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import torch
import torch.nn as nn
from transformers.modeling_bart import BartForConditionalGeneration
from relogic.logickit.dataflow.semtransparse.grammar.keywords import SKETCH_KEYWORDS, KEYWORDS
import logging
import os
logger = logging.getLogger(__name__)
WEIGHTS_NAME = "pytorch_model.bin"
class SQL2TextModel(nn.Module):
"""
output: tuple: (loss, ) in training
"""
def __init__(self):
super().__init__()
self.bert = BartForConditionalGeneration.from_pretrained("facebook/bart-large")
def forward(self, *input, **kwargs):
input_ids = kwargs.pop("input_ids")
pad_token_id = kwargs.pop("pad_token_id")
attention_mask = (input_ids != pad_token_id).long()
if self.training:
output_ids = kwargs.pop('labels')
y_ids = output_ids[:, :-1].contiguous()
lm_labels = output_ids[:, 1:].clone()
lm_labels[output_ids[:, 1:] == pad_token_id] = -100
outputs = self.bert(input_ids,
attention_mask=attention_mask, decoder_input_ids=y_ids, lm_labels=lm_labels, )
return (outputs[0],)
else:
label_eos_id = kwargs.pop("label_eos_id")
label_bos_id = kwargs.pop("label_bos_id")
label_padding_id = kwargs.pop("label_padding_id")
generated_ids = self.bert.generate(
input_ids=input_ids,
attention_mask=attention_mask,
num_beams=3,
max_length=60,
length_penalty=2.0,
early_stopping=True,
use_cache=True,
decoder_start_token_id=label_bos_id,
eos_token_id=label_eos_id,
pad_token_id=label_padding_id
)
output_ids = kwargs.pop('labels')
y_ids = output_ids[:, :-1].contiguous()
lm_labels = output_ids[:, 1:].clone()
lm_labels[output_ids[:, 1:] == pad_token_id] = -100
outputs = self.bert(input_ids,
attention_mask=attention_mask, decoder_input_ids=y_ids, lm_labels=lm_labels, )
return (outputs[0].detach(), generated_ids)
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
Arguments:
save_directory: directory to which to save.
"""
assert os.path.isdir(
save_directory
), "Saving path should be a directory where the model and configuration can be saved"
# Only save the model itself if we are using distributed training
model_to_save = self.module if hasattr(self, "module") else self
# Attach architecture to the config
# model_to_save.config.architectures = [model_to_save.__class__.__name__]
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
logger.info("Model weights saved in {}".format(output_model_file))
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