Refactor random tokenizer test
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jaime-m-p
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70ca1fe204
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77cbb79532
1 changed files with 65 additions and 81 deletions
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@ -1,15 +1,19 @@
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# tests with BPE tokenizer
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# Test libllama tokenizer == AutoTokenizer.
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# Brute force random tokens/text generation.
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#
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# sample usage:
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# Sample usage:
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#
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# python3 tests/test-tokenizer-0-bpe.py ./models/ggml-vocab-llama-bpe.gguf ~/Data/huggingface/Meta-Llama-3-8B-Instruct/
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# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
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#
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import time
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import logging
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import argparse
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import subprocess
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import random
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from typing import Iterator
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import cffi
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from transformers import AutoTokenizer, PreTrainedTokenizerBase
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@ -30,7 +34,7 @@ class LibLlama:
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def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str):
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cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h]
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res = subprocess.run(cmd, stdout=subprocess.PIPE)
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assert(res.returncode == 0)
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assert (res.returncode == 0)
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source = res.stdout.decode()
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ffi = cffi.FFI()
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if True: # workarounds for pycparser
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@ -61,12 +65,12 @@ class LibLlamaModel:
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def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
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self.lib = libllama.lib
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self.ffi = libllama.ffi
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if type(mparams) == dict:
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if isinstance(mparams, dict):
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mparams = libllama.model_default_params(**mparams)
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self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
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if not self.model:
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raise RuntimeError("error: failed to load model '%s'" % path_model)
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if type(cparams) == dict:
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if isinstance(cparams, dict):
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cparams = libllama.context_default_params(**cparams)
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self.ctx = self.lib.llama_new_context_with_model(self.model, cparams)
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if not self.ctx:
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@ -92,18 +96,9 @@ class LibLlamaModel:
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return list(self.token_ids[0:num])
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def find_first_mismatch(ids1: list[int], ids2: list[int]):
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for i, (a,b) in enumerate(zip(ids1, ids2)):
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if a != b:
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return i
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if len(ids1) == len(ids2):
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return -1
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return min(len(ids1), len(ids2))
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def test_custom_texts(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase):
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tests = [
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def generator_custom_text() -> Iterator[str]:
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"""General tests"""
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yield from [
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"",
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" ",
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" ",
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@ -146,7 +141,10 @@ def test_custom_texts(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase):
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"333333333",
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]
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more_tests = [
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def generator_custom_text_edge_cases() -> Iterator[str]:
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"""Edge cases found while debugging"""
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yield from [
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'\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F}
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'¼-a', # unicode_ranges_digit, 0x00BC
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'½-a', # unicode_ranges_digit, 0x00BD
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@ -157,18 +155,9 @@ def test_custom_texts(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase):
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'<s>a' # TODO: Phi-3 fail
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]
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for text in tests + more_tests:
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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logger.info(repr(text))
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if ids1 != ids2:
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logger.info(" TokenIDs: " + str(list(ids1)))
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logger.info(" Expected: " + str(list(ids2)))
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logger.info(" Index: %d" % find_first_mismatch(ids1, ids2))
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raise Exception()
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def test_random_chars(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, iterations = 100):
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def generator_random_chars(iterations = 100) -> Iterator[str]:
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"""Brute force random text with simple characters"""
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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CHARS = list(set("""
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@ -179,13 +168,9 @@ def test_random_chars(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase,
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.-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_
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"""))
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logger.info("Bruteforce random chars encodings ...")
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rand = random.Random()
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for m in range(iterations):
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logger.debug("%d/%d" % (m + 1, iterations))
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rand.seed(m)
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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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@ -193,61 +178,41 @@ def test_random_chars(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase,
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word = rand.choices(CHARS, k=k)
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space = rand.choice(WHITESPACES)
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text.append("".join(word) + space)
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text = "".join(text)
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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assert(ids1 == ids2)
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yield "".join(text)
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def test_random_vocab_chars(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, iterations = 100):
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def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
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"""Brute force random text with vocab characters"""
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logger.info("Building vocab char list ...")
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vocab_ids = list(tokenizer.vocab.values())
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vocab_text = tokenizer.decode(vocab_ids)
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vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True)
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vocab_chars = list(set(vocab_text))
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del vocab_ids, vocab_text
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logger.info("Bruteforce random text encodings ...")
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rand = random.Random()
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for m in range(iterations):
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logger.debug("%d/%d" % (m + 1, iterations))
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rand.seed(m)
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text = rand.choices(vocab_chars, k=1024)
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text = "".join(text)
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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assert(ids1 == ids2)
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yield "".join(text)
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def test_random_vocab_tokens(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, iterations = 100):
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def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
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"""Brute force random text from vocab tokens"""
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logger.info("Building token list ...")
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space_id = tokenizer.encode(" ")[0]
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space_id = tokenizer.encode(" ", add_special_tokens=False)[0]
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vocab_ids = list(tokenizer.vocab.values())
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vocab_ids = list(sorted(vocab_ids + vocab_ids))
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for i in range(1, len(vocab_ids), 2):
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vocab_ids[i] = space_id
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vocab_tokens = tokenizer.decode(vocab_ids)
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vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True)
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vocab_tokens = vocab_tokens.split(" ")
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del vocab_ids
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logger.info("Checking single token encodings ...")
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for token in vocab_tokens:
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ids1 = model.tokenize(token, parse_special=True)
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ids2 = tokenizer.encode(token)
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assert(ids1 == ids2)
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yield from vocab_tokens
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logger.info("Bruteforce random text encodings ...")
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rand = random.Random()
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for m in range(iterations):
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logger.debug("%d/%d" % (m + 1, iterations))
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rand.seed(m)
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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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@ -256,24 +221,17 @@ def test_random_vocab_tokens(model: LibLlamaModel, tokenizer: PreTrainedTokenize
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tokens = [t.strip(" \n\r\t") for t in tokens]
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sep = rand.choice(" \n\r\t")
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text.append("".join(tokens) + sep)
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text = "".join(text)
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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assert(ids1 == ids2)
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yield "".join(text)
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def test_random_bytes(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, iterations = 100):
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def generator_random_bytes(iterations = 100) -> Iterator[str]:
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"""Brute force random bytes"""
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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logger.info("Bruteforce random bytes encodings ...")
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rand = random.Random()
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for m in range(iterations):
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logger.debug("%d/%d" % (m + 1, iterations))
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rand.seed(m)
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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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@ -281,11 +239,36 @@ def test_random_bytes(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase,
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word = [chr(r) for r in rand.randbytes(k) if r]
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word.append(rand.choice(WHITESPACES))
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text.append("".join(word))
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text = "".join(text)
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yield "".join(text)
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def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]):
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def find_first_mismatch(ids1: list[int], ids2: list[int]):
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for i, (a,b) in enumerate(zip(ids1, ids2)):
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if a != b:
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return i
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if len(ids1) == len(ids2):
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return -1
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return min(len(ids1), len(ids2))
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t0 = time.perf_counter()
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logger.info("%s: %s" % (generator.__name__, "ini"))
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for text in generator:
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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assert(ids1 == ids2)
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if ids1 != ids2:
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i = find_first_mismatch(ids1, ids2)
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ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
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ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
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text2 = tokenizer.decode(ids2, skip_special_tokens=True)
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assert (text2 in text)
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logger.info(" Text: " + repr(text2))
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logger.info(" TokenIDs: " + str(ids1))
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logger.info(" Expected: " + str(ids2))
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raise Exception()
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t1 = time.perf_counter()
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logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
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if __name__ == "__main__":
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@ -302,10 +285,11 @@ if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
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test_custom_texts(model, tokenizer)
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test_random_chars(model, tokenizer, 10_000)
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test_random_vocab_chars(model, tokenizer, 10_000)
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test_random_vocab_tokens(model, tokenizer, 10_000)
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# test_random_bytes(model, tokenizer, 10_000) # FAIL
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test_compare_tokenizer(model, tokenizer, generator_custom_text())
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test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases())
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test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000))
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test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000))
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test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000))
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# test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL
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model.free()
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