wehub-resource-sync
256 lines
9.8 KiB
Python
256 lines
9.8 KiB
Python
# 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 argparse
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import json
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import os
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import sys
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from functools import partial
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from pathlib import Path
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import paddle
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from tqdm import tqdm
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from paddlenlp.data import Dict, Pad, Stack, Tuple, Vocab
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from paddlenlp.datasets import DatasetBuilder
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from paddlenlp.transformers.roberta.tokenizer import (
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RobertaBPETokenizer,
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RobertaTokenizer,
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)
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sys.path.append("../task/senti")
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from rnn.model import BiLSTMAttentionModel, SelfInteractiveAttention # noqa: E402
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from rnn.utils import CharTokenizer, convert_example # noqa: E402
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sys.path.append("..")
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from roberta.modeling import RobertaForSequenceClassification # noqa: E402
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sys.path.remove("..")
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sys.path.remove("../task/senti")
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sys.path.append("../..")
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from model_interpretation.utils import ( # noqa: E402
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convert_tokenizer_res_to_old_version,
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)
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sys.path.remove("../..")
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def get_args():
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parser = argparse.ArgumentParser("sentiment analysis prediction")
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parser.add_argument("--base_model", required=True, choices=["roberta_base", "roberta_large", "lstm"])
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parser.add_argument("--from_pretrained", type=str, required=True, help="pretrained model directory or tag")
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parser.add_argument(
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"--max_seq_len", type=int, default=128, help="max sentence length, should not greater than 512"
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)
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parser.add_argument("--batch_size", type=int, default=1, help="batchsize")
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parser.add_argument("--data_dir", type=str, required=True, help="data directory includes train / develop data")
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parser.add_argument("--eval", action="store_true")
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parser.add_argument("--init_checkpoint", type=str, default=None, help="checkpoint to warm start from")
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parser.add_argument("--wd", type=float, default=0.01, help="weight decay, aka L2 regularizer")
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parser.add_argument(
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"--use_amp",
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action="store_true",
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help="only activate AMP(auto mixed precision accelatoin) on TensorCore compatible devices",
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)
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parser.add_argument(
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"--inter_mode",
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type=str,
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default="attention",
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choices=["attention", "simple_gradient", "smooth_gradient", "integrated_gradient", "lime"],
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help="appoint the mode of interpretable.",
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)
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parser.add_argument("--n-samples", type=int, default=25, help="number of samples used for smooth gradient method")
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parser.add_argument("--output_dir", type=Path, required=True, help="interpretable output directory")
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parser.add_argument("--start_id", type=int, default=0)
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parser.add_argument("--vocab_path", type=str)
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parser.add_argument("--language", type=str, required=True, help="Language that the model is built for")
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args = parser.parse_args()
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return args
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class SentiData(DatasetBuilder):
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def _read(self, filename, language):
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with open(filename, "r", encoding="utf8") as f:
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for line in f.readlines():
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line_split = json.loads(line)
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yield {"id": line_split["id"], "context": line_split["context"]}
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def create_dataloader(dataset, trans_fn=None, mode="train", batch_size=1, batchify_fn=None):
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"""
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Creates dataloader.
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Args:
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dataset(obj:`paddle.io.Dataset`): Dataset instance.
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trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
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mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
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batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
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batchify_fn(obj:`callable`, optional, defaults to `None`): function to generate mini-batch data by merging
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the sample list, None for only stack each fields of sample in axis
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0(same as :attr::`np.stack(..., axis=0)`).
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Returns:
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dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
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"""
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if trans_fn:
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dataset = dataset.map(trans_fn)
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shuffle = True if mode == "train" else False
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if mode == "train":
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sampler = paddle.io.DistributedBatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
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else:
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sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
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dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, collate_fn=batchify_fn)
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return dataloader
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def map_fn_senti(examples, tokenizer, language):
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print("load data %d" % len(examples))
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contexts = [example["context"] for example in examples]
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tokenized_examples = tokenizer(contexts, max_seq_len=args.max_seq_len)
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tokenized_examples = convert_tokenizer_res_to_old_version(tokenized_examples)
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return tokenized_examples
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def truncate_offset(seg, start_offset, end_offset):
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seg_len = len(seg)
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for n in range(len(start_offset) - 1, -1, -1):
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if start_offset[n] < seg_len:
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end_offset[n] = seg_len
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break
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start_offset.pop(n)
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end_offset.pop(n)
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def init_lstm_var(args):
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vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
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tokenizer = CharTokenizer(vocab, args.language, "../punctuations")
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padding_idx = vocab.token_to_idx.get("[PAD]", 0)
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trans_fn = partial(convert_example, tokenizer=tokenizer, is_test=True, language=args.language)
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# init attention layer
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lstm_hidden_size = 196
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attention = SelfInteractiveAttention(hidden_size=2 * lstm_hidden_size)
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model = BiLSTMAttentionModel(
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attention_layer=attention,
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vocab_size=len(tokenizer.vocab),
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lstm_hidden_size=lstm_hidden_size,
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num_classes=2,
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padding_idx=padding_idx,
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)
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# Reads data and generates mini-batches.
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dev_ds = SentiData().read(os.path.join(args.data_dir, "dev"), args.language)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=padding_idx), # input_ids
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Stack(dtype="int64"), # seq len
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): [data for data in fn(samples)]
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dev_loader = create_dataloader(
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dev_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="validation", batchify_fn=batchify_fn
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)
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return model, tokenizer, dev_loader
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def init_roberta_var(args):
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tokenizer = None
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if args.language == "ch":
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tokenizer = RobertaTokenizer.from_pretrained(args.from_pretrained)
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else:
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tokenizer = RobertaBPETokenizer.from_pretrained(args.from_pretrained)
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model = RobertaForSequenceClassification.from_pretrained(
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args.from_pretrained,
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hidden_dropout_prob=0,
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attention_probs_dropout_prob=0,
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dropout=0,
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num_labels=2,
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name="",
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return_inter_score=True,
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)
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map_fn = partial(map_fn_senti, tokenizer=tokenizer, language=args.language)
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dev_ds = SentiData().read(os.path.join(args.data_dir, "dev"), args.language)
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dev_ds.map(map_fn, batched=True)
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dev_batch_sampler = paddle.io.BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)
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batchify_fn = lambda samples, fn=Dict(
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{
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"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),
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"token_type_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),
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}
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): fn(samples)
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dataloader = paddle.io.DataLoader(
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dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, return_list=True
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)
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return model, tokenizer, dataloader
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if __name__ == "__main__":
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args = get_args()
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if args.base_model.startswith("roberta"):
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model, tokenizer, dataloader = init_roberta_var(args)
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elif args.base_model == "lstm":
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model, tokenizer, dataloader = init_lstm_var(args)
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else:
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raise ValueError("unsupported base model name.")
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with paddle.amp.auto_cast(enable=args.use_amp), open(str(args.output_dir) + "/dev", "w") as out_handle:
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# Load model
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sd = paddle.load(args.init_checkpoint)
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model.set_dict(sd)
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model.train() # 为了取梯度，加载模型时dropout设为0
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print("load model from %s" % args.init_checkpoint)
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get_sub_word_ids = lambda word: map(str, tokenizer.convert_tokens_to_ids(tokenizer.tokenize(word)))
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for step, d in tqdm(enumerate(dataloader)):
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if step + 1 < args.start_id:
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continue
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result = {}
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if args.base_model.startswith("roberta"):
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input_ids, token_type_ids = d
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fwd_args = [input_ids, token_type_ids]
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fwd_kwargs = {}
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tokens = tokenizer.convert_ids_to_tokens(input_ids[0, 1:-1].tolist()) # list
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elif args.base_model == "lstm":
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input_ids, seq_lens = d
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fwd_args = [input_ids, seq_lens]
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fwd_kwargs = {}
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tokens = [tokenizer.vocab.idx_to_token[input_id] for input_id in input_ids.tolist()[0]]
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result["id"] = dataloader.dataset.data[step]["id"]
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probs, atts, embedded = model.forward_interpet(*fwd_args, **fwd_kwargs)
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pred_label = paddle.argmax(probs, axis=-1).tolist()[0]
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result["pred_label"] = pred_label
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result["probs"] = [float(format(prob, ".5f")) for prob in probs.numpy()[0].tolist()]
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if args.language == "en":
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result["context"] = tokenizer.convert_tokens_to_string(tokens)
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else:
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result["context"] = "".join(tokens)
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out_handle.write(json.dumps(result, ensure_ascii=False) + "\n")
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