Source code for gnes.encoder.text.torch_transformers
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# pylint: disable=low-comment-ratio
from typing import List
import numpy as np
from ..base import BaseTextEncoder
from ...helper import batching
[docs]class TorchTransformersEncoder(BaseTextEncoder):
is_trained = True
def __init__(self, model_dir: str,
model_name: str,
tokenizer_name: str,
use_cuda: bool = False,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.model_dir = model_dir
self.model_name = model_name
self.tokenizer_name = tokenizer_name
self.use_cuda = use_cuda
[docs] def post_init(self):
import pytorch_transformers as ptt
self.tokenizer = getattr(ptt, self.tokenizer_name).from_pretrained(self.model_dir)
self.model = getattr(ptt, self.model_name).from_pretrained(self.model_dir)
[docs] @batching
def encode(self, text: List[str], *args, **kwargs) -> np.ndarray:
import torch
batch_size = len(text)
# tokenize text
tokens_ids = []
tokens_lens = []
max_len = 0
for _ in text:
# Convert token to vocabulary indices
token_ids = self.tokenizer.encode(_)
token_len = len(token_ids)
if max_len < token_len:
max_len = token_len
tokens_ids.append(token_ids)
tokens_lens.append(token_len)
batch_data = np.zeros([batch_size, max_len], dtype=np.int64)
# batch_mask = np.zeros([batch_size, max_len], dtype=np.float32)
for i, ids in enumerate(tokens_ids):
batch_data[i, :tokens_lens[i]] = ids
# batch_mask[i, :tokens_lens[i]] = 1
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor(batch_data)
tokens_lens = torch.LongTensor(tokens_lens)
mask_tensor = torch.arange(max_len)[None, :] < tokens_lens[:, None]
mask_tensor = mask_tensor.to(
mask_tensor.device, dtype=torch.float32)
if self.use_cuda:
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
mask_tensor = mask_tensor.to('cuda')
with torch.no_grad():
out_tensor = self.model(tokens_tensor)[0]
out_tensor = torch.mul(out_tensor, mask_tensor.unsqueeze(2))
if self.use_cuda:
out_tensor = out_tensor.cpu()
return out_tensor.numpy()