vllm/tests/tokenization/test_detokenize.py
2024-03-15 23:37:01 +00:00

63 lines
2.1 KiB
Python

import pytest
from transformers import AutoTokenizer
from vllm.transformers_utils.tokenizer import detokenize_incrementally
TRUTH = [
"Hello here, this is a simple test", # noqa: E501
"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be used in production environments, where inference and serving", # noqa: E501
"我很感谢你的热情" # noqa: E501
]
TOKENIZERS = [
"facebook/opt-125m",
"gpt2",
"bigcode/tiny_starcoder_py",
"EleutherAI/gpt-j-6b",
"EleutherAI/pythia-70m",
"bigscience/bloom-560m",
"mosaicml/mpt-7b",
"tiiuae/falcon-7b",
"meta-llama/Llama-2-7b-hf",
"codellama/CodeLlama-7b-hf",
]
def _run_incremental_decode(tokenizer, all_input_ids,
skip_special_tokens: bool):
decoded_text = ""
offset = 0
token_offset = 0
prev_tokens = None
for i in range(len(all_input_ids)):
new_tokens, text, offset, token_offset = detokenize_incrementally(
tokenizer,
all_input_ids[:i + 1],
prev_tokens,
offset,
token_offset,
skip_special_tokens=skip_special_tokens)
decoded_text += text
if prev_tokens is None:
prev_tokens = new_tokens
else:
prev_tokens += new_tokens
return decoded_text
@pytest.mark.parametrize("truth", TRUTH)
@pytest.mark.parametrize("tokenizer_id", TOKENIZERS)
@pytest.mark.parametrize("skip_special_tokens", (True, False))
def test_decode_streaming(tokenizer_id, truth, skip_special_tokens):
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
all_input_ids = tokenizer(truth, add_special_tokens=False)["input_ids"]
if skip_special_tokens:
all_input_ids = ([tokenizer.bos_token_id]
if tokenizer.bos_token_id is not None else
[]) + all_input_ids + [tokenizer.eos_token_id]
decoded_text = _run_incremental_decode(
tokenizer, all_input_ids, skip_special_tokens=skip_special_tokens)
assert decoded_text == truth