import unittest, base64, functools, re, sys, time, unicodedata from tinygrad.llm.cli import SimpleTokenizer, FallbackTemplate from tinygrad.helpers import fetch @unittest.skipIf(sys.platform == 'win32', "fetch race condition on Windows") class TestLLMTokenizer(unittest.TestCase): @functools.cached_property def llama_tok(self): # from https://github.com/tinygrad/tinygrad/blob/e0106b6b257ebc003eb3694144e3e198f7d8cc37/examples/llama3.py#L14 model_file = fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model") with open(model_file, "rt") as fd: str_vocab = [line.split(maxsplit=1) for line in fd.read().splitlines() if line] # https://github.com/openai/gpt-2/blob/9b63575ef42771a015060c964af2c3da4cf7c8ab/src/encoder.py#L9 bs = [*range(33, 127), *range(161, 173), *range(174, 256)] # bytes that map to themselves _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)} _byte_encoder = {v:k for k,v in _byte_decoder.items()} normal_tokens = {''.join([_byte_encoder[x] for x in base64.b64decode(stok)]): int(srank) for stok, srank in str_vocab} special_tokens = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|reserved_special_token_2|>", "<|reserved_special_token_3|>", "<|start_header_id|>", "<|end_header_id|>", "<|reserved_special_token_4|>", "<|eot_id|>", ] + [ f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5) ] return SimpleTokenizer(normal_tokens, {token: len(normal_tokens) + i for i, token in enumerate(special_tokens)}) def _test_coding(self, tok: SimpleTokenizer, text: str, expected_tokens: list[int]): self.assertEqual(tok.encode(text), expected_tokens) self.assertEqual(tok.decode(expected_tokens), text) # NOTE: the correct tokenization for this can only be found by looking up the text chunk in the vocab, not by applying merges def test_llama_early_tokenize(self): self._test_coding(self.llama_tok, " например", [ 111797 ]) def test_llama_basic(self): self._test_coding(self.llama_tok, "hello world", [ 15339, 1917 ]) def test_llama_control_char(self): self._test_coding(self.llama_tok, " \x850", [ 220, 116360, 15 ]) def test_llama_bytes(self): self._test_coding(self.llama_tok, " \xec\x8b\xa4\xed", [ 1717, 105, 116174, 82638, 2483 ]) def test_llama_special1(self): self._test_coding(self.llama_tok, "hello <|end_of_text|>", [ 15339, 220, 128001 ]) def test_llama_special2(self): self._test_coding(self.llama_tok, "<|start_header_id|>user<|end_header_id|>\n\n", [ 128006, 882, 128007, 271 ]) def test_llama_repeat(self): self._test_coding(self.llama_tok, "00000000000000000", [ 931, 931, 931, 931, 931, 410 ]) def test_llama_pat(self): self._test_coding(self.llama_tok, "today\n \n", [ 31213, 14211 ]) def test_split_regex_matches_naive_listing(self): # the compacted codepoint ranges must match the same text as listing every codepoint def naive(pre): return "".join(re.escape(chr(cp)) for cp in range(0x323b0) if unicodedata.category(chr(cp)).startswith(pre)) r_ws, r_p_N, r_p_L = r"\t\n\x0b\x0c\r\x85" + naive("Z"), naive("N"), naive("L") naive_re = re.compile("(?i:'s|'t|'re|'ve|'m|'ll|'d)|" + 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}]+") sample = "hello world 한국어 中文 текст ١٢٣ 123 😊\n \ttoday\n'équivalent ²³№ " self.assertEqual(SimpleTokenizer({}, {})._split_to_word.findall(sample), naive_re.findall(sample)) def test_split_regex_speed(self): # the naive listing compiles a 429KB pattern that takes 10+s to match a 225KB prompt; ranges keep it small and fast tok = SimpleTokenizer({}, {}) self.assertLess(len(tok._split_to_word.pattern), 100_000) text = "The quick brown fox jumps over the lazy dog. " * 5000 tok._split_to_word.findall(text) # warmup tms = [] for _ in range(5): st = time.perf_counter() words = tok._split_to_word.findall(text) tms.append(time.perf_counter() - st) self.assertLess(min(tms), 4) # best-of-5 is robust to CI scheduling pauses; new code takes ~60ms self.assertEqual(len(words), 50001) def test_llama_continued_conversation(self): self._test_coding(self.llama_tok, "hello <|eot_id|>world", [15339, 220, 128009, 14957]) self._test_coding(self.llama_tok, "hello <|eot_id|>world again", [15339, 220, 128009, 14957, 1578]) self._test_coding(self.llama_tok, "hello changed <|eot_id|>world again", [15339, 5614, 220, 128009, 14957, 1578]) def test_long_cached_prompt_matches_fresh_tokenization(self): prefix = "system tools\n" * 700 + "<|eot_id|>" first, changed = prefix + "run tower of hanoi", prefix + "run ls /" expected = self.llama_tok.encode(changed) self.llama_tok.encode(first) self.assertEqual(self.llama_tok.encode(changed), expected) def test_tekken_from_gguf_kv(self): kv = { "tokenizer.ggml.tokens": ["", "", "", "[INST]", "[/INST]", "hello"], "tokenizer.ggml.token_type": [3, 3, 3, 3, 3, 1], "tokenizer.ggml.pre": "tekken", "tokenizer.ggml.eos_token_id": 2, } tok = SimpleTokenizer.from_gguf_kv(kv) template = FallbackTemplate(tok) self.assertEqual(template.role("user"), "[INST]") self.assertEqual(tok.encode("hello"), [5]) self.assertEqual(template.end_turn(), "[/INST]") self.assertEqual(template.role("assistant"), "") def test_tekken_gpt4o_split(self): split = {p: SimpleTokenizer({}, {}, p)._split_to_word.findall for p in ("tekken", "gpt-4o")} shared = { "HelloWorld": ["Hello", "World"], " ÜNICODE": [" ÜNICODE"], # Ü: non-ascii upper joins the run "é café": ["é", " café"], # first é is e + U+0301 combining acute (NFD) "เพื่อน วิ": ["เพื่อน", " วิ"], # thai vowel marks stay in the word "a/b\r\n x": ["a", "/b", "\r\n", " x"], # punct tail eats / } for s, want in shared.items(): self.assertEqual(split["tekken"](s), want, f"tekken {s!r}") self.assertEqual(split["gpt-4o"](s), want, f"gpt-4o {s!r}") differ = [ ("12345", list("12345"), ["123", "45"]), # digits: tekken single, o200k groups {1,3} ("it's I'M don'T", ["it", "'s", " I", "'M", " don", "'T"], ["it's", " I'M", " don'T"]), # contraction: o200k inline suffix ] for s, tk, go in differ: self.assertEqual(split["tekken"](s), tk, f"tekken {s!r}") self.assertEqual(split["gpt-4o"](s), go, f"gpt-4o {s!r}") def test_stream_decoder(self): """stream_decoder buffers incomplete UTF-8: token 25677 has 3/4 of emoji, token 138 completes it.""" bs = [*range(33, 127), *range(161, 173), *range(174, 256)] 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)} token_bytes = {25677: b'\x20\xf0\x9f\x98', 138: b'\x8a'} # ' ' + 3/4 emoji | 1/4 emoji (qwen3.5) tok = SimpleTokenizer({"".join(be[b] for b in v): k for k, v in token_bytes.items()}, {}) dec = tok.stream_decoder() self.assertEqual(dec(25677) + dec(138) + dec(), " 😊") if __name__ == '__main__': unittest.main()