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[deps] upgrade transformers (#8159)

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hoshi-hiyouga 2025-05-26 22:03:58 +08:00 committed by GitHub
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5 changed files with 44 additions and 32 deletions
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4
requirements.txt
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@ -1,10 +1,10 @@
transformers>=4.45.0,<=4.52.1,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0
transformers>=4.45.0,<=4.52.3,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0
datasets>=2.16.0,<=3.6.0
accelerate>=0.34.0,<=1.7.0
peft>=0.14.0,<=0.15.2
trl>=0.8.6,<=0.9.6
tokenizers>=0.19.0,<=0.21.1
gradio>=4.38.0,<=5.30.0
gradio>=4.38.0,<=5.31.0
scipy
einops
sentencepiece

2
src/llamafactory/extras/misc.py
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@ -95,7 +95,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version(
"transformers>=4.45.0,<=4.52.1,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0,!=4.52.0"
"transformers>=4.45.0,<=4.52.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0,!=4.52.0"
)
check_version("datasets>=2.16.0,<=3.6.0")
check_version("accelerate>=0.34.0,<=1.7.0")

2
src/llamafactory/webui/common.py
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@ -163,7 +163,7 @@ def save_args(config_path: str, config_dict: dict[str, Any]) -> None:
def _clean_cmd(args: dict[str, Any]) -> dict[str, Any]:
r"""Remove args with NoneType or False or empty string value."""
no_skip_keys = ["packing"]
no_skip_keys = ["packing", "freeze_vision_tower", "freeze_multi_modal_projector", "freeze_language_model"]
return {k: v for k, v in args.items() if (k in no_skip_keys) or (v is not None and v is not False and v != "")}

20
src/llamafactory/webui/runner.py
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@ -22,14 +22,13 @@ from typing import TYPE_CHECKING, Any, Optional
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.utils import is_torch_npu_available
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
from ..extras.constants import LLAMABOARD_CONFIG, MULTIMODAL_SUPPORTED_MODELS, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_accelerator_available, torch_gc, use_ray
from ..extras.packages import is_gradio_available
from .common import (
DEFAULT_CACHE_DIR,
DEFAULT_CONFIG_DIR,
abort_process,
calculate_pixels,
gen_cmd,
get_save_dir,
load_args,
@ -165,13 +164,6 @@ class Runner:
use_llama_pro=get("train.use_llama_pro"),
enable_thinking=get("train.enable_thinking"),
report_to=get("train.report_to"),
freeze_vision_tower=get("train.freeze_vision_tower"),
freeze_multi_modal_projector=get("train.freeze_multi_modal_projector"),
freeze_language_model=get("train.freeze_language_model"),
image_max_pixels=calculate_pixels(get("train.image_max_pixels")),
image_min_pixels=calculate_pixels(get("train.image_min_pixels")),
video_max_pixels=calculate_pixels(get("train.video_max_pixels")),
video_min_pixels=calculate_pixels(get("train.video_min_pixels")),
use_galore=get("train.use_galore"),
use_apollo=get("train.use_apollo"),
use_badam=get("train.use_badam"),
@ -244,6 +236,16 @@ class Runner:
args["pref_ftx"] = get("train.pref_ftx")
args["pref_loss"] = get("train.pref_loss")
# multimodal config
if model_name in MULTIMODAL_SUPPORTED_MODELS:
args["freeze_vision_tower"] = get("train.freeze_vision_tower")
args["freeze_multi_modal_projector"] = get("train.freeze_multi_modal_projector")
args["freeze_language_model"] = get("train.freeze_language_model")
args["image_max_pixels"] = get("train.image_max_pixels")
args["image_min_pixels"] = get("train.image_min_pixels")
args["video_max_pixels"] = get("train.video_max_pixels")
args["video_min_pixels"] = get("train.video_min_pixels")
# galore config
if args["use_galore"]:
args["galore_rank"] = get("train.galore_rank")

48
tests/data/test_template.py
View File

@ -129,23 +129,24 @@ def test_encode_multiturn(use_fast: bool):
@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):
input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
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, input_messages)
output_messages = MESSAGES if enable_thinking is False else input_messages
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
prompt_str = (
f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
)
answer_str = f"{output_messages[3]['content']}<|im_end|>\n"
if not cot_messages or enable_thinking is False:
answer_str = f"{MESSAGES[3]['content']}<|im_end|>\n"
if enable_thinking:
answer_str = "<think>\n\n</think>\n\n" + answer_str
else:
prompt_str = prompt_str + "<think>\n\n</think>\n\n"
else:
answer_str = f"{MESSAGES_WITH_THOUGHT[3]['content']}<|im_end|>\n"
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@ -154,16 +155,16 @@ def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thi
@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):
input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
encoded_pairs = template.encode_multiturn(tokenizer, input_messages)
output_messages = MESSAGES if enable_thinking is False else input_messages
prompt_str_1 = f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_1 = f"{output_messages[1]['content']}<|im_end|>\n"
prompt_str_2 = f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_2 = f"{output_messages[3]['content']}<|im_end|>\n"
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
messages = MESSAGES if not cot_messages or enable_thinking is False else MESSAGES_WITH_THOUGHT
prompt_str_1 = f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_1 = f"{messages[1]['content']}<|im_end|>\n"
prompt_str_2 = f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_2 = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages or enable_thinking is False:
if enable_thinking:
answer_str_1 = "<think>\n\n</think>\n\n" + answer_str_1
@ -253,7 +254,11 @@ def test_llama4_template(use_fast: bool):
@pytest.mark.parametrize(
"use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken."))]
"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.")),
],
)
def test_phi4_template(use_fast: bool):
prompt_str = (
@ -266,6 +271,7 @@ def test_phi4_template(use_fast: bool):
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_qwen2_5_template(use_fast: bool):
prompt_str = (
@ -282,16 +288,18 @@ def test_qwen2_5_template(use_fast: bool):
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
def test_qwen3_template(use_fast: bool, cot_messages: bool):
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
prompt_str = (
f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages:
answer_str = "<think>\n\n</think>\n\n" + answer_str
answer_str = f"<think>\n\n</think>\n\n{MESSAGES[3]['content']}<|im_end|>\n"
messages = MESSAGES
else:
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)
@ -309,6 +317,7 @@ def test_parse_llama3_template():
assert template.default_system == ""
@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)
template = parse_template(tokenizer)
@ -320,6 +329,7 @@ def test_parse_qwen_template():
assert template.default_system == "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
def test_parse_qwen3_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", token=HF_TOKEN)
template = parse_template(tokenizer)