2 Commits

Author SHA1 Message Date
  Hertz 9fd4b094d4
[model] support VibeThinker models (#9616) 2 days ago
  浮梦 18c21bce5a
[test] add allreduce test on npu (#9619) 2 days ago
23 changed files with 432 additions and 70 deletions
Split View
  1. +1
    -1
      Makefile
  2. +1
    -0
      README.md
  3. +1
    -0
      README_zh.md
  4. +11
    -0
      src/llamafactory/extras/constants.py
  5. +2
    -4
      src/llamafactory/train/test_utils.py
  6. +30
    -0
      src/llamafactory/v1/accelerator/helper.py
  7. +34
    -0
      src/llamafactory/v1/utils/utils.py
  8. +97
    -32
      tests/conftest.py
  9. +1
    -1
      tests/data/processor/test_feedback.py
  10. +1
    -1
      tests/model/model_utils/test_add_tokens.py
  11. +1
    -1
      tests/model/model_utils/test_attention.py
  12. +4
    -4
      tests/model/model_utils/test_checkpointing.py
  13. +1
    -1
      tests/model/model_utils/test_misc.py
  14. +1
    -1
      tests/model/model_utils/test_packing.py
  15. +3
    -3
      tests/model/model_utils/test_visual.py
  16. +1
    -1
      tests/model/test_base.py
  17. +3
    -3
      tests/model/test_freeze.py
  18. +2
    -2
      tests/model/test_full.py
  19. +7
    -7
      tests/model/test_lora.py
  20. +2
    -2
      tests/model/test_pissa.py
  21. +1
    -1
      tests/train/test_sft_trainer.py
  22. +93
    -0
      tests_v1/accelerator/test_allreduce.py
  23. +134
    -5
      tests_v1/conftest.py

+ 1
- 1
Makefile View File

@@ -21,4 +21,4 @@ style:
ruff format $(check_dirs)

test:
CUDA_VISIBLE_DEVICES= ASCEND_RT_VISIBLE_DEVICES=0 WANDB_DISABLED=true pytest -vv --import-mode=importlib tests/ tests_v1/
WANDB_DISABLED=true pytest -vv --import-mode=importlib tests/ tests_v1/

+ 1
- 0
README.md View File

@@ -336,6 +336,7 @@ Read technical notes:
| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [TeleChat2](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |
| [VibeThinker-1.5B](https://huggingface.co/WeiboAI) | 1.5B | qwen3 |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |


+ 1
- 0
README_zh.md View File

@@ -338,6 +338,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [TeleChat2](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |
| [VibeThinker-1.5B](https://huggingface.co/WeiboAI) | 1.5B | qwen3 |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |


+ 11
- 0
src/llamafactory/extras/constants.py View File

@@ -3520,6 +3520,17 @@ register_model_group(
)


register_model_group(
models={
"VibeThinker-1.5B": {
DownloadSource.DEFAULT: "WeiboAI/VibeThinker-1.5B",
DownloadSource.MODELSCOPE: "WeiboAI/VibeThinker-1.5B",
},
},
template="qwen3",
)


register_model_group(
models={
"Vicuna-v1.5-7B-Chat": {


+ 2
- 4
src/llamafactory/train/test_utils.py View File

@@ -20,7 +20,6 @@ from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead

from ..data import get_dataset, get_template_and_fix_tokenizer
from ..extras.misc import get_current_device
from ..hparams import get_infer_args, get_train_args
from ..model import load_model, load_tokenizer

@@ -81,17 +80,16 @@ def load_reference_model(
is_trainable: bool = False,
add_valuehead: bool = False,
) -> Union["PreTrainedModel", "LoraModel"]:
current_device = get_current_device()
if add_valuehead:
model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
model_path, torch_dtype=torch.float16, device_map=current_device
model_path, torch_dtype=torch.float16, device_map="auto"
)
if not is_trainable:
model.v_head = model.v_head.to(torch.float16)

return model

model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")
if use_lora or use_pissa:
model = PeftModel.from_pretrained(
model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable


+ 30
- 0
src/llamafactory/v1/accelerator/helper.py View File

@@ -103,6 +103,36 @@ def is_torch_xpu_available():
return get_current_accelerator().type == DeviceType.XPU


def get_current_device() -> "torch.device":
r"""Get the current available device."""
if is_torch_xpu_available():
device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_npu_available():
device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_mps_available():
device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_cuda_available():
device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
else:
device = "cpu"

return torch.device(device)


def get_device_count() -> int:
r"""Get the number of available devices."""
if is_torch_xpu_available():
return torch.xpu.device_count()
elif is_torch_npu_available():
return torch.npu.device_count()
elif is_torch_mps_available():
return torch.mps.device_count()
elif is_torch_cuda_available():
return torch.cuda.device_count()
else:
return 0


def all_gather(tensor: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
"""Gathers the tensor from all ranks and concats them along the first dim."""
world_size = get_world_size()


+ 34
- 0
src/llamafactory/v1/utils/utils.py View File

@@ -0,0 +1,34 @@
# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import socket


def find_available_port() -> int:
r"""Find an available port on the local machine."""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("", 0))
port = sock.getsockname()[1]
sock.close()
return port


def is_env_enabled(env_var: str, default: str = "0") -> bool:
r"""Check if the environment variable is enabled."""
return os.getenv(env_var, default).lower() in ["true", "y", "1"]


if __name__ == "__main__":
print(find_available_port())

+ 97
- 32
tests/conftest.py View File

@@ -17,15 +17,18 @@
Contains shared fixtures, pytest configuration, and custom markers.
"""

import os

import pytest
from pytest import Config, Item

from llamafactory.extras.misc import get_current_device, is_env_enabled
from llamafactory.extras.misc import get_current_device, get_device_count, is_env_enabled
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import patch_valuehead_model


try:
CURRENT_DEVICE = get_current_device().type
CURRENT_DEVICE = get_current_device().type # cpu | cuda | npu
except Exception:
CURRENT_DEVICE = "cpu"

@@ -33,46 +36,36 @@ except Exception:
def pytest_configure(config: Config):
"""Register custom pytest markers."""
config.addinivalue_line(
"markers", "slow: marks tests as slow (deselect with '-m \"not slow\"' or set RUN_SLOW=1 to run)"
"markers",
"slow: marks tests as slow (deselect with '-m \"not slow\"' or set RUN_SLOW=1 to run)",
)
config.addinivalue_line(
"markers",
"runs_on: test requires specific device type, e.g., @pytest.mark.runs_on(['cuda'])",
)
config.addinivalue_line(
"markers",
"require_distributed(num_devices): allow multi-device execution (default: 2)",
)
config.addinivalue_line("markers", "runs_on: test requires specific device, e.g., @pytest.mark.runs_on(['cpu'])")


def _handle_runs_on(items: list[Item]):
"""Skip tests on specified devices based on runs_on marker.

Usage:
# Skip tests on specified devices
@pytest.mark.runs_on(['cpu'])
def test_something():
pass
"""
"""Skip tests on specified device TYPES (cpu/cuda/npu)."""
for item in items:
runs_on_marker = item.get_closest_marker("runs_on")
if runs_on_marker:
runs_on_devices = runs_on_marker.args[0]
marker = item.get_closest_marker("runs_on")
if not marker:
continue

# Compatibility handling: Allow a single string instead of a list
# Example: @pytest.mark.("cpu")
if isinstance(runs_on_devices, str):
runs_on_devices = [runs_on_devices]
devices = marker.args[0]
if isinstance(devices, str):
devices = [devices]

if CURRENT_DEVICE not in runs_on_devices:
item.add_marker(
pytest.mark.skip(reason=f"test requires one of {runs_on_devices} (current: {CURRENT_DEVICE})")
)
if CURRENT_DEVICE not in devices:
item.add_marker(pytest.mark.skip(reason=f"test requires one of {devices} (current: {CURRENT_DEVICE})"))


def _handle_slow_tests(items: list[Item]):
"""Skip slow tests unless RUN_SLOW environment variable is set.

Usage:
# Skip slow tests (default)
@pytest.mark.slow

# Run slow tests
RUN_SLOW=1 pytest tests/
"""
"""Skip slow tests unless RUN_SLOW is enabled."""
if not is_env_enabled("RUN_SLOW", "0"):
skip_slow = pytest.mark.skip(reason="slow test (set RUN_SLOW=1 to run)")
for item in items:
@@ -80,10 +73,82 @@ def _handle_slow_tests(items: list[Item]):
item.add_marker(skip_slow)


def _get_visible_devices_env():
"""Return device visibility env var name."""
if CURRENT_DEVICE == "cuda":
return "CUDA_VISIBLE_DEVICES"
if CURRENT_DEVICE == "npu":
return "ASCEND_RT_VISIBLE_DEVICES"
return None


def _handle_device_visibility(items: list[Item]):
"""Handle device visibility based on test markers."""
env_key = _get_visible_devices_env()
if env_key is None or CURRENT_DEVICE == "cpu":
return

# Parse visible devices
visible_devices_env = os.environ.get(env_key)
if visible_devices_env is None:
available = get_device_count()
else:
visible_devices = [v for v in visible_devices_env.split(",") if v != ""]
available = len(visible_devices)

for item in items:
marker = item.get_closest_marker("require_distributed")
if not marker:
continue

required = marker.args[0] if marker.args else 2
if available < required:
item.add_marker(pytest.mark.skip(reason=f"test requires {required} devices, but only {available} visible"))


def pytest_collection_modifyitems(config: Config, items: list[Item]):
"""Modify test collection based on markers and environment."""
# Handle version compatibility (from HEAD)
if not is_transformers_version_greater_than("4.57.0"):
skip_bc = pytest.mark.skip(reason="Skip backward compatibility tests")
for item in items:
if "tests_v1" in str(item.fspath):
item.add_marker(skip_bc)

_handle_slow_tests(items)
_handle_runs_on(items)
_handle_device_visibility(items)


@pytest.fixture(autouse=True)
def _manage_distributed_env(request, monkeypatch):
"""Set environment variables for distributed tests if specific devices are requested."""
env_key = _get_visible_devices_env()
if not env_key:
return

# Save old environment for logic checks, monkeypatch handles restoration
old_value = os.environ.get(env_key)

marker = request.node.get_closest_marker("require_distributed")
if marker:
# Distributed test
required = marker.args[0] if marker.args else 2
specific_devices = marker.args[1] if len(marker.args) > 1 else None

if specific_devices:
devices_str = ",".join(map(str, specific_devices))
else:
devices_str = ",".join(str(i) for i in range(required))

monkeypatch.setenv(env_key, devices_str)
else:
# Non-distributed test
if old_value:
visible_devices = [v for v in old_value.split(",") if v != ""]
monkeypatch.setenv(env_key, visible_devices[0] if visible_devices else "0")
else:
monkeypatch.setenv(env_key, "0")


@pytest.fixture


+ 1
- 1
tests/data/processor/test_feedback.py View File

@@ -42,7 +42,7 @@ TRAIN_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("num_samples", [16])
def test_feedback_data(num_samples: int):
train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]


+ 1
- 1
tests/model/model_utils/test_add_tokens.py View File

@@ -25,7 +25,7 @@ TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
UNUSED_TOKEN = "<|UNUSED_TOKEN|>"


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("special_tokens", [False, True])
def test_add_tokens(special_tokens: bool):
if special_tokens:


+ 1
- 1
tests/model/model_utils/test_attention.py View File

@@ -39,7 +39,7 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.xfail(is_transformers_version_greater_than("4.48"), reason="Attention refactor.")
def test_attention():
attention_available = ["disabled"]


+ 4
- 4
tests/model/model_utils/test_checkpointing.py View File

@@ -39,7 +39,7 @@ TRAIN_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("disable_gradient_checkpointing", [False, True])
def test_vanilla_checkpointing(disable_gradient_checkpointing: bool):
model = load_train_model(disable_gradient_checkpointing=disable_gradient_checkpointing, **TRAIN_ARGS)
@@ -47,14 +47,14 @@ def test_vanilla_checkpointing(disable_gradient_checkpointing: bool):
assert getattr(module, "gradient_checkpointing") != disable_gradient_checkpointing


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_unsloth_gradient_checkpointing():
model = load_train_model(use_unsloth_gc=True, **TRAIN_ARGS)
for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
assert module._gradient_checkpointing_func.__self__.__name__ == "UnslothGradientCheckpointing"


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_upcast_layernorm():
model = load_train_model(upcast_layernorm=True, **TRAIN_ARGS)
for name, param in model.named_parameters():
@@ -62,7 +62,7 @@ def test_upcast_layernorm():
assert param.dtype == torch.float32


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_upcast_lmhead_output():
model = load_train_model(upcast_lmhead_output=True, **TRAIN_ARGS)
inputs = torch.randn((1, 16), dtype=torch.float16, device=get_current_device())


+ 1
- 1
tests/model/model_utils/test_misc.py View File

@@ -24,7 +24,7 @@ from llamafactory.model.model_utils.misc import find_expanded_modules
HF_TOKEN = os.getenv("HF_TOKEN")


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
def test_expanded_modules():
config = AutoConfig.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")


+ 1
- 1
tests/model/model_utils/test_packing.py View File

@@ -18,7 +18,7 @@ import torch
from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize(
"attention_mask,golden_seq_lens",
[


+ 3
- 3
tests/model/model_utils/test_visual.py View File

@@ -23,7 +23,7 @@ from llamafactory.hparams import FinetuningArguments, ModelArguments
from llamafactory.model.adapter import init_adapter


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("freeze_vision_tower", (False, True))
@pytest.mark.parametrize("freeze_multi_modal_projector", (False, True))
@pytest.mark.parametrize("freeze_language_model", (False, True))
@@ -49,7 +49,7 @@ def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bo
assert param.requires_grad != freeze_language_model


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("freeze_vision_tower,freeze_language_model", ((False, False), (False, True), (True, False)))
def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
@@ -82,7 +82,7 @@ def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
assert (merger_param_name in trainable_params) is False


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_visual_model_save_load():
# check VLM's state dict: https://github.com/huggingface/transformers/pull/38385
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")


+ 1
- 1
tests/model/test_base.py View File

@@ -30,7 +30,7 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_base():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA3)


+ 3
- 3
tests/model/test_freeze.py View File

@@ -44,7 +44,7 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_freeze_train_all_modules():
model = load_train_model(freeze_trainable_layers=1, **TRAIN_ARGS)
for name, param in model.named_parameters():
@@ -56,7 +56,7 @@ def test_freeze_train_all_modules():
assert param.dtype == torch.float16


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_freeze_train_extra_modules():
model = load_train_model(freeze_trainable_layers=1, freeze_extra_modules="embed_tokens,lm_head", **TRAIN_ARGS)
for name, param in model.named_parameters():
@@ -68,7 +68,7 @@ def test_freeze_train_extra_modules():
assert param.dtype == torch.float16


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_freeze_inference():
model = load_infer_model(**INFER_ARGS)
for param in model.parameters():


+ 2
- 2
tests/model/test_full.py View File

@@ -44,7 +44,7 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_full_train():
model = load_train_model(**TRAIN_ARGS)
for param in model.parameters():
@@ -52,7 +52,7 @@ def test_full_train():
assert param.dtype == torch.float32


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_full_inference():
model = load_infer_model(**INFER_ARGS)
for param in model.parameters():


+ 7
- 7
tests/model/test_lora.py View File

@@ -55,35 +55,35 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_train_qv_modules():
model = load_train_model(lora_target="q_proj,v_proj", **TRAIN_ARGS)
linear_modules, _ = check_lora_model(model)
assert linear_modules == {"q_proj", "v_proj"}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_train_all_modules():
model = load_train_model(lora_target="all", **TRAIN_ARGS)
linear_modules, _ = check_lora_model(model)
assert linear_modules == {"q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_train_extra_modules():
model = load_train_model(additional_target="embed_tokens,lm_head", **TRAIN_ARGS)
_, extra_modules = check_lora_model(model)
assert extra_modules == {"embed_tokens", "lm_head"}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_train_old_adapters():
model = load_train_model(adapter_name_or_path=TINY_LLAMA_ADAPTER, create_new_adapter=False, **TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True, is_trainable=True)
compare_model(model, ref_model)


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_train_new_adapters():
model = load_train_model(adapter_name_or_path=TINY_LLAMA_ADAPTER, create_new_adapter=True, **TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True, is_trainable=True)
@@ -92,7 +92,7 @@ def test_lora_train_new_adapters():
)


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
def test_lora_train_valuehead():
model = load_train_model(add_valuehead=True, **TRAIN_ARGS)
@@ -103,7 +103,7 @@ def test_lora_train_valuehead():
assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
def test_lora_inference():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True).merge_and_unload()


+ 2
- 2
tests/model/test_pissa.py View File

@@ -49,7 +49,7 @@ INFER_ARGS = {
}


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.xfail(reason="PiSSA initialization is not stable in different platform.")
def test_pissa_train():
model = load_train_model(**TRAIN_ARGS)
@@ -57,7 +57,7 @@ def test_pissa_train():
compare_model(model, ref_model)


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.xfail(reason="Known connection error.")
def test_pissa_inference():
model = load_infer_model(**INFER_ARGS)


+ 1
- 1
tests/train/test_sft_trainer.py View File

@@ -59,7 +59,7 @@ class DataCollatorWithVerbose(DataCollatorWithPadding):
return {k: v[:, :1] for k, v in batch.items()} # truncate input length


@pytest.mark.runs_on(["cpu", "npu"])
@pytest.mark.runs_on(["cpu", "npu", "cuda"])
@pytest.mark.parametrize("disable_shuffling", [False, True])
def test_shuffle(disable_shuffling: bool):
model_args, data_args, training_args, finetuning_args, _ = get_train_args(


+ 93
- 0
tests_v1/accelerator/test_allreduce.py View File

@@ -0,0 +1,93 @@
# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp

from llamafactory.v1.accelerator.helper import ReduceOp, all_reduce, is_torch_cuda_available, is_torch_npu_available
from llamafactory.v1.utils.utils import find_available_port


def _dist_worker(rank, world_size):
if is_torch_cuda_available():
backend = "nccl"
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(rank)
elif is_torch_npu_available():
backend = "hccl"
device = torch.device(f"npu:{rank}")
torch.npu.set_device(rank)
else:
backend = "gloo"
device = torch.device("cpu")

dist.init_process_group(
backend=backend,
rank=rank,
world_size=world_size,
)

# --------------------
# Test all_reduce SUM
# --------------------
y = torch.tensor(rank + 1.0, device=device)
y_sum = all_reduce(y.clone(), op=ReduceOp.SUM)
assert y_sum.item() == 3.0

# --------------------
# Test all_reduce MEAN
# --------------------
y_mean = all_reduce(y.clone(), op=ReduceOp.MEAN)
assert y_mean.item() == pytest.approx(1.5)

# --------------------
# Test all_reduce MAX
# --------------------
y_max = all_reduce(y.clone(), op=ReduceOp.MAX)
assert y_max.item() == 2.0

dist.destroy_process_group()


@pytest.mark.runs_on(["npu", "cuda"])
@pytest.mark.require_distributed(2)
def test_distributed_ops(monkeypatch):
monkeypatch.setenv("MASTER_ADDR", "127.0.0.1")
monkeypatch.setenv("MASTER_PORT", str(find_available_port()))
WORLD_SIZE = 2
mp.spawn(
_dist_worker,
args=(WORLD_SIZE,),
nprocs=WORLD_SIZE,
join=True,
)


@pytest.mark.runs_on(["npu", "cuda"])
@pytest.mark.require_distributed(4)
def test_required_multi():
# test require_distributed mark ok
pass


@pytest.mark.runs_on(["npu", "cuda"])
@pytest.mark.require_distributed(999)
def test_required_invalid():
# test require_distributed mark not ok,
raise RuntimeError(
"this case should not be run, please check whether the require_distributed mark implementation is correct"
)

+ 134
- 5
tests_v1/conftest.py View File

@@ -12,18 +12,147 @@
# See the License for the specific language governing permissions and
# limitations under the License.

"""LLaMA-Factory test configuration.

Contains shared fixtures, pytest configuration, and custom markers.
"""

import os

import pytest
from pytest import Config, Item

from llamafactory.train.test_utils import patch_valuehead_model
from llamafactory.v1.accelerator.helper import get_current_device, get_device_count
from llamafactory.v1.utils.packages import is_transformers_version_greater_than
from llamafactory.v1.utils.utils import is_env_enabled


def pytest_collection_modifyitems(config: Config, items: list[Item]):
if is_transformers_version_greater_than("4.57.0"):
try:
CURRENT_DEVICE = get_current_device().type # cpu | cuda | npu
except Exception:
CURRENT_DEVICE = "cpu"


def pytest_configure(config: Config):
"""Register custom pytest markers."""
config.addinivalue_line(
"markers",
"slow: marks tests as slow (deselect with '-m \"not slow\"' or set RUN_SLOW=1 to run)",
)
config.addinivalue_line(
"markers",
"runs_on: test requires specific device type, e.g., @pytest.mark.runs_on(['cuda'])",
)
config.addinivalue_line(
"markers",
"require_distributed(num_devices): allow multi-device execution (default: 2)",
)


def _handle_runs_on(items: list[Item]):
"""Skip tests on specified device TYPES (cpu/cuda/npu)."""
for item in items:
marker = item.get_closest_marker("runs_on")
if not marker:
continue

devices = marker.args[0]
if isinstance(devices, str):
devices = [devices]

if CURRENT_DEVICE not in devices:
item.add_marker(pytest.mark.skip(reason=f"test requires one of {devices} (current: {CURRENT_DEVICE})"))


def _handle_slow_tests(items: list[Item]):
"""Skip slow tests unless RUN_SLOW is enabled."""
if not is_env_enabled("RUN_SLOW", "0"):
skip_slow = pytest.mark.skip(reason="slow test (set RUN_SLOW=1 to run)")
for item in items:
if "slow" in item.keywords:
item.add_marker(skip_slow)


def _get_visible_devices_env():
"""Return device visibility env var name."""
if CURRENT_DEVICE == "cuda":
return "CUDA_VISIBLE_DEVICES"
if CURRENT_DEVICE == "npu":
return "ASCEND_RT_VISIBLE_DEVICES"
return None


def _handle_device_visibility(items: list[Item]):
"""Handle device visibility based on test markers."""
env_key = _get_visible_devices_env()
if env_key is None or CURRENT_DEVICE == "cpu":
return

skip_bc = pytest.mark.skip(reason="Skip backward compatibility tests")
# Parse visible devices
visible_devices_env = os.environ.get(env_key)
if visible_devices_env is None:
available = get_device_count()
else:
visible_devices = [v for v in visible_devices_env.split(",") if v != ""]
available = len(visible_devices)

for item in items:
if "tests_v1" in str(item.fspath):
item.add_marker(skip_bc)
marker = item.get_closest_marker("require_distributed")
if not marker:
continue

required = marker.args[0] if marker.args else 2
if available < required:
item.add_marker(pytest.mark.skip(reason=f"test requires {required} devices, but only {available} visible"))


def pytest_collection_modifyitems(config: Config, items: list[Item]):
"""Modify test collection based on markers and environment."""
# Handle version compatibility (from HEAD)
if not is_transformers_version_greater_than("4.57.0"):
skip_bc = pytest.mark.skip(reason="Skip backward compatibility tests")
for item in items:
if "tests_v1" in str(item.fspath):
item.add_marker(skip_bc)

_handle_slow_tests(items)
_handle_runs_on(items)
_handle_device_visibility(items)


@pytest.fixture(autouse=True)
def _manage_distributed_env(request, monkeypatch):
"""Set environment variables for distributed tests if specific devices are requested."""
env_key = _get_visible_devices_env()
if not env_key:
return

# Save old environment for logic checks, monkeypatch handles restoration
old_value = os.environ.get(env_key)

marker = request.node.get_closest_marker("require_distributed")
if marker:
# Distributed test
required = marker.args[0] if marker.args else 2
specific_devices = marker.args[1] if len(marker.args) > 1 else None

if specific_devices:
devices_str = ",".join(map(str, specific_devices))
else:
devices_str = ",".join(str(i) for i in range(required))

monkeypatch.setenv(env_key, devices_str)
else:
# Non-distributed test
if old_value:
visible_devices = [v for v in old_value.split(",") if v != ""]
monkeypatch.setenv(env_key, visible_devices[0] if visible_devices else "0")
else:
monkeypatch.setenv(env_key, "0")


@pytest.fixture
def fix_valuehead_cpu_loading():
"""Fix valuehead model loading."""
patch_valuehead_model()

Loading…
Cancel
Save
Baidu
map