Files
axolotl/tests/test_tokenizers.py
Wing Lian fc4e37920b transformers v5 upgrade (#3272)
* Prepare for transformers v5 upgrade

* fix hf cli

* update for hf hub changes

* fix tokenizer apply_chat_template args

* remap include_tokens_per_second

* fix tps

* handle migration for warmup

* use latest hf hub

* Fix scan -> ls

* fix import

* fix for renaming of mistral common tokenizer -> backend

* update for fixed tokenziation for llama

* Skip phi35 tests for now

* remove mistral patch fixed upstream in huggingface/transformers#41439

* use namespacing for patch

* don't rely on sdist for e2e tests for now

* run modal ci without waiting too

* Fix dep for ci

* fix imports

* Fix fp8 check

* fsdp2 fixes

* fix version handling

* update fsdp version tests for new v5 behavior

* Fail multigpu tests after 3 failures

* skip known v5 broken tests for now and cleanup

* bump deps

* unmark skipped test

* re-enable test_fsdp_qlora_prequant_packed test

* increase multigpu ci timeout

* skip broken gemma3 test

* reduce timout back to original 120min now that the hanging test is skipped

* fix for un-necessary collator for pretraining with bsz=1

* fix: safe_serialization deprecated in transformers v5 rc01 (#3318)

* torch_dtype deprecated

* load model in float32 for consistency with tests

* revert some test fixtures back

* use hf cache ls instead of scan

* don't strip fsdp_version

more fdsp_Version fixes for v5
fix version in fsdp_config
fix aliasing
fix fsdp_version check
check fsdp_version is 2 in both places

* Transformers v5 rc2 (#3347)

* bump dep

* use latest fbgemm, grab model config as part of fixture, un-skip test

* import AutoConfig

* don't need more problematic autoconfig when specifying config.json manually

* add fixtures for argilla ultrafeedback datasets

* download phi4-reasoning

* fix arg

* update tests for phi fast tokenizer changes

* use explicit model types for gemma3

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

* fix: AutoModelForVision2Seq -> AutoModelForImageTextToText

* chore: remove duplicate

* fix: attempt fix gemma3 text mode

* chore: lint

* ga release of v5

* need property setter for name_or_path for mistral tokenizer

* vllm not compatible with transformers v5

* setter for chat_template w mistral too

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
2026-01-27 17:08:24 -05:00

163 lines
5.0 KiB
Python

"""
Test cases for the tokenizer loading
"""
import unittest
import pytest
from axolotl.loaders import load_tokenizer
from axolotl.utils.dict import DictDefault
from tests.hf_offline_utils import enable_hf_offline
class TestTokenizers:
"""
test class for the load_tokenizer fn
"""
@pytest.mark.skip("LlamaTokenizer no longer has a Fast/Slow tokenizer")
@enable_hf_offline
def test_default_use_fast(self):
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
}
)
tokenizer = load_tokenizer(cfg)
assert "Fast" in tokenizer.__class__.__name__
@pytest.mark.skip("LlamaTokenizer no longer has a Fast/Slow tokenizer")
@enable_hf_offline
def test_dont_use_fast(self):
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
"tokenizer_use_fast": False,
}
)
tokenizer = load_tokenizer(cfg)
assert "Fast" not in tokenizer.__class__.__name__
@enable_hf_offline
def test_special_tokens_modules_to_save(self):
# setting special_tokens to new token
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
"adapter": "lora",
"special_tokens": {"bos_token": "[INST]"},
}
)
with pytest.raises(
ValueError,
match=r".*Please set lora_modules_to_save*",
):
load_tokenizer(cfg)
# setting special_tokens but not changing from default
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
"adapter": "lora",
"special_tokens": {"bos_token": "<s>"},
}
)
load_tokenizer(cfg)
# non-adapter setting special_tokens
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
"special_tokens": {"bos_token": "[INST]"},
}
)
load_tokenizer(cfg)
@enable_hf_offline
def test_add_additional_special_tokens(self):
cfg = DictDefault(
{
"tokenizer_config": "huggyllama/llama-7b",
"special_tokens": {"additional_special_tokens": ["<|im_start|>"]},
}
)
tokenizer = load_tokenizer(cfg)
assert tokenizer("<|im_start|>user")["input_ids"] == [1, 32000, 1404]
assert len(tokenizer) == 32001
# ensure reloading the tokenizer again from cfg results in same vocab length
tokenizer = load_tokenizer(cfg)
assert len(tokenizer) == 32001
@enable_hf_offline
def test_added_tokens_overrides(self, temp_dir):
cfg = DictDefault(
{
# use with tokenizer that has reserved_tokens in added_tokens
"tokenizer_config": "NousResearch/Llama-3.2-1B",
"added_tokens_overrides": {
128041: "RANDOM_OVERRIDE_1",
128042: "RANDOM_OVERRIDE_2",
},
"output_dir": temp_dir,
}
)
tokenizer = load_tokenizer(cfg)
assert tokenizer.encode("RANDOM_OVERRIDE_1", add_special_tokens=False) == [
128041
]
assert tokenizer.encode("RANDOM_OVERRIDE_2", add_special_tokens=False) == [
128042
]
assert (
tokenizer.decode([128041, 128042]) == "RANDOM_OVERRIDE_1RANDOM_OVERRIDE_2"
)
@enable_hf_offline
def test_added_tokens_overrides_gemma3(self, temp_dir):
cfg = DictDefault(
{
# use with tokenizer that has reserved_tokens in added_tokens
"tokenizer_config": "mlx-community/gemma-3-4b-it-8bit",
"added_tokens_overrides": {
256001: "RANDOM_OVERRIDE_1",
256002: "RANDOM_OVERRIDE_2",
},
"output_dir": temp_dir,
}
)
tokenizer = load_tokenizer(cfg)
assert tokenizer.encode("RANDOM_OVERRIDE_1", add_special_tokens=False) == [
256001
]
assert tokenizer.encode("RANDOM_OVERRIDE_2", add_special_tokens=False) == [
256002
]
assert (
tokenizer.decode([256001, 256002]) == "RANDOM_OVERRIDE_1RANDOM_OVERRIDE_2"
)
@enable_hf_offline
def test_added_tokens_overrides_with_toolargeid(self, temp_dir):
cfg = DictDefault(
{
# use with tokenizer that has reserved_tokens in added_tokens
"tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
"added_tokens_overrides": {1000000: "BROKEN_RANDOM_OVERRIDE_1"},
"output_dir": temp_dir,
}
)
with pytest.raises(
ValueError, match=r".*Token ID 1000000 not found in added_tokens.*"
):
load_tokenizer(cfg)
if __name__ == "__main__":
unittest.main()