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import yaml
import torch
from .model import Seq2SeqTransformer
from transformers import AutoTokenizer
from transformers import PreTrainedTokenizerFast
from tokenizers.processors import TemplateProcessing
def addPreprocessing(tokenizer):
tokenizer._tokenizer.post_processor = TemplateProcessing(
single=tokenizer.bos_token + " $A " + tokenizer.eos_token,
special_tokens=[(tokenizer.eos_token, tokenizer.eos_token_id), (tokenizer.bos_token, tokenizer.bos_token_id)])
def load_model(model_checkpoint_dir='model.pt',config_dir='config.yaml'):
with open(config_dir, 'r') as yaml_file:
loaded_model_params = yaml.safe_load(yaml_file)
# Create a new instance of the model with the loaded configuration
model = Seq2SeqTransformer(
loaded_model_params["num_encoder_layers"],
loaded_model_params["num_decoder_layers"],
loaded_model_params["emb_size"],
loaded_model_params["nhead"],
loaded_model_params["source_vocab_size"],
loaded_model_params["target_vocab_size"],
loaded_model_params["ffn_hid_dim"]
)
checkpoint = torch.load(model_checkpoint_dir) if torch.cuda.is_available() else torch.load(model_checkpoint_dir,map_location=torch.device('cpu'))
model.load_state_dict(checkpoint)
return model
def greedy_decode(model, src, src_mask, max_len, start_symbol):
# Move inputs to the device
src = src.to(device)
src_mask = src_mask.to(device)
# Encode the source sequence
memory = model.encode(src, src_mask)
# Initialize the target sequence with the start symbol
ys = torch.tensor([[start_symbol]]).type(torch.long).to(device)
for i in range(max_len - 1):
memory = memory.to(device)
# Create a target mask for autoregressive decoding
tgt_mask = torch.tril(torch.full((ys.size(1), ys.size(1)), float('-inf'), device=device), diagonal=-1).transpose(0, 1).to(device)
# Decode the target sequence
out = model.decode(ys, memory, tgt_mask)
# Generate the probability distribution over the vocabulary
prob = model.generator(out[:, -1])
# Select the next word with the highest probability
_, next_word = torch.max(prob, dim=1)
next_word = next_word.item()
# Append the next word to the target sequence
ys = torch.cat([ys,
torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)
# Check if the generated word is the end-of-sequence token
if next_word == target_tokenizer.eos_token_id:
break
return ys
def beam_search_decode(model, src, src_mask, max_len, start_symbol, beam_size ,length_penalty):
# Move inputs to the device
src = src.to(device)
src_mask = src_mask.to(device)
# Encode the source sequence
memory = model.encode(src, src_mask) # b * seqlen_src * hdim
# Initialize the beams (sequences, score)
beams = [(torch.tensor([[start_symbol]]).type(torch.long).to(device), 0)]
for i in range(max_len - 1):
new_beams = []
complete_beams = []
cbl = []
for ys, score in beams:
# Create a target mask for autoregressive decoding
tgt_mask = torch.tril(torch.full((ys.size(1), ys.size(1)), float('-inf'), device=device), diagonal=-1).transpose(0, 1).to(device)
# Decode the target sequence
out = model.decode(ys, memory, tgt_mask) # b * seqlen_tgt * hdim
#print(f'shape out {out.shape}')
# Generate the probability distribution over the vocabulary
prob = model.generator(out[:, -1]) # b * tgt_vocab_size
#print(f'shape prob {prob.shape}')
# Get the top beam_size candidates for the next word
_, top_indices = torch.topk(prob, beam_size, dim=1) # b * beam_size
for j,next_word in enumerate(top_indices[0]):
next_word = next_word.item()
# Append the next word to the target sequence
new_ys = torch.cat([ys, torch.full((1, 1), fill_value=next_word, dtype=src.dtype).to(device)], dim=1)
length_factor = (5 + j / 6) ** length_penalty
new_score = (score + prob[0][next_word].item()) / length_factor
if next_word == target_tokenizer.eos_token_id:
complete_beams.append((new_ys, new_score))
else:
new_beams.append((new_ys, new_score))
# Sort the beams by score and select the top beam_size beams
new_beams.sort(key=lambda x: x[1], reverse=True)
try:
beams = new_beams[:beam_size]
except:
beams = new_beams
beams = new_beams + complete_beams
beams.sort(key=lambda x: x[1], reverse=True)
best_beam = beams[0][0]
return best_beam
def translate(model: torch.nn.Module, strategy:str = 'greedy' , src_sentence: str, lenght_extend :int = 5, beam_size: int = 5, length_penalty:float = 0.6):
assert strategy in ['greedy','beam search'], 'the strategy for decoding has to be either greedy or beam search'
# Tokenize the source sentence
src = source_tokenizer(src_sentence, **token_config)['input_ids']
num_tokens = src.shape[1]
# Create a source mask
src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)
if strategy == 'greedy':
tgt_tokens = greedy_decode(model, src, src_mask, max_len=num_tokens + lenght_extend, start_symbol=target_tokenizer.bos_token_id).flatten()
# Generate the target tokens using beam search decoding
else:
tgt_tokens = beam_search_decode(model, src, src_mask, max_len=num_tokens + lenght_extend, start_symbol=target_tokenizer.bos_token_id, beam_size=beam_size,length_penalty=length_penalty).flatten()
# Decode the target tokens and clean up the result
return target_tokenizer.decode(tgt_tokens, clean_up_tokenization_spaces=True, skip_special_tokens=True)
special_tokens = {'unk_token':"[UNK]",
'cls_token':"[CLS]",
'eos_token': '[EOS]',
'sep_token':"[SEP]",
'pad_token':"[PAD]",
'mask_token':"[MASK]",
'bos_token':"[BOS]"}
source_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", **special_tokens)
target_tokenizer = PreTrainedTokenizerFast.from_pretrained('Sifal/E2KT')
addPreprocessing(source_tokenizer)
addPreprocessing(target_tokenizer)
token_config = {
"add_special_tokens": True,
"return_tensors": True,
}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model()
model.to(device)
model.eval() |