeling_utils import PretrainedConfig, PreTrainedModel, add_start_docstrings
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import logging
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logger = logging.getLogger(__name__)
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DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'dilbert-base-uncased': None, # TODO(Victor)
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}
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DILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'dilbert-base-uncased': None, #TODO(Victor)
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}
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class DilBertconfig(PretrainedConfig):
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pretrained_config_archive_map = DILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=30522,
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max_position_embeddings=512,
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sinusoidal_pos_embds=True,
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n_layers=6,
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n_heads=12,
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dim=768,
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dropout=0.1,
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attention_dropout=0.1,
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activation='gelu',
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initializer_range=0.02,
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tie_weights=True,
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**kwargs):
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super(DilBertconfig, self).__init__(**kwargs)
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if isintance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.vocab_size = vocab_size_or_config_json_file
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self.max_position_embeddings = max_position_embeddings
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self.sinusoidal_pos_embds = sinusoidal_pos_embds
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dim = dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation = activation
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self.initializer_range = initializer_range
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self.tie_weights = tie_weights
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)")
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def gelu(x):
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return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def create_sinusoidal_embeddings(n_pos, dim, out):
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position_enc = np.array([
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[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
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for pos in range(n_pos)
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])
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out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
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out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
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out.detach_()
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out.requires_grad = False
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class Embeddings(nn.Module):
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def __init__(self,
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config):
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super(Embeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, dim, padding_idx=0)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
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if sinusoidal_pos_embds:
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create_sinusoidal_embeddings(n_pos=config.max_position_embeddings,
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dim=config.dim,
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out=self.position_embeddings.weight)
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self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, input_ids):
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"""
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Parameters
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----------
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input_ids: torch.tensor(bs, max_seq_length) - The token ids to embed.
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"""
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seq_length = input_ids.size(1)
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position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) # (max_seq_length)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids) # (bs, max_seq_length)
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word_embeddings = self.word_embeddings(input_ids) # (bs, max_seq_length, dim)
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position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
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embeddings = word_embeddings + position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class MultiHeadSelfAttention(nn.Module):
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def __init__(self,
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config):
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super(MultiHeadSelfAttention, self).__init__()
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self.n_heads = config.n_heads
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self.dim = config.dim
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self.dropout = nn.Dropout(p=config.attention_dropout)
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self.output_attentions = config.output_attentions
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assert self.dim % self.n_heads == 0
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self.q_lin = nn.Linear(in_features=dim, out_features=dim)
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self.k_lin = nn.Linear(in_features=dim, out_features=dim)
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self.v_lin = nn.Linear(in_features=dim, out_features=dim)
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self.out_lin = nn.Linear(in_features=dim, out_features=dim)
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def forward(self,
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query: torch.tensor,
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key: torch.tensor,
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value: torch.tensor,
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mask: torch.tensor):
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"""
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Classic Self Attention. I don't understand the one of PyTorch...
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Parameters
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----------
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query: torch.tensor(bs, seq_length, dim)
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key: torch.tensor(bs, seq_length, dim)
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value: torch.tensor(bs, seq_length, dim)
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mask: torch.tensor(bs, seq_length)
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Return
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------
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weights: torch.tensor(bs, n_heads, seq_length, seq_length)
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Attention weights
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context: torch.tensor(bs, seq_length, dim)
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Contextualized layer
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"""
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bs, q_length, dim = query.size()
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k_length = key.size(1)
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assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
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assert key.size() == value.size()
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dim_per_head = dim // self.n_heads
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assert 2 <= mask.dim() <= 3
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causal = (mask.dim() == 3)
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mask_reshp = (bs, 1, 1, k_length)
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def shape(x):
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""" separate heads """
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return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
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def unshape(x):
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""" group heads """
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return x.transpose(1, 2).contiguous().view(bs, -1, dim)
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q = shape(self.q_lin(query)) # (bs, n_heads, q_length, dim_per_head)
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k = shape(self.k_lin(key)) # (bs, n_heads, k_length, dim_per_head)
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v = shape(self.v_lin(value)) # (bs, n_heads, k_length, dim_per_head)
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q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
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scores = torch.matmul(q, k.transpose(2,3)) # (bs, n_heads, q_length, k_length)
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mask = (mask==0).view(mask_reshp).expand_as(scores) # (bs, n_heads, q_length, k_length)
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scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, q_length, k_length)
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weights = nn.Softmax(dim=-1)(scores) # (bs, n_heads, q_length, k_length)
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weights = self.dropout(weights) # (bs, n_heads, q_length, k_length)
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context = torch.matmul(weights, v) # (bs, n_heads, q_length, dim_per_head)
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context = unshape(context) # (bs, q_length, dim)
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context = self.out_lin(context) # (bs, q_length, dim)
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if self.output_attentions:
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return context, weights
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else:
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return context
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class FFN(nn.Module):
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def __init__(self,
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config):
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super(FFN, self).__init__()
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self.dropout = nn.Dropout(p=config.dropout)
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self.lin1 = nn.Linear(in_features=config.dim, out_features=config.hidden_dim)
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self.lin2 = nn.Linear(in_features=config.hidden_dim, out_features=config.dim)
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assert activation in ['relu', 'gelu'], ValueError(f"activation ({config.activation}) must be in ['relu', 'gelu']")
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self.activation = gelu if activation == 'gelu' else nn.ReLU()
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def forward(self,
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input: torch.tensor):
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x = self.lin1(input)
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x = self.activation(x)
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x = self.lin2(x)
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x = self.dropout(x)
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return x
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class TransformerBlock(nn.Module):
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def __init__(self,
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config):
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super(TransformerBlock, self).__init__()
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self.n_heads = config.n_heads
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self.dim = config.dim
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self.hidden_dim = config.hidden_dim
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self.dropout = nn.Dropout(p=config.dropout)
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self.activation = config.activation
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self.output_attentions = config.output_attentions
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assert dim % n_heads == 0
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self.attention = MultiHeadSelfAttention(dim=config.dim,
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n_heads=config.n_heads,
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dropout=config.attention_dropout,
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output_attentions=config.output_attentions)
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self.sa_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
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self.ffn = FFN(in_dim=config.dim,
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hidden_dim=config.hidden_dim,
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out_dim=config.dim,
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dropout=config.dropout,
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activation=config.activation)
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self.output_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
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def forward(self,
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x: torch.tensor,
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attn_mask: torch.tensor = None):
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"""
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Parameters
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----------
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x: torch.tensor(bs, seq_length, dim)
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attn_mask: torch.tensor(bs, seq_length)
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"""
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# Self-Attention
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sa_output = self.attention(query=x, key=x, value=x, mask=attn_mask)
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if self.output_attentions:
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sa_output, sa_weights = sa_output # (bs, seq_length, dim)
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sa_output = self.sa_layer_norm(sa_output + x) # (bs, seq_length, dim)
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# Feed Forward Network
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ffn_output = self.ffn(sa_output) # (bs, seq_length, dim)
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ffn_output = self.output_layer_norm(ffn_output + sa_output) # (bs, seq_length, dim)
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if self.output_attentions:
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return sa_weights, ffn_output
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else:
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return ffn_output
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class Transformer(nn.Module):
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def __init__(self,
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config):
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super(Transformer, self).__init__()
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self.n_layers = config.n_layers
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self.output_attentions = config.output_attentions
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layer = TransformerBlock(n_heads=config.n_heads,
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dim=config.dim,
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hidden_dim=config.hidden_dim,
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dropout=config.dropout,
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attention_dropout=config.attention_dropout,
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activation=config.activation,
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output_attentions=config.output_attentions)
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self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layers)])
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def forward(self,
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x: torch.tensor,
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attn_mask: torch.tensor = None,
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output_all_encoded_layers: bool = True):
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"""
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Parameters
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----------
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x: torch.tensor(bs, seq_length, dim)
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attn_mask: torch.tensor(bs, seq_length)
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output_all_encoded_layers: bool
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"""
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all_encoder_layers = []
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all_attentions = []
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for _, layer_module in enumerate(self.layer):
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x = layer_module(x=x, attn_mask=attn_mask)
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if self.output_attentions:
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attentions, x = x
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all_attentions.append(attentions)
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all_encoder_layers.append(x)
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if not output_all_encoded_layers:
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all_encoder_layers = all_encoder_layers[-1]
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if self.output_attentions:
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return all_attentions, all_encoder_layers
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else:
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return all_encoder_layers
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# TODO(Victor)
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# class DilBertWithLMHeadModel(DilBertPreTrainedModel):
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# class DilBertForSequenceClassification(DilBertPretrainedModel):
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class DilBertForQuestionAnswering(DilBertPreTrainedModel):
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def __init__(self, config):
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super(DilBertForQuestionAnswering, self).__init__(config)
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self.dilbert = DilBertModel(config)
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self.qa_outputs = nn.Linear(config.dim, config.num_labels)
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assert config.num_labels == 2
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self.dropout = nn.Dropout(config.qa_dropout)
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self.apply(self.init_weights)
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def forward(self,
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input_ids: torch.tensor,
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attention_mask: torch.tensor = None,
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start_positions: torch.tensor = None,
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end_positions: torch.tensor = None):
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_, _, hidden_states = self.dilbert(input_ids=input_ids,
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attention_mask=attention_mask) # _, _, (bs, max_query_len, dim)
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hidden_states = self.dropout(hidden_states) # (bs, max_query_len, dim)
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logits = self.qa_outputs(hidden_states) # (bs, max_query_len, 2)
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start_logits, end_logits = logits.split(1, dim=-1)
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start_logits = start_logits.squeeze(-1) # (bs, max_query_len)
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end_logits = end_logits.squeeze(-1) # (bs, max_query_len)
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outputs = (start_logits, end_logits,) + (hidden_states,)
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if start_positions is not None and end_positions is not None:
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# If we are on multi-GPU, split add a dimension
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if len(start_positions.size()) > 1:
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start_positions = start_positions.squeeze(-1)
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if len(end_positions.size()) > 1:
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end_positions = end_positions.squeeze(-1)
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# sometimes the start/end positions are outside our model inputs, we ignore these terms
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ignored_index = start_logits.size(1)
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start_positions.clamp_(0, ignored_index)
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end_positions.clamp_(0, ignored_index)
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loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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outputs = (total_loss,) + outputs
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return outputs # (loss), start_logits, end_logits, hidden_states |