| import random |
|
|
| import pytest |
|
|
| from lmdeploy.tokenizer import DetokenizeState, HuggingFaceTokenizer, Tokenizer |
|
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|
| @pytest.mark.parametrize('model_path', [ |
| 'internlm/internlm-chat-7b', 'Qwen/Qwen-7B-Chat', 'baichuan-inc/Baichuan2-7B-Chat', 'upstage/SOLAR-0-70b-16bit', |
| 'baichuan-inc/Baichuan-7B', 'codellama/CodeLlama-7b-hf', 'THUDM/chatglm2-6b', '01-ai/Yi-6B-200k', |
| '01-ai/Yi-34B-Chat', '01-ai/Yi-6B-Chat', 'WizardLM/WizardLM-70B-V1.0', 'codellama/CodeLlama-34b-Instruct-hf' |
| ]) |
| @pytest.mark.parametrize('input', [' hi, this is a test ππ! ηΊδ»ιΊΌζιε¨η¨ηΉι«ε ππ ' * 5]) |
| @pytest.mark.parametrize('interval', [1, 3]) |
| @pytest.mark.parametrize('add_special_tokens', [True, False]) |
| @pytest.mark.parametrize('skip_special_tokens', [True, False]) |
| def test_tokenizer(model_path, input, interval, add_special_tokens, skip_special_tokens): |
| tokenizer = Tokenizer(model_path).model |
| encoded = tokenizer.encode(input, False, add_special_tokens=add_special_tokens) |
| output = '' |
| input = tokenizer.decode(encoded, skip_special_tokens=skip_special_tokens) |
| state = DetokenizeState() |
| for i in range(0, len(encoded), interval): |
| offset = i + interval |
| if offset < len(encoded): |
| |
| if random.randint(1, 10) < 4: |
| offset -= interval |
| decoded, state = tokenizer.detokenize_incrementally(encoded[:offset], state, skip_special_tokens) |
| output += decoded |
| assert input == output, 'input string should equal to output after enc-dec' |
|
|
|
|
| @pytest.mark.parametrize('model_path', [ |
| 'internlm/internlm-chat-7b', 'Qwen/Qwen-7B-Chat', 'baichuan-inc/Baichuan2-7B-Chat', 'codellama/CodeLlama-7b-hf', |
| 'upstage/SOLAR-0-70b-16bit' |
| ]) |
| @pytest.mark.parametrize('stop_words', ['.', ' ', '?', '']) |
| def test_tokenizer_with_stop_words(model_path, stop_words): |
| tokenizer = HuggingFaceTokenizer(model_path) |
| indexes = tokenizer.indexes_containing_token(stop_words) |
| assert indexes is not None |
|
|
|
|
| def test_qwen_vl_decode_special(): |
| from lmdeploy.tokenizer import Tokenizer |
| tok = Tokenizer('Qwen/Qwen-VL-Chat') |
| try: |
| tok.decode([151857]) |
| assert (0) |
| except Exception as e: |
| assert str(e) == 'Unclosed image token' |
|
|
|
|
| def test_glm4_special_token(): |
| from lmdeploy.tokenizer import ChatGLM4Tokenizer, Tokenizer |
| model_path = 'THUDM/glm-4-9b-chat' |
| tokenizer = Tokenizer(model_path) |
| assert isinstance(tokenizer.model, ChatGLM4Tokenizer) |
| special_tokens = [ |
| '<|endoftext|>', '[MASK]', '[gMASK]', '[sMASK]', '<sop>', '<eop>', '<|system|>', '<|user|>', '<|assistant|>', |
| '<|observation|>', '<|begin_of_image|>', '<|end_of_image|>', '<|begin_of_video|>', '<|end_of_video|>' |
| ] |
| speicial_token_ids = [i for i in range(151329, 151343)] |
|
|
| for token, token_id in zip(special_tokens, speicial_token_ids): |
| _token_id = tokenizer.encode(token, add_bos=False) |
| assert len(_token_id) == 1 and _token_id[0] == token_id |
|
|
|
|
| @pytest.mark.parametrize('model_path', |
| ['Qwen/Qwen2-7B-Instruct', 'deepseek-ai/deepseek-vl-1.3b-chat', 'OpenGVLab/InternVL2-1B']) |
| def test_check_transformers_version(model_path): |
| tokenizer = HuggingFaceTokenizer(model_path) |
| assert tokenizer is not None |
|
|