wrapper.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""BERT (PaddlePaddle) model wrapper"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import json
import collections
import multiprocessing
import argparse
import numpy as np
import paddle.fluid as fluid
from squad_reader import DataProcessor, get_answers
from model.xlnet import XLNetConfig, XLNetModel


conf_dir = "xlnet_config"
bert_config_path = conf_dir+'/xlnet_config.json'
spiece_model_file = conf_dir+'/spiece.model'
ema_decay = 0.9999
verbose = False
vocab_path = conf_dir+'/vocab.txt'
max_seq_len = 800
max_query_length = 64
max_answer_length = 30
in_tokens = False
do_lower_case = False
doc_stride = 128
n_best_size = 20
start_n_top = 5
end_n_top = 5
use_cuda = True


class BertModelWrapper():
    """
    Wrap a tnet model
     the basic processes include input checking, preprocessing, calling tf-serving
     and postprocessing
    """
    def __init__(self, model_dir):
        """ """
        xlnet_config = XLNetConfig(bert_config_path)
        xlnet_config.print_config()

        if use_cuda:
            place = fluid.CUDAPlace(0)
            dev_count = fluid.core.get_cuda_device_count()
        else:
            place = fluid.CPUPlace()
            dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
        self.exe = fluid.Executor(place)

        self.processor = DataProcessor(
            spiece_model_file=spiece_model_file,
            uncased=do_lower_case,
            max_seq_length=max_seq_len,
            doc_stride=doc_stride,
            max_query_length=max_query_length)

        self.inference_program, self.feed_target_names, self.fetch_targets = \
            fluid.io.load_inference_model(dirname=model_dir, executor=self.exe)

        # self.inference_program = fluid.compiler.CompiledProgram(self.inference_program)
        # self.exe = fluid.ParallelExecutor(
        #     use_cuda=use_cuda,
        #     main_program=self.inference_program)

    def preprocessor(self, samples, batch_size):
        """Preprocess the input samples, including word seg, padding, token to ids"""
        # Tokenization and paragraph padding
        examples, features, batch = self.processor.data_generator(
            samples, batch_size)
        self.samples = samples
        return examples, features, batch

    def call_mrc(self, batch, squeeze_dim0=False, return_list=False):
        """MRC"""
        if squeeze_dim0 and return_list:
            raise ValueError("squeeze_dim0 only work for dict-type return value.")
        src_ids = batch[0]
        pos_ids = batch[1]
        sent_ids = batch[2]
        input_mask = batch[3]
        unique_id = batch[4]
        emmmm = batch[5]
        feed_dict = {
            self.feed_target_names[0]: src_ids,
            self.feed_target_names[1]: pos_ids,
            self.feed_target_names[2]: sent_ids,
            self.feed_target_names[3]: input_mask,
            self.feed_target_names[4]: unique_id,
            self.feed_target_names[5]: emmmm
        }
        
        np_unique_ids, np_start_logits, np_start_top_index, np_end_logits, np_end_top_index, np_cls_logits = \
            self.exe.run(self.inference_program, feed=feed_dict, fetch_list=self.fetch_targets, use_program_cache=True)

        # np_unique_ids, np_start_logits, np_end_logits, np_num_seqs = \
        #     self.exe.run(feed=feed_dict, fetch_list=self.fetch_targets)

        if len(np_unique_ids) == 1 and squeeze_dim0:
            np_unique_ids = np_unique_ids[0]
            np_start_logits = np_start_logits[0]
            np_end_logits = np_end_logits[0]

        if return_list:
            mrc_results = [{'unique_ids': id, 'start_logits': st, 'start_idx': st_idx, 'end_logits': end, 'end_idx': end_idx, 'cls': cls} 
                            for id, st, st_idx, end, end_idx, cls in zip(np_unique_ids, np_start_logits, np_start_top_index, np_end_logits, np_end_top_index, np_cls_logits)]
        else:
            raise NotImplementedError()
        return mrc_results

    def postprocessor(self, examples, features, mrc_results):
        """Extract answer
         batch: [examples, features] from preprocessor
         mrc_results: model results from call_mrc. if mrc_results is list, each element of which is a size=1 batch.
        """
        RawResult = collections.namedtuple("RawResult",
                                            ["unique_id", "start_top_log_probs", "start_top_index",
                                            "end_top_log_probs", "end_top_index", "cls_logits"])
        results = []
        if isinstance(mrc_results, list):
            for res in mrc_results:
                unique_id = res['unique_ids'][0]
                start_logits = [float(x) for x in res['start_logits'].flat]
                start_idx = [int(x) for x in res['start_idx'].flat]
                end_logits = [float(x) for x in res['end_logits'].flat]
                end_idx = [int(x) for x in res['end_idx'].flat]
                cls_logits = float(res['cls'].flat[0])

                results.append(
                    RawResult(
                        unique_id=unique_id,
                        start_top_log_probs=start_logits,
                        start_top_index=start_idx,
                        end_top_log_probs=end_logits,
                        end_top_index=end_idx,
                        cls_logits=cls_logits))
        else:
            assert isinstance(mrc_results, dict)
            raise NotImplementedError()
            for idx in range(mrc_results['unique_ids'].shape[0]):
                unique_id = int(mrc_results['unique_ids'][idx])
                start_logits = [float(x) for x in mrc_results['start_logits'][idx].flat]
                end_logits = [float(x) for x in mrc_results['end_logits'][idx].flat]
                results.append(
                    RawResult(
                        unique_id=unique_id,
                        start_logits=start_logits,
                        end_logits=end_logits))
        
        answers = get_answers(
            examples, features, results, n_best_size,
            max_answer_length, start_n_top, end_n_top)
        return answers