wehub-resource-sync
266 lines
12 KiB
Python
266 lines
12 KiB
Python
# coding:utf-8
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import paddle
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from ..data import Pad, Stack, Tuple, Vocab
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from ..transformers import ErnieModel, ErnieTokenizer, is_chinese_char
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from .models import ErnieForCSC
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from .task import Task
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from .utils import static_mode_guard
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usage = r"""
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from paddlenlp import Taskflow
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text_correction = Taskflow("text_correction")
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text_correction('遇到逆竟时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。')
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'''
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[{'source': '遇到逆竟时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。',
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'target': '遇到逆境时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。',
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'errors': [{'position': 3, 'correction': {'竟': '境'}}]}
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]
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'''
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text_correction(['遇到逆竟时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。',
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'人生就是如此，经过磨练才能让自己更加拙壮，才能使自己更加乐观。'])
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'''
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[{'source': '遇到逆竟时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。',
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'target': '遇到逆境时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。',
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'errors': [{'position': 3, 'correction': {'竟': '境'}}]},
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{'source': '人生就是如此，经过磨练才能让自己更加拙壮，才能使自己更加乐观。',
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'target': '人生就是如此，经过磨练才能让自己更加茁壮，才能使自己更加乐观。',
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'errors': [{'position': 18, 'correction': {'拙': '茁'}}]}
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]
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'''
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"""
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TASK_MODEL_MAP = {"ernie-csc": "ernie-1.0"}
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class CSCTask(Task):
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"""
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The text generation model to predict the question or chinese poetry.
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Args:
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task(string): The name of task.
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model(string): The model name in the task.
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kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
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"""
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resource_files_names = {"model_state": "model_state.pdparams", "pinyin_vocab": "pinyin_vocab.txt"}
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resource_files_urls = {
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"ernie-csc": {
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"model_state": [
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"https://bj.bcebos.com/paddlenlp/taskflow/text_correction/ernie-csc/model_state.pdparams",
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"cdc53e7e3985ffc78fedcdf8e6dca6d2",
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],
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"pinyin_vocab": [
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"https://bj.bcebos.com/paddlenlp/taskflow/text_correction/ernie-csc/pinyin_vocab.txt",
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"5599a8116b6016af573d08f8e686b4b2",
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],
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}
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}
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def __init__(self, task, model, **kwargs):
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super().__init__(task=task, model=model, **kwargs)
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self._usage = usage
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self._check_task_files()
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self._construct_vocabs()
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self._get_inference_model()
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self._construct_tokenizer(model)
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try:
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import pypinyin
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except ImportError:
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raise ImportError("Please install the dependencies first, pip install pypinyin --upgrade")
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self._pypinyin = pypinyin
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self._batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=self._tokenizer.pad_token_id, dtype="int64"), # input
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Pad(axis=0, pad_val=self._tokenizer.pad_token_type_id, dtype="int64"), # segment
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Pad(
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axis=0, pad_val=self._pinyin_vocab.token_to_idx[self._pinyin_vocab.pad_token], dtype="int64"
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), # pinyin
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Stack(axis=0, dtype="int64"), # length
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): [data for data in fn(samples)]
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self._num_workers = self.kwargs["num_workers"] if "num_workers" in self.kwargs else 0
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self._batch_size = self.kwargs["batch_size"] if "batch_size" in self.kwargs else 1
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self._lazy_load = self.kwargs["lazy_load"] if "lazy_load" in self.kwargs else False
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self._max_seq_len = self.kwargs["max_seq_len"] if "max_seq_len" in self.kwargs else 128
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self._split_sentence = self.kwargs["split_sentence"] if "split_sentence" in self.kwargs else False
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def _construct_input_spec(self):
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"""
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Construct the input spec for the convert dygraph model to static model.
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"""
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self._input_spec = [
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paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
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paddle.static.InputSpec(shape=[None, None], dtype="int64", name="pinyin_ids"),
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]
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def _construct_vocabs(self):
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pinyin_vocab_path = os.path.join(self._task_path, "pinyin_vocab.txt")
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self._pinyin_vocab = Vocab.load_vocabulary(pinyin_vocab_path, unk_token="[UNK]", pad_token="[PAD]")
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def _construct_model(self, model):
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"""
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Construct the inference model for the predictor.
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"""
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ernie = ErnieModel.from_pretrained(TASK_MODEL_MAP[model])
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model_instance = ErnieForCSC(
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ernie,
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pinyin_vocab_size=len(self._pinyin_vocab),
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pad_pinyin_id=self._pinyin_vocab[self._pinyin_vocab.pad_token],
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)
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# Load the model parameter for the predict
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model_path = os.path.join(self._task_path, "model_state.pdparams")
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state_dict = paddle.load(model_path)
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model_instance.set_state_dict(state_dict)
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self._model = model_instance
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self._model.eval()
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def _construct_tokenizer(self, model):
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"""
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Construct the tokenizer for the predictor.
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"""
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self._tokenizer = ErnieTokenizer.from_pretrained(TASK_MODEL_MAP[model])
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def _preprocess(self, inputs, padding=True, add_special_tokens=True):
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input_texts = self._check_input_text(inputs)
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examples = []
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texts = []
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max_predict_len = self._max_seq_len - 2
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short_input_texts, self.input_mapping = self._auto_splitter(
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input_texts, max_predict_len, split_sentence=self._split_sentence
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)
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for text in short_input_texts:
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if not (isinstance(text, str) and len(text) > 0):
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continue
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example = {"source": text.strip()}
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input_ids, token_type_ids, pinyin_ids, length = self._convert_example(example)
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examples.append((input_ids, token_type_ids, pinyin_ids, length))
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texts.append(example["source"])
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batch_examples = [examples[idx : idx + self._batch_size] for idx in range(0, len(examples), self._batch_size)]
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batch_texts = [
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short_input_texts[idx : idx + self._batch_size] for idx in range(0, len(examples), self._batch_size)
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]
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outputs = {}
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outputs["batch_examples"] = batch_examples
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outputs["batch_texts"] = batch_texts
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return outputs
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def _run_model(self, inputs):
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"""
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Run the task model from the outputs of the `_tokenize` function.
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"""
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results = []
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with static_mode_guard():
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for examples in inputs["batch_examples"]:
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token_ids, token_type_ids, pinyin_ids, lengths = self._batchify_fn(examples)
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self.input_handles[0].copy_from_cpu(token_ids)
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self.input_handles[1].copy_from_cpu(pinyin_ids)
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self.predictor.run()
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det_preds = self.output_handle[0].copy_to_cpu()
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char_preds = self.output_handle[1].copy_to_cpu()
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batch_result = []
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for i in range(len(lengths)):
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batch_result.append((det_preds[i], char_preds[i], lengths[i]))
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results.append(batch_result)
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inputs["batch_results"] = results
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return inputs
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def _postprocess(self, inputs):
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"""
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The model output is the logits and probs, this function will convert the model output to raw text.
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"""
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results = []
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for examples, texts, temp_results in zip(
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inputs["batch_examples"], inputs["batch_texts"], inputs["batch_results"]
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):
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for i in range(len(examples)):
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result = {}
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det_pred, char_preds, length = temp_results[i]
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pred_result = self._parse_decode(texts[i], char_preds, det_pred, length)
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result["source"] = texts[i]
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result["target"] = "".join(pred_result)
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results.append(result)
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results = self._auto_joiner(results, self.input_mapping, is_dict=True)
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for result in results:
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errors_result = []
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for i, (source_token, target_token) in enumerate(zip(result["source"], result["target"])):
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if source_token != target_token:
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errors_result.append({"position": i, "correction": {source_token: target_token}})
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result["errors"] = errors_result
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return results
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def _convert_example(self, example):
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source = example["source"]
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words = list(source)
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length = len(words)
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words = ["[CLS]"] + words + ["[SEP]"]
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input_ids = self._tokenizer.convert_tokens_to_ids(words)
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token_type_ids = [0] * len(input_ids)
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# Use pad token in pinyin emb to map word emb [CLS], [SEP]
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pinyins = self._pypinyin.lazy_pinyin(source, style=self._pypinyin.Style.TONE3, neutral_tone_with_five=True)
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pinyin_ids = [0]
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# Align pinyin and chinese char
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pinyin_offset = 0
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for i, word in enumerate(words[1:-1]):
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pinyin = "[UNK]" if word != "[PAD]" else "[PAD]"
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if len(word) == 1 and is_chinese_char(ord(word)):
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while pinyin_offset < len(pinyins):
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current_pinyin = pinyins[pinyin_offset][:-1]
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pinyin_offset += 1
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if current_pinyin in self._pinyin_vocab:
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pinyin = current_pinyin
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break
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pinyin_ids.append(self._pinyin_vocab[pinyin])
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pinyin_ids.append(0)
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assert len(input_ids) == len(pinyin_ids), "length of input_ids must be equal to length of pinyin_ids"
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return input_ids, token_type_ids, pinyin_ids, length
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def _parse_decode(self, words, corr_preds, det_preds, lengths):
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UNK = self._tokenizer.unk_token
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UNK_id = self._tokenizer.convert_tokens_to_ids(UNK)
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corr_pred = corr_preds[1 : 1 + lengths].tolist()
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det_pred = det_preds[1 : 1 + lengths].tolist()
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words = list(words)
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rest_words = []
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max_seq_length = self._max_seq_len - 2
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if len(words) > max_seq_length:
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rest_words = words[max_seq_length:]
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words = words[:max_seq_length]
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pred_result = ""
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for j, word in enumerate(words):
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candidates = self._tokenizer.convert_ids_to_tokens(
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corr_pred[j] if corr_pred[j] < self._tokenizer.vocab_size else UNK_id
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)
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word_icc = is_chinese_char(ord(word))
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cand_icc = is_chinese_char(ord(candidates)) if len(candidates) == 1 else False
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if not word_icc or det_pred[j] == 0 or candidates in [UNK, "[PAD]"] or (word_icc and not cand_icc):
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pred_result += word
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else:
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pred_result += candidates.lstrip("##")
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pred_result += "".join(rest_words)
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return pred_result
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