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firestar5683
109 lines
6.2 KiB
Python
109 lines
6.2 KiB
Python
import unittest, base64, functools, re, sys, time, unicodedata
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from tinygrad.llm.cli import SimpleTokenizer, FallbackTemplate
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from tinygrad.helpers import fetch
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@unittest.skipIf(sys.platform == 'win32', "fetch race condition on Windows")
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class TestLLMTokenizer(unittest.TestCase):
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@functools.cached_property
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def llama_tok(self):
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# from https://github.com/tinygrad/tinygrad/blob/e0106b6b257ebc003eb3694144e3e198f7d8cc37/examples/llama3.py#L14
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model_file = fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model")
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with open(model_file, "rt") as fd:
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str_vocab = [line.split(maxsplit=1) for line in fd.read().splitlines() if line]
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# https://github.com/openai/gpt-2/blob/9b63575ef42771a015060c964af2c3da4cf7c8ab/src/encoder.py#L9
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bs = [*range(33, 127), *range(161, 173), *range(174, 256)] # bytes that map to themselves
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_byte_decoder = {chr(b): b for b in bs} | {chr(256+i): b for i,b in enumerate(b for b in range(256) if b not in bs)}
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_byte_encoder = {v:k for k,v in _byte_decoder.items()}
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normal_tokens = {''.join([_byte_encoder[x] for x in base64.b64decode(stok)]): int(srank) for stok, srank in str_vocab}
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special_tokens = [
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"<|begin_of_text|>",
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"<|end_of_text|>",
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"<|reserved_special_token_0|>",
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"<|reserved_special_token_1|>",
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"<|reserved_special_token_2|>",
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"<|reserved_special_token_3|>",
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"<|start_header_id|>",
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"<|end_header_id|>",
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"<|reserved_special_token_4|>",
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"<|eot_id|>",
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] + [ f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5) ]
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return SimpleTokenizer(normal_tokens, {token: len(normal_tokens) + i for i, token in enumerate(special_tokens)})
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def _test_coding(self, tok: SimpleTokenizer, text: str, expected_tokens: list[int]):
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self.assertEqual(tok.encode(text), expected_tokens)
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self.assertEqual(tok.decode(expected_tokens), text)
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# NOTE: the correct tokenization for this can only be found by looking up the text chunk in the vocab, not by applying merges
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def test_llama_early_tokenize(self): self._test_coding(self.llama_tok, " например", [ 111797 ])
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def test_llama_basic(self): self._test_coding(self.llama_tok, "hello world", [ 15339, 1917 ])
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def test_llama_control_char(self): self._test_coding(self.llama_tok, " \x850", [ 220, 116360, 15 ])
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def test_llama_bytes(self): self._test_coding(self.llama_tok, " \xec\x8b\xa4\xed", [ 1717, 105, 116174, 82638, 2483 ])
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def test_llama_special1(self): self._test_coding(self.llama_tok, "hello <|end_of_text|>", [ 15339, 220, 128001 ])
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def test_llama_special2(self): self._test_coding(self.llama_tok, "<|start_header_id|>user<|end_header_id|>\n\n", [ 128006, 882, 128007, 271 ])
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def test_llama_repeat(self): self._test_coding(self.llama_tok, "00000000000000000", [ 931, 931, 931, 931, 931, 410 ])
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def test_llama_pat(self): self._test_coding(self.llama_tok, "today\n \n", [ 31213, 14211 ])
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def test_split_regex_matches_naive_listing(self):
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# the compacted codepoint ranges must match the same text as listing every codepoint
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def naive(pre): return "".join(re.escape(chr(cp)) for cp in range(0x323b0) if unicodedata.category(chr(cp)).startswith(pre))
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r_ws, r_p_N, r_p_L = r"\t\n\x0b\x0c\r\x85" + naive("Z"), naive("N"), naive("L")
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naive_re = re.compile("(?i:'s|'t|'re|'ve|'m|'ll|'d)|" +
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f"[^\\r\\n{r_p_N}{r_p_L}]?[{r_p_L}]+|[{r_p_N}]{{1,3}}| ?[^{r_ws}{r_p_N}{r_p_L}]+[\\r\\n]*|[{r_ws}]*[\\r\\n]+|[{r_ws}]+(?![^{r_ws}])|[{r_ws}]+")
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sample = "hello world 한국어 中文 текст ١٢٣ 123 😊\n \ttoday\n'équivalent ²³№ "
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self.assertEqual(SimpleTokenizer({}, {})._split_to_word.findall(sample), naive_re.findall(sample))
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def test_split_regex_speed(self):
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# the naive listing compiles a 429KB pattern that takes 10+s to match a 225KB prompt; ranges keep it small and fast
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tok = SimpleTokenizer({}, {})
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self.assertLess(len(tok._split_to_word.pattern), 100_000)
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text = "The quick brown fox jumps over the lazy dog. " * 5000
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tok._split_to_word.findall(text) # warmup
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tms = []
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for _ in range(5):
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st = time.perf_counter()
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words = tok._split_to_word.findall(text)
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tms.append(time.perf_counter() - st)
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self.assertLess(min(tms), 4) # best-of-5 is robust to CI scheduling pauses; new code takes ~60ms
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self.assertEqual(len(words), 50001)
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def test_llama_continued_conversation(self):
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self._test_coding(self.llama_tok, "hello <|eot_id|>world", [15339, 220, 128009, 14957])
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self._test_coding(self.llama_tok, "hello <|eot_id|>world again", [15339, 220, 128009, 14957, 1578])
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self._test_coding(self.llama_tok, "hello changed <|eot_id|>world again", [15339, 5614, 220, 128009, 14957, 1578])
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def test_long_cached_prompt_matches_fresh_tokenization(self):
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prefix = "system tools\n" * 700 + "<|eot_id|>"
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first, changed = prefix + "run tower of hanoi", prefix + "run ls /"
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expected = self.llama_tok.encode(changed)
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self.llama_tok.encode(first)
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self.assertEqual(self.llama_tok.encode(changed), expected)
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def test_tekken_from_gguf_kv(self):
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kv = {
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"tokenizer.ggml.tokens": ["<unk>", "<s>", "</s>", "[INST]", "[/INST]", "hello"],
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"tokenizer.ggml.token_type": [3, 3, 3, 3, 3, 1],
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"tokenizer.ggml.pre": "tekken",
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"tokenizer.ggml.eos_token_id": 2,
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}
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tok = SimpleTokenizer.from_gguf_kv(kv)
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template = FallbackTemplate(tok)
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self.assertEqual(template.role("user"), "[INST]")
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self.assertEqual(tok.encode("hello"), [5])
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self.assertEqual(template.end_turn(), "[/INST]")
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self.assertEqual(template.role("assistant"), "")
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def test_stream_decoder(self):
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"""stream_decoder buffers incomplete UTF-8: token 25677 has 3/4 of emoji, token 138 completes it."""
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bs = [*range(33, 127), *range(161, 173), *range(174, 256)]
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be = {b: chr(b) for b in bs} | {b: chr(256+i) for i,b in enumerate(b for b in range(256) if b not in bs)}
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token_bytes = {25677: b'\x20\xf0\x9f\x98', 138: b'\x8a'} # ' ' + 3/4 emoji | 1/4 emoji (qwen3.5)
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tok = SimpleTokenizer({"".join(be[b] for b in v): k for k, v in token_bytes.items()}, {})
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dec = tok.stream_decoder()
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self.assertEqual(dec(25677) + dec(138) + dec(), " 😊")
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if __name__ == '__main__':
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unittest.main()
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