File size: 3,944 Bytes
f4dcc30 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | # Copyright (c) Microsoft, Inc. 2020
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Author: penhe@microsoft.com
# Date: 01/25/2019
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pdb
import torch
from torch.nn import CrossEntropyLoss
import math
from ...deberta import *
from ...utils import *
__all__= ['SequenceClassificationModel']
class SequenceClassificationModel(NNModule):
def __init__(self, config, num_labels=2, drop_out=None, pre_trained=None):
super().__init__(config)
self.num_labels = num_labels
self._register_load_state_dict_pre_hook(self._pre_load_hook)
self.deberta = DeBERTa(config, pre_trained=pre_trained)
if pre_trained is not None:
self.config = self.deberta.config
else:
self.config = config
pool_config = PoolConfig(self.config)
output_dim = self.deberta.config.hidden_size
self.pooler = ContextPooler(pool_config)
output_dim = self.pooler.output_dim()
self.classifier = torch.nn.Linear(output_dim, num_labels)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = StableDropout(drop_out)
self.apply(self.init_weights)
self.deberta.apply_state()
def forward(self, input_ids, type_ids=None, input_mask=None, labels=None, position_ids=None, **kwargs):
outputs = self.deberta(input_ids, attention_mask=input_mask, token_type_ids=type_ids,
position_ids=position_ids, output_all_encoded_layers=True)
encoder_layers = outputs['hidden_states']
pooled_output = self.pooler(encoder_layers[-1])
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = torch.tensor(0).to(logits)
if labels is not None:
if self.num_labels ==1:
# regression task
loss_fn = torch.nn.MSELoss()
logits=logits.view(-1).to(labels.dtype)
loss = loss_fn(logits, labels.view(-1))
elif labels.dim()==1 or labels.size(-1)==1:
label_index = (labels >= 0).nonzero()
labels = labels.long()
if label_index.size(0) > 0:
labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), logits.size(1)))
labels = torch.gather(labels, 0, label_index.view(-1))
loss_fct = CrossEntropyLoss()
loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
else:
loss = torch.tensor(0).to(logits)
else:
log_softmax = torch.nn.LogSoftmax(-1)
label_confidence = 1
loss = -((log_softmax(logits)*labels).sum(-1)*label_confidence).mean()
return {
'logits' : logits,
'loss' : loss
}
def export_onnx(self, onnx_path, input):
del input[0]['labels'] #= input[0]['labels'].unsqueeze(1)
torch.onnx.export(self, input, onnx_path, opset_version=13, do_constant_folding=False, \
input_names=['input_ids', 'type_ids', 'input_mask', 'position_ids', 'labels'], output_names=['logits', 'loss'], \
dynamic_axes={'input_ids' : {0 : 'batch_size', 1: 'sequence_length'}, \
'type_ids' : {0 : 'batch_size', 1: 'sequence_length'}, \
'input_mask' : {0 : 'batch_size', 1: 'sequence_length'}, \
'position_ids' : {0 : 'batch_size', 1: 'sequence_length'}, \
# 'labels' : {0 : 'batch_size', 1: 'sequence_length'}, \
})
def _pre_load_hook(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
new_state = dict()
bert_prefix = prefix + 'bert.'
deberta_prefix = prefix + 'deberta.'
for k in list(state_dict.keys()):
if k.startswith(bert_prefix):
nk = deberta_prefix + k[len(bert_prefix):]
value = state_dict[k]
del state_dict[k]
state_dict[nk] = value
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