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run_model_pegasus.py
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run_model_pegasus.py
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import torch
from transformers import BatchEncoding, PreTrainedTokenizer
from typing import Optional, List
from argparse import Namespace
from data_collection import DataCollector
from util import load_BART_or_PEGASUS, load_data, parse_arg
import logging
class SumGen(torch.nn.Module):
def __init__(self, model, tokenizer: PreTrainedTokenizer, cur_dir,
use_cache=True, max_len=30, full_data=False):
# BART encoder outputs: [x, encoder_states, all_attentions]
# BART decoder outputs: [x, next_cache, all_hidden_states, all_self_attns, all_enc_self_attns]
super().__init__()
self.model = model
self.output_attentions = True
self.output_hidden_states = True
self.tokenizer = tokenizer
self.max_len = max_len
self.use_cache = use_cache
self.encoder = self.model.get_encoder()
self.recorder = DataCollector(full_data=full_data, cur_dir=cur_dir)
self.logsoftmax = torch.nn.LogSoftmax(-1)
self.return_dict = False
def save_data(self):
pass
def forward(self, input_doc, input_mask, tgt_sum=None):
device = input_doc.device
batch_size = input_doc.shape[0]
seq_length = input_doc.shape[1]
cur_len = 1
has_eos = [False for _ in range(batch_size)]
bos_token_id = 0
decoded = [[bos_token_id] for _ in range(batch_size)]
decoder_input_ids = torch.LongTensor(decoded).to(device)
past_key_values = None
encoder_outputs = self.encoder(input_doc, attention_mask=input_mask,
return_dict=self.return_dict)
expanded_batch_idxs = (
torch.arange(batch_size)
.view(-1, 1)
.repeat(1, 1)
.view(-1)
.to(device)
)
if self.return_dict:
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(0,
expanded_batch_idxs)
else:
expanded_last_hidden_state = encoder_outputs[0].index_select(0, expanded_batch_idxs) # not sure why 0....
assert len(encoder_outputs) == 1
encoder_outputs = (expanded_last_hidden_state,)
self.recorder.add_input_doc(input_doc, input_mask)
while cur_len < self.max_len and (not all(has_eos)):
logging.debug(f"Current step: {cur_len}")
cur_decoded, cur_past_key_values, cur_decoder_input_ids = self.forward_step(attn_mask=input_mask,
past_key_values=past_key_values,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs
)
# print('run normal')
# cur_decoded is just a list with token id
for idx, cur_dec_tok in enumerate(cur_decoded):
if cur_dec_tok == self.tokenizer.eos_token_id:
has_eos[idx] = True
if tgt_sum is None:
# target = cur_decoder_input_ids[:, -1].unsqueeze(0)
target = cur_decoded[0][0] # assume batch size = 1
print(f'target : {target}')
else:
pass
past_key_values = cur_past_key_values
decoder_input_ids = cur_decoder_input_ids
cur_len += 1
self.recorder.write_to_disk_numpy()
logging.info("end of decoding")
def forward_step(self,
attn_mask, past_key_values, decoder_input_ids, encoder_outputs,
):
"""The forward pass for one single time step
"""
model_inputs = {"input_ids": None,
"past_key_values": past_key_values,
"attention_mask": attn_mask,
"encoder_outputs": encoder_outputs,
"decoder_input_ids": decoder_input_ids,
}
outputs = self.model.forward(**model_inputs,
output_attentions=self.output_attentions,
output_hidden_states=self.output_hidden_states,
use_cache=self.use_cache,
return_dict=self.return_dict) # first decoder ouptuts, then encoder outputs
if self.return_dict:
logits = outputs['logits']
next_cache = outputs['past_key_values']
dec_enc_attns = None
else:
logits = outputs[0] # batch, 1, vocab size
next_cache, decoder_hidden_states, dec_dec_attns, dec_enc_attns = outputs[1], outputs[2], \
outputs[3], outputs[4]
# logits are raw score before softmax
log_prob = self.logsoftmax(logits[:, -1, :])
if dec_enc_attns:
self.recorder.add_step(pred_distribution=log_prob,
all_hidden_states=decoder_hidden_states, attentions=dec_enc_attns)
else:
self.recorder.add_step(pred_distribution=log_prob)
next_token_logits = logits[:, -1, :]
next_token = torch.argmax(next_token_logits, dim=-1)
self.recorder.add_logit(next_token)
cur_next_token = next_token.unsqueeze(-1)
cur_decoded = cur_next_token.tolist()
past_key_values = next_cache
decoder_input_ids = torch.cat([decoder_input_ids, cur_next_token], dim=-1)
return cur_decoded, past_key_values, decoder_input_ids
if __name__ == '__main__':
print("Running experiment")
args = parse_arg()
batch_size = 17 if args.data_name == 'xsum' else 15
max_len = 30 if args.data_name == 'xsum' else 80
split = 'test'
data_generator = load_data(args.dataset_dir, args.data_name,
tokenizer_name=args.model_name,
split=split,
batch_size=batch_size,
max_length=args.enc_len,
max_sample_num=args.max_sample_num + 30)
model, tokenizer = load_BART_or_PEGASUS(args.model_name)
device = torch.device(args.device)
model = model.to(device)
summary_gen_model = SumGen(model=model, tokenizer=tokenizer, cur_dir=args.cur_dir,
full_data=args.feature,
max_len=max_len)
total_cnt = 0
for batch in data_generator:
batch_sz = batch['input_ids'].size()[0]
total_cnt += batch_sz
print(f"Total count: {total_cnt}")
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
tgt = batch['labels'].to(device)
summary_gen_model.forward(input_ids, attention_mask, tgt)