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Fix llama tokenizer (#22402)
* draft * update tokenization limma and conversion script * more udpates * initial commit * style * default pad to None * draft tokenization tests * update test * update tokenization tests * nits * update * versioning test * major fix * fix more testst * finish fixing special masks * last nit * more nits * add encode decode tests * add more * fix token type ids * style
This commit is contained in:
Arthur
GitHub
parent
9eae4aa576
commit
c0f99b4d2e
@ -20,7 +20,7 @@ import shutil
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import torch
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from transformers import LlamaConfig, LlamaForCausalLM
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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"""
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@ -233,19 +233,9 @@ def write_model(model_path, input_base_path, model_size):
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def write_tokenizer(tokenizer_path, input_tokenizer_path):
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print(f"Fetching the tokenizer from {input_tokenizer_path}.")
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os.makedirs(tokenizer_path, exist_ok=True)
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write_json({}, os.path.join(tokenizer_path, "special_tokens_map.json"))
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write_json(
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{
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"bos_token": "",
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"eos_token": "",
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"model_max_length": int(1e30),
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "",
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},
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os.path.join(tokenizer_path, "tokenizer_config.json"),
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)
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shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model"))
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# Initialize the tokenizer based on the `spm` model
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tokenizer = LlamaTokenizer(input_tokenizer_path)
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tokenizer.save_pretrained(tokenizer_path)
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def main():
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@ -25,7 +25,7 @@ from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import PreTrainedTokenizer
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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@ -33,7 +33,17 @@ logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
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PRETRAINED_VOCAB_FILES_MAP = {}
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {
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"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
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},
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"tokenizer_file": {
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"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
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},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"hf-internal-testing/llama-tokenizer": 2048,
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}
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class LlamaTokenizer(PreTrainedTokenizer):
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@ -47,6 +57,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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@ -55,51 +66,50 @@ class LlamaTokenizer(PreTrainedTokenizer):
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unk_token="<unk>",
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bos_token="<s>",
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eos_token="</s>",
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pad_token=None,
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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add_bos_token=True,
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add_eos_token=False,
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decode_with_prefix_space=False,
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clean_up_tokenization_spaces=False,
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**kwargs,
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):
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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add_bos_token=add_bos_token,
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add_eos_token=add_eos_token,
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sp_model_kwargs=self.sp_model_kwargs,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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self.vocab_file = vocab_file
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self.add_bos_token = add_bos_token
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self.add_eos_token = add_eos_token
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self.decode_with_prefix_space = decode_with_prefix_space
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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self._no_prefix_space_tokens = None
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""" Initialisation"""
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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@property
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def no_prefix_space_tokens(self):
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if self._no_prefix_space_tokens is None:
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vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
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self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
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return self._no_prefix_space_tokens
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def __setstate__(self, d):
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self.__dict__ = d
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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@property
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def vocab_size(self):
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"""Returns vocab size"""
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return self.sp_model.get_piece_size()
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@property
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def bos_token_id(self) -> Optional[int]:
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return self.sp_model.bos_id()
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@property
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def eos_token_id(self) -> Optional[int]:
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return self.sp_model.eos_id()
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def get_vocab(self):
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"""Returns vocab as a dict"""
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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@ -119,21 +129,15 @@ class LlamaTokenizer(PreTrainedTokenizer):
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token = self.sp_model.IdToPiece(index)
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return token
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def _maybe_add_prefix_space(self, tokens, decoded):
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if tokens and tokens[0] not in self.no_prefix_space_tokens:
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return " " + decoded
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else:
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return decoded
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for token in tokens:
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for i, token in enumerate(tokens):
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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if not prev_is_special and i != 0:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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@ -142,7 +146,6 @@ class LlamaTokenizer(PreTrainedTokenizer):
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
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return out_string
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
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@ -173,18 +176,13 @@ class LlamaTokenizer(PreTrainedTokenizer):
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return (out_vocab_file,)
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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if self.add_bos_token:
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bos_token_ids = [self.bos_token_id]
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else:
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bos_token_ids = []
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bos_token_id = [self.bos_token_id] if self.add_bos_token else []
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eos_token_id = [self.eos_token_id] if self.add_eos_token else []
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output = bos_token_ids + token_ids_0
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output = bos_token_id + token_ids_0 + eos_token_id
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if token_ids_1 is not None:
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output = output + token_ids_1
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if self.add_eos_token:
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output = output + [self.eos_token_id]
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output = output + bos_token_id + token_ids_1 + eos_token_id
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return output
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@ -211,28 +209,46 @@ class LlamaTokenizer(PreTrainedTokenizer):
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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bos_token_id = [1] if self.add_bos_token else []
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eos_token_id = [1] if self.add_eos_token else []
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
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return (
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bos_token_id
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+ ([0] * len(token_ids_0))
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+ eos_token_id
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+ bos_token_id
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+ ([0] * len(token_ids_1))
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+ eos_token_id
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)
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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use of token type ids, therefore a list of zeros is returned.
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Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
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sequence pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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if token_ids_1 is None, only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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List of ids.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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eos = [self.eos_token_id]
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
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412
tests/models/llama/test_tokenization_llama.py
Normal file
412
tests/models/llama/test_tokenization_llama.py
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@ -0,0 +1,412 @@
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# Copyright 2023 The HuggingFace Team. 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 shutil
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import tempfile
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import unittest
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from datasets import load_dataset
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from transformers import (
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SPIECE_UNDERLINE,
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AddedToken,
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LlamaTokenizer,
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is_torch_available,
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)
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from transformers.testing_utils import (
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get_tests_dir,
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nested_simplify,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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)
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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pass
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@require_sentencepiece
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@require_tokenizers
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class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = LlamaTokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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from_pretrained_kwargs = {}
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[285, 46, 10, 170, 382],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids,
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[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
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)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@unittest.skip("Let's wait for the fast tokenizer!")
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def test_save_pretrained(self):
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self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {})
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files + the tokenizer.json file for the fast one
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=True
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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|
||||
# Save tokenizer rust, legacy_format=False
|
||||
tmpdirname2 = tempfile.mkdtemp()
|
||||
|
||||
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
|
||||
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
||||
|
||||
# Checks it saved the tokenizer.json file
|
||||
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
|
||||
|
||||
# Checks everything loads correctly in the same way
|
||||
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
||||
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
||||
|
||||
# Check special tokens are set accordingly on Rust and Python
|
||||
for key in tokenizer_pp.special_tokens_map:
|
||||
self.assertTrue(hasattr(tokenizer_rp, key))
|
||||
|
||||
shutil.rmtree(tmpdirname2)
|
||||
|
||||
@require_torch
|
||||
def test_batch_tokenization(self):
|
||||
if not self.test_seq2seq:
|
||||
return
|
||||
|
||||
tokenizers = self.get_tokenizers()
|
||||
for tokenizer in tokenizers:
|
||||
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
||||
# Longer text that will definitely require truncation.
|
||||
text = [
|
||||
" UN Chief Says There Is No Military Solution in Syria",
|
||||
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
|
||||
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
|
||||
" will only worsen the violence and misery for millions of people.",
|
||||
]
|
||||
try:
|
||||
batch = tokenizer(
|
||||
text=text,
|
||||
max_length=3,
|
||||
max_target_length=10,
|
||||
return_tensors="pt",
|
||||
)
|
||||
except NotImplementedError:
|
||||
return
|
||||
self.assertEqual(batch.input_ids.shape[1], 3)
|
||||
# max_target_length will default to max_length if not specified
|
||||
batch = tokenizer(text, max_length=3, return_tensors="pt")
|
||||
self.assertEqual(batch.input_ids.shape[1], 3)
|
||||
|
||||
batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
|
||||
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
|
||||
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
|
||||
self.assertNotIn("decoder_input_ids", batch_encoder_only)
|
||||
|
||||
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
|
||||
def test_save_slow_from_fast_and_reload_fast(self):
|
||||
pass
|
||||
|
||||
def test_special_tokens_initialization(self):
|
||||
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
||||
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
||||
added_tokens = [AddedToken("<special>", lstrip=True)]
|
||||
|
||||
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
|
||||
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
||||
)
|
||||
r_output = tokenizer_r.encode("Hey this is a <special> token")
|
||||
|
||||
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
|
||||
|
||||
self.assertTrue(special_token_id in r_output)
|
||||
|
||||
if self.test_slow_tokenizer:
|
||||
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
|
||||
pretrained_name,
|
||||
additional_special_tokens=added_tokens,
|
||||
**kwargs, # , from_slow=True <- unfortunately too slow to convert
|
||||
)
|
||||
tokenizer_p = self.tokenizer_class.from_pretrained(
|
||||
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
||||
)
|
||||
|
||||
p_output = tokenizer_p.encode("Hey this is a <special> token")
|
||||
|
||||
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
|
||||
|
||||
self.assertEqual(p_output, r_output)
|
||||
self.assertEqual(cr_output, r_output)
|
||||
self.assertTrue(special_token_id in p_output)
|
||||
self.assertTrue(special_token_id in cr_output)
|
||||
|
||||
@slow
|
||||
def test_tokenizer_integration(self):
|
||||
# fmt: off
|
||||
expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
|
||||
# fmt: on
|
||||
|
||||
self.tokenizer_integration_test_util(
|
||||
expected_encoding=expected_encoding,
|
||||
model_name="hf-internal-testing/llama-tokenizer",
|
||||
revision="0984d03108b1a041ed679bd253b6519b7e1a4778",
|
||||
padding=False,
|
||||
)
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
class LlamaIntegrationTest(unittest.TestCase):
|
||||
checkpoint_name = "hf-internal-testing/llama-tokenizer"
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(cls.checkpoint_name)
|
||||
cls.rust_tokenizer = cls.tokenizer # TODO @narsil replace with the rust one
|
||||
cls.pad_token_id = 1
|
||||
return cls
|
||||
|
||||
@require_torch
|
||||
def integration_tests(self):
|
||||
inputs = self.tokenizer(
|
||||
["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
nested_simplify(inputs),
|
||||
{
|
||||
"input_ids": [
|
||||
[1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
|
||||
[1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
|
||||
],
|
||||
"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
|
||||
},
|
||||
)
|
||||
|
||||
def test_simple_encode_decode(self):
|
||||
pyth_tokenizer = self.tokenizer
|
||||
rust_tokenizer = self.rust_tokenizer
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||||
self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||||
self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||||
self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||||
|
||||
# bytefallback showcase
|
||||
self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
|
||||
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
|
||||
self.assertEqual(
|
||||
pyth_tokenizer.decode(
|
||||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||||
),
|
||||
"生活的真谛是",
|
||||
)
|
||||
self.assertEqual(
|
||||
rust_tokenizer.decode(
|
||||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||||
),
|
||||
"生活的真谛是",
|
||||
)
|
||||
|
||||
# Inner spaces showcase
|
||||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||||
self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||||
self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
|
||||
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
|
||||
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||||
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
|
||||
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||||
|
||||
self.assertEqual(pyth_tokenizer.decode([869]), ".")
|
||||
self.assertEqual(rust_tokenizer.decode([869]), ".")
|
||||
|
||||
self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
|
||||
self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
|
||||
|
||||
@unittest.skipIf(
|
||||
os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
|
||||
"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
|
||||
)
|
||||
def test_integration_test_xnli(self):
|
||||
import tqdm
|
||||
|
||||
pyth_tokenizer = self.tokenizer
|
||||
rust_tokenizer = self.rust_tokenizer
|
||||
|
||||
dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
|
||||
for item in tqdm.tqdm(dataset["validation"]):
|
||||
string = item["code"]
|
||||
encoded1 = pyth_tokenizer.encode(string)
|
||||
encoded2 = rust_tokenizer.encode(string)
|
||||
|
||||
self.assertEqual(encoded1, encoded2)
|
||||
|
||||
decoded1 = pyth_tokenizer.decode(encoded1)
|
||||
decoded2 = rust_tokenizer.decode(encoded2)
|
||||
|
||||
self.assertEqual(decoded1, decoded2)
|
||||
|
||||
dataset = load_dataset("xnli", "all_languages")
|
||||
|
||||
for item in tqdm.tqdm(dataset["train"]):
|
||||
for string in item["premise"].values():
|
||||
encoded1 = pyth_tokenizer.encode(string)
|
||||
encoded2 = rust_tokenizer.encode(string)
|
||||
|
||||
self.assertEqual(encoded1, encoded2)
|
||||
|
||||
decoded1 = pyth_tokenizer.decode(encoded1)
|
||||
decoded2 = rust_tokenizer.decode(encoded2)
|
||||
|
||||
self.assertEqual(decoded1, decoded2)
|
||||
@ -3932,7 +3932,7 @@ class TokenizerTesterMixin:
|
||||
tokenizer_fast.save_pretrained(tmp_dir_2)
|
||||
tokenizer = BertTokenizer.from_pretrained(tmp_dir_2)
|
||||
|
||||
assert tokenizer_fast.clean_up_tokenization_spaces is False
|
||||
assert tokenizer.clean_up_tokenization_spaces is False
|
||||
decoded = tokenizer.decode(tokens)
|
||||
assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user