fix package requirement of regex and sentencepiece (#5261)

PaddleNLP/examples/language_model/gpt2/data.py

@@ -208,7 +208,7 @@ class GPT2Dataset(paddle.io.Dataset):
         # -INF mask value as default
         attention_mask = (attention_mask - 1.0) * 1e9
         # Bool mask of attention
-        # attention_mask = attention_mask.astype("float32")
+        attention_mask = attention_mask.astype("float32")
         return [tokens, loss_mask, attention_mask, position_ids, labels]
 
     def _get_single_sample_from_idx(self, doc_index_f, doc_index_l, offset_f,

PaddleNLP/paddlenlp/transformers/gpt2/modeling.py

@@ -13,16 +13,17 @@
 # limitations under the License.
 
 import collections
+import math
+
 import numpy as np
 import paddle
 import paddle.nn as nn
-import paddle.tensor as tensor
 import paddle.nn.functional as F
-import math
+import paddle.tensor as tensor
 from paddle.fluid import layers
+from paddle.nn.layer.transformer import _convert_param_attr_to_list
 
 from .. import PretrainedModel, register_base_model
-from paddle.nn.layer.transformer import _convert_param_attr_to_list
 
 __all__ = [
     'GPT2Model',

PaddleNLP/paddlenlp/transformers/gpt2/tokenizer.py

@@ -13,14 +13,13 @@
 # limitations under the License.
 
 import os
-import regex as re
-import unicodedata
+from functools import lru_cache
+from collections import namedtuple
+
 import json
-import sentencepiece
 import jieba
+from paddle.utils import try_import
 
-from functools import lru_cache
-from collections import namedtuple
 from .. import PretrainedTokenizer
 from ..tokenizer_utils import convert_to_unicode, whitespace_tokenize,\
     _is_whitespace, _is_control, _is_punctuation
@@ -122,7 +121,8 @@ class GPT2ChineseTokenizer(PretrainedTokenizer):
         self.max_len = max_len if max_len is not None else int(1e12)
         self.encoder = json.load(open(vocab_file))
         self.decoder = {v: k for k, v in self.encoder.items()}
-        self.sp = sentencepiece.SentencePieceProcessor(model_file=model_file)
+        mod = try_import("sentencepiece")
+        self.sp = mod.SentencePieceProcessor(model_file=model_file)
         self.translator = str.maketrans(" \n", "\u2582\u2583")
 
     def tokenize(self, text):
@@ -220,7 +220,7 @@ class GPT2Tokenizer(PretrainedTokenizer):
         bpe_merges = [tuple(merge.split()) for merge in bpe_data]
         self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
         self.cache = {}
-
+        re = try_import("regex")
         self.pat = re.compile(
             r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
         )
@@ -295,6 +295,7 @@ class GPT2Tokenizer(PretrainedTokenizer):
     def tokenize(self, text):
         """ Tokenize a string. """
         bpe_tokens = []
+        re = try_import("regex")
         for token in re.findall(self.pat, text):
             token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
             bpe_tokens.extend(