uie_predictor.py

# Copyright (c) 2022 Heiheiyoyo. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import six

from transformers import BertTokenizerFast
import math
import argparse
import os.path

from utils import logger, get_bool_ids_greater_than, get_span, get_id_and_prob, cut_chinese_sent, dbc2sbc


class ONNXInferBackend(object):
    def __init__(self,
                 model_path_prefix,
                 device='cpu',
                 use_fp16=False):
        from onnxruntime import InferenceSession, SessionOptions
        logger.info(">>> [ONNXInferBackend] Creating Engine ...")
        onnx_model = float_onnx_file = os.path.join(
            model_path_prefix, "inference.onnx")
        if not os.path.exists(onnx_model):
            raise OSError(f'{onnx_model} not exists!')
        infer_model_dir = model_path_prefix

        if device == "gpu":
            providers = ['CUDAExecutionProvider']
            logger.info(">>> [ONNXInferBackend] Use GPU to inference ...")
            if use_fp16:
                logger.info(">>> [ONNXInferBackend] Use FP16 to inference ...")
                from onnxconverter_common import float16
                import onnx
                fp16_model_file = os.path.join(infer_model_dir,
                                               "fp16_model.onnx")
                onnx_model = onnx.load_model(float_onnx_file)
                trans_model = float16.convert_float_to_float16(
                    onnx_model, keep_io_types=True)
                onnx.save_model(trans_model, fp16_model_file)
                onnx_model = fp16_model_file
        else:
            providers = ['CPUExecutionProvider']
            logger.info(">>> [ONNXInferBackend] Use CPU to inference ...")

        sess_options = SessionOptions()
        self.predictor = InferenceSession(
            onnx_model, sess_options=sess_options, providers=providers)
        if device == "gpu":
            try:
                assert 'CUDAExecutionProvider' in self.predictor.get_providers()
            except AssertionError:
                raise AssertionError(
                    f"The environment for GPU inference is not set properly. "
                    "A possible cause is that you had installed both onnxruntime and onnxruntime-gpu. "
                    "Please run the following commands to reinstall: \n "
                    "1) pip uninstall -y onnxruntime onnxruntime-gpu \n 2) pip install onnxruntime-gpu"
                )
        logger.info(">>> [InferBackend] Engine Created ...")

    def infer(self, input_dict: dict):
        result = self.predictor.run(None, dict(input_dict))
        return result


class PyTorchInferBackend:
    def __init__(self,
                 model_path_prefix,
                 device='cpu',
                 use_fp16=False):
        from model import UIE
        logger.info(">>> [PyTorchInferBackend] Creating Engine ...")
        self.model = UIE.from_pretrained(model_path_prefix)
        self.model.eval()
        self.device = device
        if self.device == 'gpu':
            logger.info(">>> [PyTorchInferBackend] Use GPU to inference ...")
            if use_fp16:
                logger.info(
                    ">>> [PyTorchInferBackend] Use FP16 to inference ...")
                self.model = self.model.half()
            self.model = self.model.cuda()
        else:
            logger.info(">>> [PyTorchInferBackend] Use CPU to inference ...")
        logger.info(">>> [PyTorchInferBackend] Engine Created ...")

    def infer(self, input_dict):
        import torch
        for input_name, input_value in input_dict.items():
            input_value = torch.LongTensor(input_value)
            if self.device == 'gpu':
                input_value = input_value.cuda()
            input_dict[input_name] = input_value

        outputs = self.model(**input_dict)
        start_prob, end_prob = outputs[0], outputs[1]
        if self.device == 'gpu':
            start_prob, end_prob = start_prob.cpu(), end_prob.cpu()
        start_prob = start_prob.detach().numpy()
        end_prob = end_prob.detach().numpy()
        return start_prob, end_prob


class UIEPredictor(object):

    def __init__(self, task_path, schema, engine='pytorch', device='cpu', position_prob=0.5, max_seq_len=512, batch_size=64, split_sentence=False, use_fp16=False):

        assert isinstance(
            device, six.string_types
        ), "The type of device must be string."
        assert device in [
            'cpu', 'gpu'], "The device must be cpu or gpu."
        self._engine = engine
        self._task_path = task_path
        self._device = device
        self._position_prob = position_prob
        self._max_seq_len = max_seq_len
        self._batch_size = 64
        self._split_sentence = False
        self._use_fp16 = use_fp16

        self._schema_tree = None
        self.set_schema(schema)
        self._prepare_predictor()

    def _prepare_predictor(self):
        self._tokenizer = BertTokenizerFast.from_pretrained(
            self._task_path)
        assert self._engine in ['pytorch',
                                'onnx'], "engine must be pytorch or onnx!"
        if self._engine == 'pytorch':
            self.inference_backend = PyTorchInferBackend(
                self._task_path, device=self._device, use_fp16=self._use_fp16)

        if self._engine == 'onnx':
            self.inference_backend = ONNXInferBackend(
                self._task_path, device=self._device, use_fp16=self._use_fp16)

    def set_schema(self, schema):
        if isinstance(schema, dict) or isinstance(schema, str):
            schema = [schema]
        self._schema_tree = self._build_tree(schema)

    def __call__(self, inputs):
        texts = inputs
        if isinstance(texts, str):
            texts = [texts]
        results = self._multi_stage_predict(texts)
        return results

    def _multi_stage_predict(self, datas):
        """
        Traversal the schema tree and do multi-stage prediction.
        Args:
            datas (list): a list of strings
        Returns:
            list: a list of predictions, where the list's length
                equals to the length of `datas`
        """
        results = [{} for _ in range(len(datas))]
        # input check to early return
        if len(datas) < 1 or self._schema_tree is None:
            return results

        # copy to stay `self._schema_tree` unchanged
        schema_list = self._schema_tree.children[:]
        while len(schema_list) > 0:
            node = schema_list.pop(0)
            examples = []
            input_map = {}
            cnt = 0
            idx = 0
            if not node.prefix:
                for data in datas:
                    examples.append({
                        "text": data,
                        "prompt": dbc2sbc(node.name)
                    })
                    input_map[cnt] = [idx]
                    idx += 1
                    cnt += 1
            else:
                for pre, data in zip(node.prefix, datas):
                    if len(pre) == 0:
                        input_map[cnt] = []
                    else:
                        for p in pre:
                            examples.append({
                                "text": data,
                                "prompt": dbc2sbc(p + node.name)
                            })
                        input_map[cnt] = [i + idx for i in range(len(pre))]
                        idx += len(pre)
                    cnt += 1
            if len(examples) == 0:
                result_list = []
            else:
                result_list = self._single_stage_predict(examples)

            if not node.parent_relations:
                relations = [[] for i in range(len(datas))]
                for k, v in input_map.items():
                    for idx in v:
                        if len(result_list[idx]) == 0:
                            continue
                        if node.name not in results[k].keys():
                            results[k][node.name] = result_list[idx]
                        else:
                            results[k][node.name].extend(result_list[idx])
                    if node.name in results[k].keys():
                        relations[k].extend(results[k][node.name])
            else:
                relations = node.parent_relations
                for k, v in input_map.items():
                    for i in range(len(v)):
                        if len(result_list[v[i]]) == 0:
                            continue
                        if "relations" not in relations[k][i].keys():
                            relations[k][i]["relations"] = {
                                node.name: result_list[v[i]]
                            }
                        elif node.name not in relations[k][i]["relations"].keys(
                        ):
                            relations[k][i]["relations"][
                                node.name] = result_list[v[i]]
                        else:
                            relations[k][i]["relations"][node.name].extend(
                                result_list[v[i]])

                new_relations = [[] for i in range(len(datas))]
                for i in range(len(relations)):
                    for j in range(len(relations[i])):
                        if "relations" in relations[i][j].keys(
                        ) and node.name in relations[i][j]["relations"].keys():
                            for k in range(
                                    len(relations[i][j]["relations"][
                                        node.name])):
                                new_relations[i].append(relations[i][j][
                                    "relations"][node.name][k])
                relations = new_relations

            prefix = [[] for _ in range(len(datas))]
            for k, v in input_map.items():
                for idx in v:
                    for i in range(len(result_list[idx])):
                        prefix[k].append(result_list[idx][i]["text"] + "的")

            for child in node.children:
                child.prefix = prefix
                child.parent_relations = relations
                schema_list.append(child)
        return results

    def _convert_ids_to_results(self, examples, sentence_ids, probs):
        """
        Convert ids to raw text in a single stage.
        """
        results = []
        for example, sentence_id, prob in zip(examples, sentence_ids, probs):
            if len(sentence_id) == 0:
                results.append([])
                continue
            result_list = []
            text = example["text"]
            prompt = example["prompt"]
            for i in range(len(sentence_id)):
                start, end = sentence_id[i]
                if start < 0 and end >= 0:
                    continue
                if end < 0:
                    start += (len(prompt) + 1)
                    end += (len(prompt) + 1)
                    result = {"text": prompt[start:end],
                              "probability": prob[i]}
                    result_list.append(result)
                else:
                    result = {
                        "text": text[start:end],
                        "start": start,
                        "end": end,
                        "probability": prob[i]
                    }
                    result_list.append(result)
            results.append(result_list)
        return results

    def _auto_splitter(self, input_texts, max_text_len, split_sentence=False):
        '''
        Split the raw texts automatically for model inference.
        Args:
            input_texts (List[str]): input raw texts.
            max_text_len (int): cutting length.
            split_sentence (bool): If True, sentence-level split will be performed.
        return:
            short_input_texts (List[str]): the short input texts for model inference.
            input_mapping (dict): mapping between raw text and short input texts.
        '''
        input_mapping = {}
        short_input_texts = []
        cnt_org = 0
        cnt_short = 0
        for text in input_texts:
            if not split_sentence:
                sens = [text]
            else:
                sens = cut_chinese_sent(text)
            for sen in sens:
                lens = len(sen)
                if lens <= max_text_len:
                    short_input_texts.append(sen)
                    if cnt_org not in input_mapping.keys():
                        input_mapping[cnt_org] = [cnt_short]
                    else:
                        input_mapping[cnt_org].append(cnt_short)
                    cnt_short += 1
                else:
                    temp_text_list = [
                        sen[i:i + max_text_len]
                        for i in range(0, lens, max_text_len)
                    ]
                    short_input_texts.extend(temp_text_list)
                    short_idx = cnt_short
                    cnt_short += math.ceil(lens / max_text_len)
                    temp_text_id = [
                        short_idx + i for i in range(cnt_short - short_idx)
                    ]
                    if cnt_org not in input_mapping.keys():
                        input_mapping[cnt_org] = temp_text_id
                    else:
                        input_mapping[cnt_org].extend(temp_text_id)
            cnt_org += 1
        return short_input_texts, input_mapping

    def _single_stage_predict(self, inputs):
        input_texts = []
        prompts = []
        for i in range(len(inputs)):
            input_texts.append(inputs[i]["text"])
            prompts.append(inputs[i]["prompt"])
        # max predict length should exclude the length of prompt and summary tokens
        max_predict_len = self._max_seq_len - len(max(prompts)) - 3

        short_input_texts, self.input_mapping = self._auto_splitter(
            input_texts, max_predict_len, split_sentence=self._split_sentence)

        short_texts_prompts = []
        for k, v in self.input_mapping.items():
            short_texts_prompts.extend([prompts[k] for i in range(len(v))])
        short_inputs = [{
            "text": short_input_texts[i],
            "prompt": short_texts_prompts[i]
        } for i in range(len(short_input_texts))]

        sentence_ids = []
        probs = []

        input_ids = []
        token_type_ids = []
        attention_mask = []
        offset_maps = []

        encoded_inputs = self._tokenizer(
            text=short_texts_prompts,
            text_pair=short_input_texts,
            stride=2,
            truncation=True,
            max_length=self._max_seq_len,
            padding="longest",
            add_special_tokens=True,
            return_offsets_mapping=True,
            return_tensors="np")

        start_prob_concat, end_prob_concat = [], []
        for batch_start in range(0, len(short_input_texts), self._batch_size):
            input_ids = encoded_inputs["input_ids"][batch_start:batch_start+self._batch_size]
            token_type_ids = encoded_inputs["token_type_ids"][batch_start:batch_start+self._batch_size]
            attention_mask = encoded_inputs["attention_mask"][batch_start:batch_start+self._batch_size]
            offset_maps = encoded_inputs["offset_mapping"][batch_start:batch_start+self._batch_size]
            input_dict = {
                "input_ids": np.array(
                    input_ids, dtype="int64"),
                "token_type_ids": np.array(
                    token_type_ids, dtype="int64"),
                "attention_mask": np.array(
                    attention_mask, dtype="int64")
            }

            outputs = self.inference_backend.infer(input_dict)
            start_prob, end_prob = outputs[0], outputs[1]
            start_prob_concat.append(start_prob)
            end_prob_concat.append(end_prob)
        start_prob_concat = np.concatenate(start_prob_concat)
        end_prob_concat = np.concatenate(end_prob_concat)

        start_ids_list = get_bool_ids_greater_than(
            start_prob_concat, limit=self._position_prob, return_prob=True)
        end_ids_list = get_bool_ids_greater_than(
            end_prob_concat, limit=self._position_prob, return_prob=True)

        input_ids = input_dict['input_ids']
        sentence_ids = []
        probs = []
        for start_ids, end_ids, ids, offset_map in zip(start_ids_list,
                                                       end_ids_list,
                                                       input_ids.tolist(),
                                                       offset_maps):
            for i in reversed(range(len(ids))):
                if ids[i] != 0:
                    ids = ids[:i]
                    break
            span_list = get_span(start_ids, end_ids, with_prob=True)
            sentence_id, prob = get_id_and_prob(span_list, offset_map.tolist())
            sentence_ids.append(sentence_id)
            probs.append(prob)

        results = self._convert_ids_to_results(short_inputs, sentence_ids,
                                               probs)
        results = self._auto_joiner(results, short_input_texts,
                                    self.input_mapping)
        return results

    def _auto_joiner(self, short_results, short_inputs, input_mapping):
        concat_results = []
        is_cls_task = False
        for short_result in short_results:
            if short_result == []:
                continue
            elif 'start' not in short_result[0].keys(
            ) and 'end' not in short_result[0].keys():
                is_cls_task = True
                break
            else:
                break
        for k, vs in input_mapping.items():
            if is_cls_task:
                cls_options = {}
                single_results = []
                for v in vs:
                    if len(short_results[v]) == 0:
                        continue
                    if short_results[v][0]['text'] not in cls_options.keys():
                        cls_options[short_results[v][0][
                            'text']] = [1, short_results[v][0]['probability']]
                    else:
                        cls_options[short_results[v][0]['text']][0] += 1
                        cls_options[short_results[v][0]['text']][
                            1] += short_results[v][0]['probability']
                if len(cls_options) != 0:
                    cls_res, cls_info = max(cls_options.items(),
                                            key=lambda x: x[1])
                    concat_results.append([{
                        'text': cls_res,
                        'probability': cls_info[1] / cls_info[0]
                    }])
                else:
                    concat_results.append([])
            else:
                offset = 0
                single_results = []
                for v in vs:
                    if v == 0:
                        single_results = short_results[v]
                        offset += len(short_inputs[v])
                    else:
                        for i in range(len(short_results[v])):
                            if 'start' not in short_results[v][
                                    i] or 'end' not in short_results[v][i]:
                                continue
                            short_results[v][i]['start'] += offset
                            short_results[v][i]['end'] += offset
                        offset += len(short_inputs[v])
                        single_results.extend(short_results[v])
                concat_results.append(single_results)
        return concat_results

    def predict(self, input_data):
        results = self._multi_stage_predict(input_data)
        return results

    @classmethod
    def _build_tree(cls, schema, name='root'):
        """
        Build the schema tree.
        """
        schema_tree = SchemaTree(name)
        for s in schema:
            if isinstance(s, str):
                schema_tree.add_child(SchemaTree(s))
            elif isinstance(s, dict):
                for k, v in s.items():
                    if isinstance(v, str):
                        child = [v]
                    elif isinstance(v, list):
                        child = v
                    else:
                        raise TypeError(
                            "Invalid schema, value for each key:value pairs should be list or string"
                            "but {} received".format(type(v)))
                    schema_tree.add_child(cls._build_tree(child, name=k))
            else:
                raise TypeError(
                    "Invalid schema, element should be string or dict, "
                    "but {} received".format(type(s)))
        return schema_tree


class SchemaTree(object):
    """
    Implementataion of SchemaTree
    """

    def __init__(self, name='root', children=None):
        self.name = name
        self.children = []
        self.prefix = None
        self.parent_relations = None
        if children is not None:
            for child in children:
                self.add_child(child)

    def __repr__(self):
        return self.name

    def add_child(self, node):
        assert isinstance(
            node, SchemaTree
        ), "The children of a node should be an instacne of SchemaTree."
        self.children.append(node)


def parse_args():
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "-m",
        "--model_path_prefix",
        type=str,
        default='uie_base_pytorch',
        help="The path prefix of inference model to be used.", )
    parser.add_argument(
        "-p",
        "--position_prob",
        default=0.5,
        type=float,
        help="Probability threshold for start/end index probabiliry.", )
    parser.add_argument(
        "--use_fp16",
        action='store_true',
        help="Whether to use fp16 inference, only takes effect when deploying on gpu.",
    )
    parser.add_argument(
        "--max_seq_len",
        default=512,
        type=int,
        help="The maximum input sequence length. Sequences longer than this will be split automatically.",
    )
    parser.add_argument(
        "-D",
        "--device",
        choices=['cpu', 'gpu'],
        default="gpu",
        help="Select which device to run model, defaults to gpu."
    )
    parser.add_argument(
        "-e",
        "--engine",
        choices=['pytorch', 'onnx'],
        default="pytorch",
        help="Select which engine to run model, defaults to pytorch."
    )
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()
    args.schema = ['航母']
    uie = UIEPredictor(task_path=args.model_path_prefix, schema=args.schema, engine=args.engine, device=args.device,
                       position_prob=args.position_prob, max_seq_len=args.max_seq_len, batch_size=64, split_sentence=False, use_fp16=args.use_fp16)
    print(uie("印媒所称的“印度第一艘国产航母”—“维克兰特”号"))