wehub-resource-sync
217 lines
8.4 KiB
Python
217 lines
8.4 KiB
Python
# Most codes of this file is adopted from flair, which is licenced under:
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#
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# The MIT License (MIT)
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#
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# Flair is licensed under the following MIT License (MIT) Copyright © 2018 Zalando SE, https://tech.zalando.com
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# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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import os
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from typing import List, Dict, Callable
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import torch
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import torch.nn as nn
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from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
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from hanlp_common.configurable import Configurable
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from hanlp.common.transform import TransformList, FieldToIndex
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from hanlp.common.vocab import Vocab
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from hanlp.layers.embeddings.embedding import Embedding, EmbeddingDim
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from hanlp.utils.io_util import get_resource
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from hanlp.utils.torch_util import pad_lists, batched_index_select
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from tests import cdroot
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class RNNLanguageModel(nn.Module):
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"""Container module with an encoder, a recurrent module, and a decoder."""
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def __init__(self,
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n_tokens,
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is_forward_lm: bool,
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hidden_size: int,
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embedding_size: int = 100):
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super(RNNLanguageModel, self).__init__()
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self.is_forward_lm: bool = is_forward_lm
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self.n_tokens = n_tokens
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self.hidden_size = hidden_size
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self.embedding_size = embedding_size
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self.encoder = nn.Embedding(n_tokens, embedding_size)
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self.rnn = nn.LSTM(embedding_size, hidden_size, batch_first=True)
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def forward(self, ids: torch.LongTensor, lens: torch.LongTensor):
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emb = self.encoder(ids)
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x = pack_padded_sequence(emb, lens, True, False)
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x, _ = self.rnn(x)
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x, _ = pad_packed_sequence(x, True)
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return x
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@classmethod
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def load_language_model(cls, model_file):
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model_file = get_resource(model_file)
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state = torch.load(model_file)
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model = RNNLanguageModel(state['n_tokens'],
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state['is_forward_lm'],
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state['hidden_size'],
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state['embedding_size'])
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model.load_state_dict(state['state_dict'], strict=False)
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return model
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def save(self, file):
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model_state = {
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'state_dict': self.state_dict(),
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'n_tokens': self.n_tokens,
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'is_forward_lm': self.is_forward_lm,
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'hidden_size': self.hidden_size,
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'embedding_size': self.embedding_size,
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}
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torch.save(model_state, file, pickle_protocol=4)
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class ContextualStringEmbeddingModule(nn.Module, EmbeddingDim):
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def __init__(self, field: str, path: str, trainable=False) -> None:
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super().__init__()
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self.field = field
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path = get_resource(path)
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f = os.path.join(path, 'forward.pt')
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b = os.path.join(path, 'backward.pt')
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self.f: RNNLanguageModel = RNNLanguageModel.load_language_model(f)
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self.b: RNNLanguageModel = RNNLanguageModel.load_language_model(b)
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if not trainable:
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for p in self.parameters():
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p.requires_grad_(False)
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def __call__(self, batch: dict, **kwargs):
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args = ['f_char_id', 'f_offset', 'b_char_id', 'b_offset']
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keys = [f'{self.field}_{key}' for key in args]
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args = [batch[key] for key in keys]
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return super().__call__(*args, **kwargs)
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@property
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def embedding_dim(self):
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return self.f.rnn.hidden_size + self.b.rnn.hidden_size
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def run_lm(self, lm, ids: torch.Tensor, offsets: torch.LongTensor):
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lens = offsets.max(-1)[0] + 1
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rnn_output = lm(ids, lens)
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return batched_index_select(rnn_output, offsets)
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def forward(self,
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f_chars_id: torch.Tensor,
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f_offset: torch.LongTensor,
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b_chars_id: torch.Tensor,
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b_offset: torch.LongTensor, **kwargs):
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f = self.run_lm(self.f, f_chars_id, f_offset)
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b = self.run_lm(self.b, b_chars_id, b_offset)
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return torch.cat([f, b], dim=-1)
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def embed(self, sents: List[List[str]], vocab: Dict[str, int]):
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f_chars, f_offsets = [], []
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b_chars, b_offsets = [], []
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transform = ContextualStringEmbeddingTransform('token')
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for tokens in sents:
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sample = transform({'token': tokens})
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for each, name in zip([f_chars, b_chars, f_offsets, b_offsets],
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'f_chars, b_chars, f_offsets, b_offsets'.split(', ')):
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each.append(sample[f'token_{name}'])
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f_ids = []
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for cs in f_chars:
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f_ids.append([vocab[c] for c in cs])
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f_ids = pad_lists(f_ids)
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f_offsets = pad_lists(f_offsets)
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b_ids = []
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for cs in b_chars:
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b_ids.append([vocab[c] for c in cs])
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b_ids = pad_lists(b_ids)
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b_offsets = pad_lists(b_offsets)
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return self.forward(f_ids, f_offsets, b_ids, b_offsets)
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class ContextualStringEmbeddingTransform(Configurable):
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def __init__(self, src: str) -> None:
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self.src = src
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def __call__(self, sample: dict):
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tokens = sample[self.src]
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f_o = []
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b_o = []
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sentence_text = ' '.join(tokens)
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end_marker = ' '
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extra_offset = 1
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# f
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input_text = '\n' + sentence_text + end_marker
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f_chars = list(input_text)
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# b
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sentence_text = sentence_text[::-1]
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input_text = '\n' + sentence_text + end_marker
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b_chars = list(input_text)
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offset_forward: int = extra_offset
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offset_backward: int = len(sentence_text) + extra_offset
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for token in tokens:
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offset_forward += len(token)
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f_o.append(offset_forward)
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b_o.append(offset_backward)
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# This language model is tokenized
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offset_forward += 1
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offset_backward -= 1
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offset_backward -= len(token)
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sample[f'{self.src}_f_char'] = f_chars
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sample[f'{self.src}_b_char'] = b_chars
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sample[f'{self.src}_f_offset'] = f_o
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sample[f'{self.src}_b_offset'] = b_o
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return sample
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class ContextualStringEmbedding(Embedding):
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def __init__(self, field, path, trainable=False) -> None:
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super().__init__()
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self.trainable = trainable
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self.path = path
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self.field = field
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def transform(self, **kwargs) -> Callable:
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vocab = Vocab()
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vocab.load(os.path.join(get_resource(self.path), 'vocab.json'))
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return TransformList(ContextualStringEmbeddingTransform(self.field),
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FieldToIndex(f'{self.field}_f_char', vocab),
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FieldToIndex(f'{self.field}_b_char', vocab))
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def module(self, **kwargs) -> nn.Module:
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return ContextualStringEmbeddingModule(self.field, self.path, self.trainable)
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def main():
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# _validate()
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flair = ContextualStringEmbedding('token', 'FASTTEXT_DEBUG_EMBEDDING_EN')
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print(flair.config)
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def _validate():
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cdroot()
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flair = ContextualStringEmbeddingModule('token', 'FLAIR_LM_WMT11_EN')
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vocab = torch.load('/home/hhe43/flair/item2idx.pt')
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vocab = dict((x.decode(), y) for x, y in vocab.items())
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# vocab = Vocab(token_to_idx=vocab, pad_token='<unk>')
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# vocab.lock()
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# vocab.summary()
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# vocab.save('vocab.json')
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tokens = 'I love Berlin .'.split()
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sent = ' '.join(tokens)
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embed = flair.embed([tokens, tokens], vocab)
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gold = torch.load('/home/hhe43/flair/gold.pt')
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print(torch.allclose(embed[1, :, :2048], gold, atol=1e-6))
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# print(torch.all(torch.eq(embed[1, :, :], gold)))
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if __name__ == '__main__':
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main()
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