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[deps] adapt to transformers v5 (#10147)

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Co-authored-by: frozenleaves <frozen@Mac.local>
Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
This commit is contained in:
浮梦
2026-02-02 12:07:19 +08:00
committed by GitHub
parent 762b480131
commit bf04ca6af8
23 changed files with 149 additions and 120 deletions
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2
examples/v1/train_full/train_full_fsdp2.yaml
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@@ -18,7 +18,7 @@ init_config:
name: init_on_meta
### data
train_dataset: data/v1_sft_demo.yaml
train_dataset: data/v1_sft_demo.yaml
### training
output_dir: outputs/test_fsdp2

4
pyproject.toml
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@@ -40,10 +40,10 @@ dependencies = [
"torch>=2.4.0",
"torchvision>=0.19.0",
"torchaudio>=2.4.0",
"transformers>=4.51.0,<=4.57.1,!=4.52.0,!=4.57.0",
"transformers>=4.51.0,<=5.0.0,!=4.52.0,!=4.57.0",
"datasets>=2.16.0,<=4.0.0",
"accelerate>=1.3.0,<=1.11.0",
"peft>=0.14.0,<=0.17.1",
"peft>=0.18.0,<=0.18.1",
"trl>=0.18.0,<=0.24.0",
"torchdata>=0.10.0,<=0.11.0",
# gui

2
requirements/deepspeed.txt
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@@ -1 +1 @@
deepspeed>=0.10.0,<=0.16.9
deepspeed>=0.10.0,<=0.18.4

4
src/llamafactory/extras/misc.py
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@@ -94,10 +94,10 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version("transformers>=4.51.0,<=4.57.1")
check_version("transformers>=4.51.0,<=5.0.0")
check_version("datasets>=2.16.0,<=4.0.0")
check_version("accelerate>=1.3.0,<=1.11.0")
check_version("peft>=0.14.0,<=0.17.1")
check_version("peft>=0.18.0,<=0.18.1")
check_version("trl>=0.18.0,<=0.24.0")

4
src/llamafactory/hparams/data_args.py
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@@ -65,7 +65,9 @@ class DataArguments:
)
mix_strategy: Literal["concat", "interleave_under", "interleave_over", "interleave_once"] = field(
default="concat",
metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling/sampling w.o. replacement)."},
metadata={
"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling/sampling w.o. replacement)."
},
)
interleave_probs: str | None = field(
default=None,

3
src/llamafactory/hparams/model_args.py
View File

@@ -206,9 +206,6 @@ class BaseModelArguments:
if self.model_name_or_path is None:
raise ValueError("Please provide `model_name_or_path`.")
if self.split_special_tokens and self.use_fast_tokenizer:
raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
if self.adapter_name_or_path is not None: # support merging multiple lora weights
self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]

6
src/llamafactory/hparams/parser.py
View File

@@ -139,10 +139,6 @@ def _verify_model_args(
if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
if data_args.template == "yi" and model_args.use_fast_tokenizer:
logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
model_args.use_fast_tokenizer = False
def _check_extra_dependencies(
model_args: "ModelArguments",
@@ -188,9 +184,7 @@ def _check_extra_dependencies(
if training_args is not None:
if training_args.deepspeed:
# pin deepspeed version < 0.17 because of https://github.com/deepspeedai/DeepSpeed/issues/7347
check_version("deepspeed", mandatory=True)
check_version("deepspeed>=0.10.0,<=0.16.9")
if training_args.predict_with_generate:
check_version("jieba", mandatory=True)

5
src/llamafactory/model/loader.py
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@@ -22,7 +22,6 @@ from transformers import (
AutoModelForImageTextToText,
AutoModelForSeq2SeqLM,
AutoModelForTextToWaveform,
AutoModelForVision2Seq,
AutoProcessor,
AutoTokenizer,
)
@@ -166,11 +165,9 @@ def load_model(
else:
if type(config) in AutoModelForImageTextToText._model_mapping.keys(): # image-text
load_class = AutoModelForImageTextToText
elif type(config) in AutoModelForVision2Seq._model_mapping.keys(): # image-text
load_class = AutoModelForVision2Seq
elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys(): # audio-text
load_class = AutoModelForSeq2SeqLM
elif type(config) in AutoModelForTextToWaveform._model_mapping.keys(): # audio hack for qwen omni
elif type(config) in AutoModelForTextToWaveform._model_mapping.keys(): # audio-text for qwen omni
load_class = AutoModelForTextToWaveform
else:
load_class = AutoModelForCausalLM

8
src/llamafactory/model/model_utils/visual.py
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@@ -374,7 +374,13 @@ _register_composite_model(
_register_composite_model(
model_type="qwen3_omni_moe_thinker",
projector_key="visual.merger",
vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list", "audio_tower"],
vision_model_keys=[
"visual.pos_embed",
"visual.patch_embed",
"visual.blocks",
"visual.deepstack_merger_list",
"audio_tower",
],
language_model_keys=["model", "lm_head"],
lora_conflict_keys=["patch_embed"],
)

10
src/llamafactory/train/callbacks.py
View File

@@ -103,7 +103,9 @@ class FixValueHeadModelCallback(TrainerCallback):
if args.should_save:
output_dir = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
fix_valuehead_checkpoint(
model=kwargs.pop("model"), output_dir=output_dir, safe_serialization=args.save_safetensors
model=kwargs.pop("model"),
output_dir=output_dir,
safe_serialization=getattr(args, "save_safetensors", True),
)
@@ -137,7 +139,7 @@ class PissaConvertCallback(TrainerCallback):
if isinstance(model, PeftModel):
init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
setattr(model.peft_config["default"], "init_lora_weights", True)
model.save_pretrained(pissa_init_dir, safe_serialization=args.save_safetensors)
model.save_pretrained(pissa_init_dir, safe_serialization=getattr(args, "save_safetensors", True))
setattr(model.peft_config["default"], "init_lora_weights", init_lora_weights)
@override
@@ -155,11 +157,11 @@ class PissaConvertCallback(TrainerCallback):
if isinstance(model, PeftModel):
init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
setattr(model.peft_config["default"], "init_lora_weights", True)
model.save_pretrained(pissa_backup_dir, safe_serialization=args.save_safetensors)
model.save_pretrained(pissa_backup_dir, safe_serialization=getattr(args, "save_safetensors", True))
setattr(model.peft_config["default"], "init_lora_weights", init_lora_weights)
model.save_pretrained(
pissa_convert_dir,
safe_serialization=args.save_safetensors,
safe_serialization=getattr(args, "save_safetensors", True),
path_initial_model_for_weight_conversion=pissa_init_dir,
)
model.load_adapter(pissa_backup_dir, "default", is_trainable=True)

2
src/llamafactory/train/ppo/workflow.py
View File

@@ -72,7 +72,7 @@ def run_ppo(
ppo_trainer.ppo_train(resume_from_checkpoint=training_args.resume_from_checkpoint)
ppo_trainer.save_model()
if training_args.should_save:
fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
fix_valuehead_checkpoint(model, training_args.output_dir, getattr(training_args, "save_safetensors", True))
ppo_trainer.save_state() # must be called after save_model to have a folder
if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:

2
src/llamafactory/train/rm/trainer.py
View File

@@ -114,7 +114,7 @@ class PairwiseTrainer(Trainer):
if state_dict is None:
state_dict = self.model.state_dict()
if self.args.save_safetensors:
if getattr(self.args, "save_safetensors", True):
from collections import defaultdict
ptrs = defaultdict(list)

2
src/llamafactory/train/rm/workflow.py
View File

@@ -65,7 +65,7 @@ def run_rm(
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_model()
if training_args.should_save:
fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
fix_valuehead_checkpoint(model, training_args.output_dir, getattr(training_args, "save_safetensors", True))
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)

10
tests/conftest.py
View File

@@ -18,7 +18,6 @@ Contains shared fixtures, pytest configuration, and custom markers.
"""
import os
import sys
import pytest
import torch
@@ -149,14 +148,7 @@ def _manage_distributed_env(request: FixtureRequest, monkeypatch: MonkeyPatch) -
devices_str = ",".join(str(i) for i in range(required))
monkeypatch.setenv(env_key, devices_str)
# add project root dir to path for mp run
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if project_root not in sys.path:
sys.path.insert(0, project_root)
os.environ["PYTHONPATH"] = project_root + os.pathsep + os.environ.get("PYTHONPATH", "")
monkeypatch.syspath_prepend(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
else: # non-distributed test
if old_value:
visible_devices = [v for v in old_value.split(",") if v != ""]

8
tests/data/processor/test_feedback.py
View File

@@ -20,6 +20,7 @@ from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import load_dataset_module
@@ -52,7 +53,12 @@ def test_feedback_data(num_samples: int):
for index in indexes:
messages = original_data["messages"][index]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
prompt_len = len(ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True))
ref_prompt_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
ref_prompt_ids = ref_prompt_ids["input_ids"]
prompt_len = len(ref_prompt_ids)
ref_labels = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_labels

19
tests/data/processor/test_pairwise.py
View File

@@ -20,6 +20,7 @@ from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import load_dataset_module
@@ -63,13 +64,21 @@ def test_pairwise_data(num_samples: int):
rejected_messages = original_data["conversations"][index] + [original_data["rejected"][index]]
chosen_messages = _convert_sharegpt_to_openai(chosen_messages)
rejected_messages = _convert_sharegpt_to_openai(rejected_messages)
ref_chosen_input_ids = ref_tokenizer.apply_chat_template(chosen_messages)
chosen_prompt_len = len(ref_tokenizer.apply_chat_template(chosen_messages[:-1], add_generation_prompt=True))
ref_chosen_labels = [IGNORE_INDEX] * chosen_prompt_len + ref_chosen_input_ids[chosen_prompt_len:]
ref_chosen_prompt_ids = ref_tokenizer.apply_chat_template(chosen_messages[:-1], add_generation_prompt=True)
ref_rejected_input_ids = ref_tokenizer.apply_chat_template(rejected_messages)
rejected_prompt_len = len(
ref_tokenizer.apply_chat_template(rejected_messages[:-1], add_generation_prompt=True)
)
ref_rejected_prompt_ids = ref_tokenizer.apply_chat_template(rejected_messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_chosen_input_ids = ref_chosen_input_ids["input_ids"]
ref_rejected_input_ids = ref_rejected_input_ids["input_ids"]
ref_chosen_prompt_ids = ref_chosen_prompt_ids["input_ids"]
ref_rejected_prompt_ids = ref_rejected_prompt_ids["input_ids"]
chosen_prompt_len = len(ref_chosen_prompt_ids)
rejected_prompt_len = len(ref_rejected_prompt_ids)
ref_chosen_labels = [IGNORE_INDEX] * chosen_prompt_len + ref_chosen_input_ids[chosen_prompt_len:]
ref_rejected_labels = [IGNORE_INDEX] * rejected_prompt_len + ref_rejected_input_ids[rejected_prompt_len:]
assert train_dataset["chosen_input_ids"][index] == ref_chosen_input_ids
assert train_dataset["chosen_labels"][index] == ref_chosen_labels

31
tests/data/processor/test_supervised.py
View File

@@ -20,6 +20,7 @@ from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import load_dataset_module
@@ -59,7 +60,16 @@ def test_supervised_single_turn(num_samples: int):
{"role": "assistant", "content": original_data["output"][index]},
]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
ref_prompt_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
ref_prompt_ids = ref_prompt_ids["input_ids"]
prompt_len = len(ref_prompt_ids)
ref_label_ids = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_label_ids
@pytest.mark.runs_on(["cpu", "mps"])
@@ -73,6 +83,10 @@ def test_supervised_multi_turn(num_samples: int):
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
ref_input_ids = ref_tokenizer.apply_chat_template(original_data["messages"][index])
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
# cannot test the label ids in multi-turn case
assert train_dataset["input_ids"][index] == ref_input_ids
@@ -86,9 +100,12 @@ def test_supervised_train_on_prompt(num_samples: int):
original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
ref_ids = ref_tokenizer.apply_chat_template(original_data["messages"][index])
assert train_dataset["input_ids"][index] == ref_ids
assert train_dataset["labels"][index] == ref_ids
ref_input_ids = ref_tokenizer.apply_chat_template(original_data["messages"][index])
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_input_ids
@pytest.mark.runs_on(["cpu", "mps"])
@@ -103,7 +120,13 @@ def test_supervised_mask_history(num_samples: int):
for index in indexes:
messages = original_data["messages"][index]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
prompt_len = len(ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True))
ref_prompt_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
ref_prompt_ids = ref_prompt_ids["input_ids"]
prompt_len = len(ref_prompt_ids)
ref_label_ids = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_label_ids

14
tests/data/processor/test_unsupervised.py
View File

@@ -19,6 +19,7 @@ import pytest
from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import load_dataset_module
@@ -55,8 +56,13 @@ def test_unsupervised_data(num_samples: int):
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
messages = original_data["messages"][index]
ref_ids = ref_tokenizer.apply_chat_template(messages)
ref_input_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
ref_labels = ref_ids[len(ref_input_ids) :]
assert train_dataset["input_ids"][index] == ref_input_ids
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
ref_prompt_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_input_ids = ref_input_ids["input_ids"]
ref_prompt_ids = ref_prompt_ids["input_ids"]
ref_labels = ref_input_ids[len(ref_prompt_ids) :]
assert train_dataset["input_ids"][index] == ref_prompt_ids
assert train_dataset["labels"][index] == ref_labels

4
tests/data/test_collator.py
View File

@@ -17,7 +17,7 @@ import os
import pytest
import torch
from PIL import Image
from transformers import AutoConfig, AutoModelForVision2Seq
from transformers import AutoConfig, AutoModelForImageTextToText
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.collator import MultiModalDataCollatorForSeq2Seq, prepare_4d_attention_mask
@@ -82,7 +82,7 @@ def test_multimodal_collator():
template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = AutoModelForImageTextToText.from_config(config)
data_collator = MultiModalDataCollatorForSeq2Seq(
template=template,

84
tests/data/test_template.py
View File

@@ -20,6 +20,7 @@ from transformers import AutoTokenizer
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.template import parse_template
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import DataArguments
@@ -65,7 +66,6 @@ def _check_template(
template_name: str,
prompt_str: str,
answer_str: str,
use_fast: bool,
messages: list[dict[str, str]] = MESSAGES,
) -> None:
r"""Check template.
@@ -75,13 +75,15 @@ def _check_template(
template_name: the template name.
prompt_str: the string corresponding to the prompt part.
answer_str: the string corresponding to the answer part.
use_fast: whether to use fast tokenizer.
messages: the list of messages.
"""
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
tokenizer = AutoTokenizer.from_pretrained(model_id)
content_str = tokenizer.apply_chat_template(messages, tokenize=False)
content_ids = tokenizer.apply_chat_template(messages, tokenize=True)
if is_transformers_version_greater_than("5.0.0"):
content_ids = content_ids["input_ids"]
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, messages)
assert content_str == prompt_str + answer_str
@@ -90,9 +92,8 @@ def _check_template(
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_oneturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
def test_encode_oneturn():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
prompt_str = (
@@ -106,9 +107,8 @@ def test_encode_oneturn(use_fast: bool):
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_multiturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
def test_encode_multiturn():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
prompt_str_1 = (
@@ -128,11 +128,10 @@ def test_encode_multiturn(use_fast: bool):
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
def test_reasoning_encode_oneturn(cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
@@ -155,11 +154,10 @@ def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thi
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
def test_reasoning_encode_multiturn(cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
@@ -185,10 +183,9 @@ def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_t
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_jinja_template(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
def test_jinja_template():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
tokenizer.chat_template = template._get_jinja_template(tokenizer) # llama3 template no replace
assert tokenizer.chat_template != ref_tokenizer.chat_template
@@ -222,8 +219,7 @@ def test_get_stop_token_ids():
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_gemma_template(use_fast: bool):
def test_gemma_template():
prompt_str = (
f"<bos><start_of_turn>user\n{MESSAGES[0]['content']}<end_of_turn>\n"
f"<start_of_turn>model\n{MESSAGES[1]['content']}<end_of_turn>\n"
@@ -231,13 +227,12 @@ def test_gemma_template(use_fast: bool):
"<start_of_turn>model\n"
)
answer_str = f"{MESSAGES[3]['content']}<end_of_turn>\n"
_check_template("google/gemma-3-4b-it", "gemma", prompt_str, answer_str, use_fast)
_check_template("google/gemma-3-4b-it", "gemma", prompt_str, answer_str)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_gemma2_template(use_fast: bool):
def test_gemma2_template():
prompt_str = (
f"<bos><start_of_turn>user\n{MESSAGES[0]['content']}<end_of_turn>\n"
f"<start_of_turn>model\n{MESSAGES[1]['content']}<end_of_turn>\n"
@@ -245,13 +240,12 @@ def test_gemma2_template(use_fast: bool):
"<start_of_turn>model\n"
)
answer_str = f"{MESSAGES[3]['content']}<end_of_turn>\n"
_check_template("google/gemma-2-2b-it", "gemma2", prompt_str, answer_str, use_fast)
_check_template("google/gemma-2-2b-it", "gemma2", prompt_str, answer_str)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_llama3_template(use_fast: bool):
def test_llama3_template():
prompt_str = (
f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{MESSAGES[0]['content']}<|eot_id|>"
f"<|start_header_id|>assistant<|end_header_id|>\n\n{MESSAGES[1]['content']}<|eot_id|>"
@@ -259,14 +253,11 @@ def test_llama3_template(use_fast: bool):
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = f"{MESSAGES[3]['content']}<|eot_id|>"
_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize(
"use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Llama 4 has no slow tokenizer."))]
)
def test_llama4_template(use_fast: bool):
def test_llama4_template():
prompt_str = (
f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{MESSAGES[0]['content']}<|eot|>"
f"<|header_start|>assistant<|header_end|>\n\n{MESSAGES[1]['content']}<|eot|>"
@@ -274,18 +265,11 @@ def test_llama4_template(use_fast: bool):
"<|header_start|>assistant<|header_end|>\n\n"
)
answer_str = f"{MESSAGES[3]['content']}<|eot|>"
_check_template(TINY_LLAMA4, "llama4", prompt_str, answer_str, use_fast)
_check_template(TINY_LLAMA4, "llama4", prompt_str, answer_str)
@pytest.mark.parametrize(
"use_fast",
[
pytest.param(True, marks=pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")),
pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken.")),
],
)
@pytest.mark.runs_on(["cpu", "mps"])
def test_phi4_template(use_fast: bool):
def test_phi4_template():
prompt_str = (
f"<|im_start|>user<|im_sep|>{MESSAGES[0]['content']}<|im_end|>"
f"<|im_start|>assistant<|im_sep|>{MESSAGES[1]['content']}<|im_end|>"
@@ -293,13 +277,12 @@ def test_phi4_template(use_fast: bool):
"<|im_start|>assistant<|im_sep|>"
)
answer_str = f"{MESSAGES[3]['content']}<|im_end|>"
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_qwen2_5_template(use_fast: bool):
def test_qwen2_5_template():
prompt_str = (
"<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
@@ -308,13 +291,12 @@ def test_qwen2_5_template(use_fast: bool):
"<|im_start|>assistant\n"
)
answer_str = f"{MESSAGES[3]['content']}<|im_end|>\n"
_check_template("Qwen/Qwen2.5-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
_check_template("Qwen/Qwen2.5-7B-Instruct", "qwen", prompt_str, answer_str)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
def test_qwen3_template(use_fast: bool, cot_messages: bool):
def test_qwen3_template(cot_messages: bool):
prompt_str = (
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
@@ -328,12 +310,12 @@ def test_qwen3_template(use_fast: bool, cot_messages: bool):
answer_str = f"{MESSAGES_WITH_THOUGHT[3]['content']}<|im_end|>\n"
messages = MESSAGES_WITH_THOUGHT
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, use_fast, messages=messages)
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, messages=messages)
@pytest.mark.runs_on(["cpu", "mps"])
def test_parse_llama3_template():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, token=HF_TOKEN)
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = parse_template(tokenizer)
assert template.format_user.slots == [
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
@@ -348,7 +330,7 @@ def test_parse_llama3_template():
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
def test_parse_qwen_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
template = parse_template(tokenizer)
assert template.__class__.__name__ == "Template"
assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]
@@ -361,7 +343,7 @@ def test_parse_qwen_template():
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
def test_parse_qwen3_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", token=HF_TOKEN)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
template = parse_template(tokenizer)
assert template.__class__.__name__ == "ReasoningTemplate"
assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]

14
tests/model/model_utils/test_visual.py
View File

@@ -16,7 +16,8 @@ import os
import pytest
import torch
from transformers import AutoConfig, AutoModelForVision2Seq
from safetensors.torch import load_file
from transformers import AutoConfig, AutoModelForImageTextToText
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import FinetuningArguments, ModelArguments
@@ -36,7 +37,7 @@ def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bo
)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = AutoModelForImageTextToText.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
for name, param in model.named_parameters():
@@ -56,7 +57,7 @@ def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = AutoModelForImageTextToText.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
trainable_params, frozen_params = set(), set()
@@ -86,13 +87,14 @@ def test_visual_model_save_load():
finetuning_args = FinetuningArguments(finetuning_type="full")
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = AutoModelForImageTextToText.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=False)
model.to_empty(device="cpu")
loaded_model_weight = dict(model.named_parameters())
model.save_pretrained(os.path.join("output", "qwen2_vl"), max_shard_size="10GB", safe_serialization=False)
saved_model_weight = torch.load(os.path.join("output", "qwen2_vl", "pytorch_model.bin"), weights_only=False)
model.save_pretrained(os.path.join("output", "qwen2_vl"), max_shard_size="10GB", safe_serialization=True)
saved_model_weight = load_file(os.path.join("output", "qwen2_vl", "model.safetensors"))
if is_transformers_version_greater_than("4.52.0"):
assert "model.language_model.layers.0.self_attn.q_proj.weight" in loaded_model_weight

2
tests/version.txt
View File

@@ -1,2 +1,2 @@
# change if test fails or cache is outdated
0.9.5.105
0.9.5.106

29
tests_v1/core/utils/test_rendering.py
View File

@@ -23,6 +23,13 @@ from llamafactory.v1.core.utils.rendering import Renderer
from llamafactory.v1.utils.types import Processor
def _get_input_ids(inputs: list | dict) -> list:
if not isinstance(inputs, list):
return inputs["input_ids"]
else:
return inputs
HF_MESSAGES = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is LLM?"},
@@ -81,15 +88,15 @@ def test_chatml_rendering():
tokenizer: Processor = AutoTokenizer.from_pretrained("llamafactory/tiny-random-qwen3")
renderer = Renderer(template="chatml", processor=tokenizer)
hf_inputs = tokenizer.apply_chat_template(HF_MESSAGES[:-1], add_generation_prompt=True)
hf_inputs = _get_input_ids(tokenizer.apply_chat_template(HF_MESSAGES[:-1], add_generation_prompt=True))
v1_inputs = renderer.render_messages(V1_MESSAGES[:-1], is_generate=True)
assert v1_inputs["input_ids"] == hf_inputs
assert v1_inputs["attention_mask"] == [1] * len(hf_inputs)
assert v1_inputs["labels"] == [-100] * len(hf_inputs)
assert v1_inputs["loss_weights"] == [0.0] * len(hf_inputs)
hf_inputs_part = tokenizer.apply_chat_template(HF_MESSAGES[:-1], add_generation_prompt=False)
hf_inputs_full = tokenizer.apply_chat_template(HF_MESSAGES, add_generation_prompt=False)
hf_inputs_part = _get_input_ids(tokenizer.apply_chat_template(HF_MESSAGES[:-1], add_generation_prompt=False))
hf_inputs_full = _get_input_ids(tokenizer.apply_chat_template(HF_MESSAGES, add_generation_prompt=False))
v1_inputs_full = renderer.render_messages(V1_MESSAGES, is_generate=False)
assert v1_inputs_full["input_ids"] == hf_inputs_full
assert v1_inputs_full["attention_mask"] == [1] * len(hf_inputs_full)
@@ -124,17 +131,21 @@ def test_qwen3_nothink_rendering():
tokenizer: Processor = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
renderer = Renderer(template="qwen3_nothink", processor=tokenizer)
hf_inputs = tokenizer.apply_chat_template(HF_MESSAGES_WITH_TOOLS[:-1], tools=V1_TOOLS, add_generation_prompt=True)
hf_inputs = _get_input_ids(
tokenizer.apply_chat_template(HF_MESSAGES_WITH_TOOLS[:-1], tools=V1_TOOLS, add_generation_prompt=True)
)
v1_inputs = renderer.render_messages(V1_MESSAGES_WITH_TOOLS[:-1], tools=json.dumps(V1_TOOLS), is_generate=True)
assert v1_inputs["input_ids"] == hf_inputs
assert v1_inputs["attention_mask"] == [1] * len(hf_inputs)
assert v1_inputs["labels"] == [-100] * len(hf_inputs)
assert v1_inputs["loss_weights"] == [0.0] * len(hf_inputs)
hf_inputs_part = tokenizer.apply_chat_template(
HF_MESSAGES_WITH_TOOLS[:-1], tools=V1_TOOLS, add_generation_prompt=False
hf_inputs_part = _get_input_ids(
tokenizer.apply_chat_template(HF_MESSAGES_WITH_TOOLS[:-1], tools=V1_TOOLS, add_generation_prompt=False)
)
hf_inputs_full = _get_input_ids(
tokenizer.apply_chat_template(HF_MESSAGES_WITH_TOOLS, tools=V1_TOOLS, add_generation_prompt=False)
)
hf_inputs_full = tokenizer.apply_chat_template(HF_MESSAGES_WITH_TOOLS, tools=V1_TOOLS, add_generation_prompt=False)
v1_inputs_full = renderer.render_messages(V1_MESSAGES_WITH_TOOLS, tools=json.dumps(V1_TOOLS), is_generate=False)
assert v1_inputs_full["input_ids"] == hf_inputs_full
assert v1_inputs_full["attention_mask"] == [1] * len(hf_inputs_full)
@@ -187,7 +198,7 @@ def test_qwen3_nothink_rendering_remote(num_samples: int):
def test_process_sft_samples():
tokenizer: Processor = AutoTokenizer.from_pretrained("llamafactory/tiny-random-qwen3")
renderer = Renderer(template="chatml", processor=tokenizer)
hf_inputs = tokenizer.apply_chat_template(HF_MESSAGES)
hf_inputs = _get_input_ids(tokenizer.apply_chat_template(HF_MESSAGES))
samples = [{"messages": V1_MESSAGES, "extra_info": "test", "_dataset_name": "default"}]
model_inputs = renderer.process_samples(samples)
@@ -200,7 +211,7 @@ def test_process_sft_samples():
def test_process_dpo_samples():
tokenizer: Processor = AutoTokenizer.from_pretrained("llamafactory/tiny-random-qwen3")
renderer = Renderer(template="chatml", processor=tokenizer)
hf_inputs = tokenizer.apply_chat_template(HF_MESSAGES)
hf_inputs = _get_input_ids(tokenizer.apply_chat_template(HF_MESSAGES))
samples = [
{