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[Model] Add text classification task for ernie-3.0 (#430)
* move text_cls to ernie-3.0 * Add main page of ernie-3.0 * rename infer -> seq_cls_infer * Fix the links * Add ernie-3.0 python, cpp readme * Fix some cpp readme * Add fastdeploy::FDERROR * Add python readme for ernie-3.0 * update README.md * Add empty line * update readme * Fix readme * remove the - from ernie 3.0 * ernie-3.0 -> ernie 3.0 * Use AutoTokenizer to tokenize * Ernie -> ERNIE
This commit is contained in:
Jack Zhou
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# Copyright (c) 2022 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 distutils.util
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import numpy as np
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import faster_tokenizer
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from paddlenlp.transformers import AutoTokenizer
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import fastdeploy as fd
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_dir", required=True, help="The directory of model.")
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parser.add_argument(
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"--vocab_path",
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type=str,
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default="",
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help="The path of tokenizer vocab.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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choices=['gpu', 'cpu'],
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--backend",
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type=str,
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default='onnx_runtime',
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choices=[
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'onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt'
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],
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help="The inference runtime backend.")
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parser.add_argument(
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"--batch_size", type=int, default=1, help="The batch size of data.")
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parser.add_argument(
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"--max_length",
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type=int,
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default=128,
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help="The max length of sequence.")
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parser.add_argument(
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"--log_interval",
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type=int,
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default=10,
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help="The interval of logging.")
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parser.add_argument(
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"--use_fp16",
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type=distutils.util.strtobool,
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default=False,
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help="Wheter to use FP16 mode")
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parser.add_argument(
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"--use_fast",
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type=distutils.util.strtobool,
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default=False,
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help="Whether to use fast_tokenizer to accelarate the tokenization.")
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return parser.parse_args()
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def batchfy_text(texts, batch_size):
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batch_texts = []
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batch_start = 0
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while batch_start < len(texts):
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batch_texts += [
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texts[batch_start:min(batch_start + batch_size, len(texts))]
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]
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batch_start += batch_size
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return batch_texts
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class ErnieForSequenceClassificationPredictor(object):
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def __init__(self, args):
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self.tokenizer = AutoTokenizer.from_pretrained(
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'ernie-3.0-medium-zh', use_faster=args.use_fast)
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self.runtime = self.create_fd_runtime(args)
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self.batch_size = args.batch_size
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self.max_length = args.max_length
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def create_fd_runtime(self, args):
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option = fd.RuntimeOption()
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model_path = os.path.join(args.model_dir, "infer.pdmodel")
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params_path = os.path.join(args.model_dir, "infer.pdiparams")
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option.set_model_path(model_path, params_path)
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if args.device == 'cpu':
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option.use_cpu()
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else:
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option.use_gpu()
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if args.backend == 'paddle':
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option.use_paddle_backend()
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elif args.backend == 'onnx_runtime':
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option.use_ort_backend()
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elif args.backend == 'openvino':
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option.use_openvino_backend()
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else:
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option.use_trt_backend()
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if args.backend == 'paddle_tensorrt':
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option.enable_paddle_to_trt()
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option.enable_paddle_trt_collect_shape()
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trt_file = os.path.join(args.model_dir, "infer.trt")
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option.set_trt_input_shape(
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'input_ids',
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min_shape=[1, args.max_length],
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opt_shape=[args.batch_size, args.max_length],
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max_shape=[args.batch_size, args.max_length])
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option.set_trt_input_shape(
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'token_type_ids',
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min_shape=[1, args.max_length],
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opt_shape=[args.batch_size, args.max_length],
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max_shape=[args.batch_size, args.max_length])
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if args.use_fp16:
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option.enable_trt_fp16()
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trt_file = trt_file + ".fp16"
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option.set_trt_cache_file(trt_file)
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return fd.Runtime(option)
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def preprocess(self, texts, texts_pair):
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data = self.tokenizer(
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texts,
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texts_pair,
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max_length=self.max_length,
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padding=True,
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truncation=True)
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input_ids_name = self.runtime.get_input_info(0).name
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token_type_ids_name = self.runtime.get_input_info(1).name
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input_map = {
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input_ids_name: np.array(
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data["input_ids"], dtype="int64"),
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token_type_ids_name: np.array(
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data["token_type_ids"], dtype="int64")
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}
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return input_map
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def infer(self, input_map):
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results = self.runtime.infer(input_map)
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return results
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def postprocess(self, infer_data):
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logits = np.array(infer_data[0])
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max_value = np.max(logits, axis=1, keepdims=True)
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exp_data = np.exp(logits - max_value)
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probs = exp_data / np.sum(exp_data, axis=1, keepdims=True)
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out_dict = {
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"label": probs.argmax(axis=-1),
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"confidence": probs.max(axis=-1)
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}
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return out_dict
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def predict(self, texts, texts_pair=None):
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input_map = self.preprocess(texts, texts_pair)
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infer_result = self.infer(input_map)
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output = self.postprocess(infer_result)
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return output
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if __name__ == "__main__":
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args = parse_arguments()
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predictor = ErnieForSequenceClassificationPredictor(args)
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texts_ds = ["花呗收款额度限制", "花呗支持高铁票支付吗"]
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texts_pair_ds = ["收钱码，对花呗支付的金额有限制吗", "为什么友付宝不支持花呗付款"]
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batch_texts = batchfy_text(texts_ds, args.batch_size)
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batch_texts_pair = batchfy_text(texts_pair_ds, args.batch_size)
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for bs, (texts,
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texts_pair) in enumerate(zip(batch_texts, batch_texts_pair)):
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outputs = predictor.predict(texts, texts_pair)
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for i, (sentence1, sentence2) in enumerate(zip(texts, texts_pair)):
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print(
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f"Batch id:{bs}, example id:{i}, sentence1:{sentence1}, sentence2:{sentence2}, label:{outputs['label'][i]}, similarity:{outputs['confidence'][i]:.4f}"
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)
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