# Copyright (c) 2021 PaddlePaddle Authors. 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. from __future__ import print_function import io import os import unittest import numpy as np import paddle import paddle.nn as nn from paddle.dataset.common import DATA_HOME from paddle.fluid.framework import core, in_dygraph_mode from paddle.fluid.layer_helper import LayerHelper import sys sys.path.append("./tokenizer") from tokenizer.bert_tokenizer import BertTokenizer def to_string_tensor(string_values, name): """ Create the tensor that the value holds the list of string. NOTICE: The value will be holded in the cpu place. Args: string_values(list[string]): The value will be setted to the tensor. name(string): The name of the tensor. """ tensor = paddle.Tensor(core.VarDesc.VarType.STRING, [], name, core.VarDesc.VarType.STRINGS, False) tensor.value().set_string_list(string_values) return tensor def to_map_tensor(string_dict, name): """ Create the tensor that the value holds the map, the type of key is the string and the value is the int. NOTICE: The value will be holded in the cpu place. Args: string_dict(dict): The value will be setted to the tensor. name(string): The name of the tensor. """ tensor = paddle.Tensor(core.VarDesc.VarType.RAW, [], name, core.VarDesc.VarType.VOCAB, True) tensor.value().set_vocab(string_dict) return tensor class FasterTokenizer(nn.Layer): def __init__(self, vocab_dict): super(FasterTokenizer, self).__init__() vocab_tensor = to_map_tensor(vocab_dict, "vocab") self.register_buffer("vocab", vocab_tensor, persistable=True) def forward(self, text, text_pair=None, do_lower_case=True, max_seq_len=-1, is_split_into_words=False, pad_to_max_seq_len=False): if in_dygraph_mode(): input_ids, seg_ids = core.ops.faster_tokenizer( self.vocab, text, text_pair, "do_lower_case", do_lower_case, "max_seq_len", max_seq_len, "pad_to_max_seq_len", pad_to_max_seq_len, "is_split_into_words", is_split_into_words) return input_ids, seg_ids attrs = { "do_lower_case": do_lower_case, "max_seq_len": max_seq_len, "pad_to_max_seq_len": pad_to_max_seq_len, "is_split_into_words": is_split_into_words, } helper = LayerHelper("faster_tokenizer") input_ids = helper.create_variable_for_type_inference(dtype="int64") seg_ids = helper.create_variable_for_type_inference(dtype="int64") if text_pair is None: helper.append_op( type='faster_tokenizer', inputs={'Vocab': self.vocab, 'Text': text}, outputs={'InputIds': input_ids, 'SegmentIds': seg_ids}, attrs=attrs) else: helper.append_op( type='faster_tokenizer', inputs={ 'Vocab': self.vocab, 'Text': text, 'TextPair': text_pair }, outputs={'InputIds': input_ids, 'SegmentIds': seg_ids}, attrs=attrs) return input_ids, seg_ids class Predictor(object): def __init__(self, model_dir): model_file = os.path.join(model_dir, "inference.pdmodel") params_file = os.path.join(model_dir, "inference.pdiparams") if not os.path.exists(model_file): raise ValueError("not find model file path {}".format(model_file)) if not os.path.exists(params_file): raise ValueError("not find params file path {}".format(params_file)) config = paddle.inference.Config(model_file, params_file) # fast_tokenizer op only support cpu. config.disable_gpu() config.set_cpu_math_library_num_threads(10) config.switch_use_feed_fetch_ops(False) self.predictor = paddle.inference.create_predictor(config) self.input_handles = [ self.predictor.get_input_handle(name) for name in self.predictor.get_input_names() ] self.output_handles = [ self.predictor.get_output_handle(name) for name in self.predictor.get_output_names() ] def predict(self, data): self.input_handles[0].copy_from_cpu(data) self.predictor.run() input_ids = self.output_handles[0].copy_to_cpu() token_type_ids = self.output_handles[1].copy_to_cpu() return input_ids, token_type_ids class TestBertTokenizerOp(unittest.TestCase): def setUp(self): self.bert_tokenizer = BertTokenizer.from_pretrained("bert-base-chinese") self.faster_tokenizer = FasterTokenizer(self.bert_tokenizer.vocab) self.init_data() self.save_path = os.path.join(DATA_HOME, "fast_tokenizer") self.param_path = os.path.join(self.save_path, "model.pdparams") self.inference_path = os.path.join(self.save_path, "inference") def init_data(self): self.text = [ '选择珠江花园的原因就是方便，有电动扶梯直接到达海边，周围餐馆、食廊、商场、超市、摊位一应俱全。' '酒店装修一般，但还算整洁。 泳池在大堂的屋顶，因此很小，不过女儿倒是喜欢。 包的早餐是西式的，' '还算丰富。 服务吗，一般' ] self.text_pair = ['非常不错，服务很好，位于市中心区，交通方便，不过价格也高！'] self.text_tensor = to_string_tensor(self.text, "text") self.text_pair_tensor = to_string_tensor(self.text_pair, "text_pair") self.texts = [ '很好的地理位置，一蹋糊涂的服务，萧条的酒店。', ' 选择珠江花园的原因就是方便，有电动扶梯直接到达海边，周围餐馆、食廊、商场、超市、摊位一应俱全。酒店装修一般，' '但还算整洁。 泳池在大堂的屋顶，因此很小，不过女儿倒是喜欢。 包的早餐是西式的，还算丰富。 服务吗，一般', 'Test bert tokenizer. The first text.' ] self.text_pairs = [ '非常不错，服务很好，位于市中心区，交通方便，不过价格也高！', '房间太小。其他的都一般。。。。。。。。。', 'Test bert tokenizer. The second text.' ] self.texts_tensor = to_string_tensor(self.texts, "texts") self.text_pairs_tensor = to_string_tensor(self.text_pairs, "text_pairs") def test_padding(self): self.max_seq_len = 128 self.pad_to_max_seq_len = True self.is_split_into_words = False # case 1: only one text (batch_size = 1) input_ids, token_type_ids = self.faster_tokenizer( text=self.text_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( text=self.text, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) # case 2: only one text and one text_pair (batch_size = 1) input_ids, token_type_ids = self.faster_tokenizer( text=self.text_tensor, text_pair=self.text_pair_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( text=self.text, text_pair=self.text_pair, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) # case 3: only texts (batch_size = 3) input_ids, token_type_ids = self.faster_tokenizer( text=self.texts_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( self.texts, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = [i["input_ids"] for i in encoded_inputs] py_token_type_ids = [i["token_type_ids"] for i in encoded_inputs] py_input_ids = np.array(py_input_ids).reshape([3, -1]) py_token_type_ids = np.array(py_token_type_ids).reshape([3, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) # case 4: texts and text pairs (batch_size = 3) input_ids, token_type_ids = self.faster_tokenizer( text=self.texts_tensor, text_pair=self.text_pairs_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( self.texts, self.text_pairs, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = [i["input_ids"] for i in encoded_inputs] py_token_type_ids = [i["token_type_ids"] for i in encoded_inputs] py_input_ids = np.array(py_input_ids).reshape([3, -1]) py_token_type_ids = np.array(py_token_type_ids).reshape([3, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) def test_no_padding(self): self.max_seq_len = 128 self.pad_to_max_seq_len = False self.is_split_into_words = False # case 1: only one text (batch_size = 1) input_ids, token_type_ids = self.faster_tokenizer( text=self.text_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( self.text, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) # case 2: only one text and one text_pair (batch_size = 1) input_ids, token_type_ids = self.faster_tokenizer( self.text_tensor, self.text_pair_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( self.text, self.text_pair, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) def test_is_split_into_words(self): self.is_split_into_words = True input_ids, token_type_ids = self.faster_tokenizer( self.text_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( list(self.text[0]), is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs["token_type_ids"]).reshape( [1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) def test_inference(self): if not os.path.exists(self.save_path): os.makedirs(self.save_path, exist_ok=True) paddle.save(self.faster_tokenizer.state_dict(), self.param_path) state_dict = paddle.load(self.param_path) self.faster_tokenizer.set_dict(state_dict) static_model = paddle.jit.to_static( self.faster_tokenizer, input_spec=[ paddle.static.InputSpec( shape=[None], dtype=core.VarDesc.VarType.STRINGS), # texts ]) # Save in static graph model. paddle.jit.save(static_model, self.inference_path) predictor = Predictor(self.save_path) input_ids, token_type_ids = predictor.predict(self.text) encoded_inputs = self.bert_tokenizer(self.text) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) def test_feed_string_var(self): paddle.enable_static() x = paddle.static.data( name="x", shape=[-1], dtype=core.VarDesc.VarType.STRINGS) exe = paddle.static.Executor(paddle.framework.CPUPlace()) exe.run(paddle.static.default_main_program(), feed={'x': self.text}) paddle.disable_static() def test_data_parallel(self): self.max_seq_len = 128 self.pad_to_max_seq_len = True self.is_split_into_words = False model = paddle.DataParallel(self.faster_tokenizer) input_ids, token_type_ids = model( text=self.text_tensor, do_lower_case=self.bert_tokenizer.do_lower_case, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) input_ids = input_ids.numpy() token_type_ids = token_type_ids.numpy() encoded_inputs = self.bert_tokenizer( self.text, max_seq_len=self.max_seq_len, pad_to_max_seq_len=self.pad_to_max_seq_len, is_split_into_words=self.is_split_into_words) py_input_ids = np.array(encoded_inputs[0]["input_ids"]).reshape([1, -1]) py_token_type_ids = np.array(encoded_inputs[0][ "token_type_ids"]).reshape([1, -1]) self.assertTrue(np.allclose(input_ids, py_input_ids, rtol=0, atol=0.01)) self.assertTrue( np.allclose( token_type_ids, py_token_type_ids, rtol=0, atol=0.01)) if __name__ == '__main__': unittest.main()