[version] release v0.9.5 (#10532)

Co-authored-by: cxy-thinkbook <xuanyuchen@seu.edu.cn>

README.md

@@ -15,8 +15,6 @@
 
 [![Open in Colab](assets/thirdparty/colab.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
 [![Open in DSW](assets/thirdparty/dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
-[![Open in Lab4ai](assets/thirdparty/lab4ai.svg)](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
-[![Open in Online](assets/thirdparty/online.svg)](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
 [![Open in Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Open in Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Novita](https://img.shields.io/badge/Novita-Deploy%20Template-blue)](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
@@ -38,7 +36,7 @@
 
 </div>
 
-👋 Join our [WeChat](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg), [NPU](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg), [Lab4AI](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg), [LLaMA Factory Online](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.jpg) user group.
+👋 Join our [WeChat](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg) and [NPU](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg) user groups.
 
 \[ English | [中文](README_zh.md) \]
 
@@ -52,14 +50,11 @@ Start local training:
 Start cloud training:
 - **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
 - **PAI-DSW (free trial)**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
-- **LLaMA Factory Online**: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory
-- **Alaya NeW (cloud GPU deal)**: https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
 
 Read technical notes:
 - **Documentation (WIP)**: https://llamafactory.readthedocs.io/en/latest/
 - **Documentation (AMD GPU)**: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
 - **Official Blog**: https://blog.llamafactory.net/en/
-- **Official Course**: https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
 
 > [!NOTE]
 > Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
@@ -78,7 +73,6 @@ Read technical notes:
   - [Data Preparation](#data-preparation)
   - [Quickstart](#quickstart)
   - [Fine-Tuning with LLaMA Board GUI](#fine-tuning-with-llama-board-gui-powered-by-gradio)
-  - [LLaMA Factory Online](#llama-factory-online)
   - [Build Docker](#build-docker)
   - [Deploy with OpenAI-style API and vLLM](#deploy-with-openai-style-api-and-vllm)
   - [Download from ModelScope Hub](#download-from-modelscope-hub)
@@ -117,15 +111,11 @@ Read technical notes:
 
 - 💡 [KTransformers Fine-Tuning × LLaMA Factory: Fine-tuning 1000 Billion models with 2 4090-GPU + CPU](https://blog.llamafactory.net/en/posts/ktransformers/) (English)
 - 💡 [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)
-- [Fine-tune a mental health LLM using LLaMA-Factory](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory) (Chinese)
-- [Fine-tune GPT-OSS for Role-Playing using LLaMA-Factory](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory) (Chinese)
 - [A One-Stop Code-Free Model Reinforcement Learning and Deployment Platform based on LLaMA-Factory and EasyR1](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/) (Chinese)
 - [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
 
 <details><summary>All Blogs</summary>
 
-- [Fine-tune Llama3.1-70B for Medical Diagnosis using LLaMA-Factory](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory) (Chinese)
-- [Fine-tune Qwen2.5-VL for Autonomous Driving using LLaMA-Factory](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory) (Chinese)
 - [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
 - [A One-Stop Code-Free Model Fine-Tuning \& Deployment Platform based on SageMaker and LLaMA-Factory](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/) (Chinese)
 - [LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) (Chinese)
@@ -661,10 +651,6 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
 llamafactory-cli webui
 ```
 
-### LLaMA Factory Online
-
-Read our [documentation](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory).
-
 ### Build Docker
 
 For CUDA users:

README_zh.md

@@ -15,8 +15,6 @@
 
 [![Open in Colab](assets/thirdparty/colab.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
 [![Open in DSW](assets/thirdparty/dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
-[![Open in Lab4ai](assets/thirdparty/lab4ai.svg)](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
-[![Open in Online](assets/thirdparty/online.svg)](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
 [![Open in Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Open in Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Novita](https://img.shields.io/badge/Novita-Deploy%20Template-blue)](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
@@ -38,7 +36,7 @@
 
 </div>
 
-👋 加入我们的[微信群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg)、[NPU 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg)、[大模型实验室群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg) 或 [LLaMA Factory Online 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.png)。
+👋 加入我们的[微信群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg)和 [NPU 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg)。
 
 \[ [English](README.md) | 中文 \]
 
@@ -52,8 +50,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 开始云端训练：
 - **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
 - **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
-- **LLaMA Factory Online（在线微调）**：https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory
-- **九章智算云（算力优惠活动）**：https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
 
 阅读技术文档：
 - **入门教程**：https://zhuanlan.zhihu.com/p/695287607
@@ -61,7 +57,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
 - **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
 - **官方博客**：https://blog.llamafactory.net/
-- **官方课程**：https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
 
 > [!NOTE]
 > 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
@@ -80,7 +75,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
   - [数据准备](#数据准备)
   - [快速开始](#快速开始)
   - [LLaMA Board 可视化微调](#llama-board-可视化微调由-gradio-驱动)
-  - [LLaMA Factory Online 在线微调](#llama-factory-online-在线微调)
   - [构建 Docker](#构建-docker)
   - [利用 vLLM 部署 OpenAI API](#利用-vllm-部署-openai-api)
   - [从魔搭社区下载](#从魔搭社区下载)
@@ -119,15 +113,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 
 - 💡 [KTransformers Fine-Tuning × LLaMA Factory: 用2张4090级的GPU+CPU 微调 1000B规模的超大模型](https://swcil84qspu.feishu.cn/wiki/Z1sSwb2poijybxkyPEkcDG6enVc) (中文)
 - 💡 [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
-- [使用 LLaMA-Factory 微调心理健康大模型](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory)（中文）
-- [使用 LLaMA-Factory 构建 GPT-OSS 角色扮演模型](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory)（中文）
 - [基于 LLaMA-Factory 和 EasyR1 打造一站式无代码大模型强化学习和部署平台 LLM Model Hub](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)（中文）
 - [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
 
 <details><summary>全部博客</summary>
 
-- [使用 LLaMA-Factory 微调 Llama3.1-70B 医学诊断模型](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory)（中文）
-- [使用 LLaMA-Factory 微调 Qwen2.5-VL 实现自动驾驶场景微调](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory)（中文）
 - [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
 - [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
 - [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
@@ -662,10 +652,6 @@ llamafactory-cli export examples/merge_lora/qwen3_lora_sft.yaml
 llamafactory-cli webui
 ```
 
-### LLaMA Factory Online 在线微调
-
-详情阅读该[文档](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory)。
-
 ### 构建 Docker
 
 CUDA 用户：

examples/v1/train_batching_strategy/train_full_fsdp2_batching_normal.yaml

@@ -0,0 +1,31 @@
+model: Qwen/Qwen3-0.6B
+model_class: llm
+
+template: qwen3_nothink
+
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 2
+batching_strategy: normal
+
+cutoff_len: 2048
+learning_rate: 1.0e-4
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

examples/v1/train_batching_strategy/train_full_fsdp2_dynamic_batching.yaml

@@ -0,0 +1,30 @@
+model: Qwen/Qwen3-0.6B
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 2
+batching_strategy: dynamic_batching
+cutoff_len: 2048
+learning_rate: 1.0e-4
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

examples/v1/train_batching_strategy/train_full_fsdp2_dynamic_padding_free.yaml

@@ -0,0 +1,30 @@
+model: Qwen/Qwen3-0.6B
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 4
+batching_strategy: dynamic_padding_free
+flash_attn: flash_attention2
+cutoff_len: 2048
+learning_rate: 1.0e-4
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

examples/v1/train_batching_strategy/train_full_fsdp2_padding_free.yaml

@@ -0,0 +1,30 @@
+model: Qwen/Qwen3-0.6B
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 4
+batching_strategy: padding_free
+flash_attn: flash_attention2
+cutoff_len: 2048
+learning_rate: 1.0e-4
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

examples/v1/train_full/train_full_liger_kernel.yaml

@@ -0,0 +1,28 @@
+model: Qwen/Qwen3-0.6B
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: liger_kernel
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

src/llamafactory/extras/env.py

@@ -19,7 +19,7 @@
 from collections import OrderedDict
 
 
-VERSION = "0.9.5.dev0"
+VERSION = "0.9.5"
 
 
 def print_env() -> None:

src/llamafactory/model/patcher.py

@@ -162,8 +162,14 @@ def patch_qwen3_5_forward(model: "PreTrainedModel") -> None:
         if position_ids is not None and position_ids.ndim == 3:
             position_ids = position_ids[0]
 
-        # `prepare_fa_kwargs_from_position_ids` would crash on None; guard for safety.
-        cu_seqlens = prepare_fa_kwargs_from_position_ids(position_ids)[0][0] if position_ids is not None else None
+        # cu_seqlens for the FLA varlen path is only needed when batch_size == 1:
+        # packing / neat-packing: always folded into a single sequence (bsz == 1) -> varlen
+        # non-packing, bsz == 1: single segment, equivalent to a standard single sequence
+        # non-packing, bsz > 1: not packed, use cu_seqlens=None and standard batched kernels
+        if position_ids is not None and batch_size == 1:
+            cu_seqlens = prepare_fa_kwargs_from_position_ids(position_ids)[0][0]
+        else:
+            cu_seqlens = None
 
         # FLA varlen kernels expect [B, T, D] layout, not [B, D, T] like the
         # standard causal-conv1d path that the upstream forward uses.

src/llamafactory/train/callbacks.py

@@ -20,7 +20,6 @@ import sys
 import time
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass, field
 from datetime import timedelta
 from typing import TYPE_CHECKING, Any, Optional
 
@@ -584,7 +583,7 @@ class ModuleProfilerCallback(TrainerCallback):
         if matched:
             logger.info_rank0(
                 f"ModuleProfiler: registered hooks on {len(matched)} modules: {matched[:5]}"
-                + (f" ... (+{len(matched)-5} more)" if len(matched) > 5 else "")
+                + (f" ... (+{len(matched) - 5} more)" if len(matched) > 5 else "")
             )
         else:
             logger.warning_rank0(f"ModuleProfiler: no modules matched patterns {self.patterns}")
@@ -616,7 +615,7 @@ class ModuleProfilerCallback(TrainerCallback):
             bwd = self._backward_times.get(name, [])
             fwd_mean = sum(fwd) / len(fwd) if fwd else 0.0
             bwd_mean = sum(bwd) / len(bwd) if bwd else 0.0
-            lines.append(f"  {name}: fwd={fwd_mean:.3f}, bwd={bwd_mean:.3f}, total={fwd_mean+bwd_mean:.3f}")
+            lines.append(f"  {name}: fwd={fwd_mean:.3f}, bwd={bwd_mean:.3f}, total={fwd_mean + bwd_mean:.3f}")
 
         logger.info_rank0("\n".join(lines))
         self._forward_times.clear()

src/llamafactory/train/tuner.py

@@ -80,10 +80,10 @@ def _training_function(config: dict[str, Any]) -> None:
     if finetuning_args.early_stopping_steps is not None:
         callbacks.append(EarlyStoppingCallback(early_stopping_patience=finetuning_args.early_stopping_steps))
 
-    if training_args.enable_torch_profiler:
+    if getattr(training_args, "enable_torch_profiler", False):
         callbacks.append(TorchProfilerCallback(training_args))
 
-    if training_args.profile_modules:
+    if getattr(training_args, "profile_modules", None):
         callbacks.append(ModuleProfilerCallback(training_args.profile_modules))
 
     callbacks.append(ReporterCallback(model_args, data_args, finetuning_args, generating_args))  # add to last

src/llamafactory/v1/config/__init__.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from ..utils.types import AttentionFunction
 from .arg_parser import InputArgument, get_args
 from .arg_utils import BatchingStrategy, ModelClass, SampleBackend
 from .data_args import DataArguments
@@ -21,6 +22,7 @@ from .training_args import TrainingArguments
 
 
 __all__ = [
+    "AttentionFunction",
     "BatchingStrategy",
     "DataArguments",
     "InputArgument",

src/llamafactory/v1/config/arg_parser.py

@@ -57,15 +57,12 @@ def get_args(args: InputArgument = None) -> tuple[ModelArguments, DataArguments,
             print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
             raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
 
+    model_args, data_args, training_args, sample_args = parsed_args
     # Seed as early as possible after argument parsing so all downstream
     # components (dist init, dataloader, model init in run_* entrypoints) share the same RNG state.
-    for arg in parsed_args:
-        seed = getattr(arg, "seed", None)
-        if seed is not None:
-            set_seed(seed)
-            break
+    set_seed(training_args.seed, full_determinism=training_args.full_determinism)
 
-    return tuple(parsed_args)
+    return model_args, data_args, training_args, sample_args
 
 
 if __name__ == "__main__":

src/llamafactory/v1/config/model_args.py

@@ -15,6 +15,7 @@
 
 from dataclasses import dataclass, field
 
+from ..utils.types import AttentionFunction
 from .arg_utils import ModelClass, PluginConfig, get_plugin_config
 
 
@@ -32,6 +33,12 @@ class ModelArguments:
         default=False,
         metadata={"help": "Trust remote code from Hugging Face."},
     )
+    flash_attn: AttentionFunction = field(
+        default=AttentionFunction.SDPA,
+        metadata={
+            "help": "Attention implementation to use: eager, sdpa, or flash_attention_2. SDPA is the default implementation for models."
+        },
+    )
     model_class: ModelClass = field(
         default=ModelClass.LLM,
         metadata={"help": "Model class from Hugging Face."},
@@ -54,6 +61,12 @@ class ModelArguments:
     )
 
     def __post_init__(self) -> None:
+        supported_flash_attn = [item.value for item in AttentionFunction]
+        if self.flash_attn not in supported_flash_attn:
+            raise ValueError(
+                f"Unsupported `flash_attn`: {self.flash_attn}. Supported values are: {supported_flash_attn}."
+            )
+
         self.init_config = get_plugin_config(self.init_config)
         self.peft_config = get_plugin_config(self.peft_config)
         self.kernel_config = get_plugin_config(self.kernel_config)

src/llamafactory/v1/config/training_args.py

@@ -85,6 +85,10 @@ class TrainingArguments:
         default=42,
         metadata={"help": "Random seed that will be set at the beginning of training."},
     )
+    full_determinism: bool = field(
+        default=False,
+        metadata={"help": "Enable full deterministic mode for reproducible distributed training."},
+    )
     resume_from_checkpoint: str | None = field(
         default=None,
         metadata={"help": "Path to a checkpoint directory to resume training from, or 'auto' to find the latest."},
@@ -116,3 +120,9 @@ class TrainingArguments:
         self.dist_config = get_plugin_config(self.dist_config)
         self.optim_config = get_plugin_config(self.optim_config)
         self.lr_scheduler_config = get_plugin_config(self.lr_scheduler_config)
+
+        if str(self.batching_strategy) == str(BatchingStrategy.DYNAMIC_BATCHING):
+            if self.max_steps is None or self.max_steps <= 0:
+                raise ValueError("`dynamic_batching` requires `max_steps` because it is step-driven.")
+            if self.save_epochs is not None:
+                raise ValueError("`save_epochs` is not supported with `dynamic_batching`; use `save_steps` instead.")

src/llamafactory/v1/core/base_trainer.py

@@ -34,7 +34,7 @@ import torch.nn.functional as F
 
 from ..accelerator.helper import ReduceOp
 from ..accelerator.interface import Dim, DistributedInterface
-from ..config import TrainingArguments
+from ..config import BatchingStrategy, TrainingArguments
 from ..utils import logging
 from ..utils.callbacks import (
     CallbackHandler,
@@ -147,13 +147,19 @@ class BaseTrainer:
             from ..plugins.model_plugins.parallelization.sequence_parallel import SequenceParallelModelPlugin
 
             if model.config._attn_implementation != "flash_attention_2":
-                logger.warning_rank0(
-                    "Sequence parallelism is optimized for flash attention only. Replace the attention implementation to flash_attention_2."
+                raise ValueError(
+                    "Sequence parallelism requires flash attention. Please set `flash_attn: flash_attention_2`."
                 )
-                model.config._attn_implementation = "flash_attention_2"
+
             SequenceParallelModelPlugin(self.args.dist_config.get("cp_mode", "ulysses"))(model, self.args.dist_config)
 
     def _create_batch_generator(self) -> None:
+        if (
+            self.args.batching_strategy == BatchingStrategy.PADDING_FREE
+            and getattr(self.model.config, "_attn_implementation", None) != "flash_attention_2"
+        ):
+            raise ValueError("`padding_free` requires `flash_attn: flash_attention_2`.")
+
         self.train_batch_generator = BatchGenerator(
             dataset=self.train_dataset,
             renderer=self.renderer,
@@ -237,6 +243,7 @@ class BaseTrainer:
             self.train_batch_generator.set_epoch(epoch)
             self.callback_handler.on_epoch_begin(self.args, self.state)
 
+            # BatchGenerator is an iterator; each loop step calls its __next__ to produce one optimizer step.
             for micro_batches in self.train_batch_generator:
                 self.global_step += 1
 

src/llamafactory/v1/core/model_engine.py

@@ -120,6 +120,7 @@ class ModelEngine:
             init_device = DistributedInterface().current_device
 
         init_kwargs = {} if self._deepspeed_zero3_enabled else {"device_map": init_device}
+        logger.info_rank0(f"Using attention implementation: {self.args.flash_attn}.")
 
         if self.args.quant_config is not None:
             from ..plugins.model_plugins.quantization import QuantizationPlugin
@@ -164,6 +165,7 @@ class ModelEngine:
                 self.args.model,
                 config=self.model_config,
                 dtype="auto",
+                attn_implementation=self.args.flash_attn,
                 trust_remote_code=self.args.trust_remote_code,
                 **init_kwargs,
             )
@@ -188,9 +190,12 @@ class ModelEngine:
         if self.args.kernel_config is not None:
             from ..plugins.model_plugins.kernels.interface import KernelPlugin
 
-            model = KernelPlugin(self.args.kernel_config.name)(
-                model, include_kernels=self.args.kernel_config.get("include_kernels")
-            )
+            kernel_config = self.args.kernel_config
+            kernel_kwargs: dict = {"model": model, "include_kernels": kernel_config.get("include_kernels")}
+            if kernel_config.name == "liger_kernel":
+                # Fused linear CE omits logits; SFT stage needs logits for loss_weights.
+                kernel_kwargs["require_logits"] = self.is_train
+            model = KernelPlugin(kernel_config.name)(**kernel_kwargs)
 
         return model
 

src/llamafactory/v1/core/utils/batching.py

@@ -42,6 +42,8 @@ from .rendering import Renderer
 
 logger = logging.get_logger(__name__)
 
+__all__ = ["BatchGenerator"]
+
 
 def default_collate_fn(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
     micro_batch_size = batch_info["micro_batch_size"]
@@ -102,19 +104,18 @@ class BatchGenerator(Iterator):
         if not self.drop_last:
             raise ValueError("Drop last must be True.")
 
+        self._batch_info: BatchInfo = {
+            "micro_batch_size": self.micro_batch_size,
+            "num_micro_batch": self.num_micro_batch,
+            "cutoff_len": self.cutoff_len,
+        }
+
         self._init_data_provider()
 
         self._is_resuming: bool = False
         self._data_iter = iter(self._data_provider)
         self._buffer = StatefulBuffer()
 
-        self._batch_info: BatchInfo = {
-            "micro_batch_size": self.micro_batch_size,
-            "num_micro_batch": self.num_micro_batch,
-            "cutoff_len": self.cutoff_len,
-            "data_iter": self._data_iter,
-        }
-
         logger.info_rank0(
             f"Init unified data loader with global batch size {self.global_batch_size}, "
             f"micro batch size {self.micro_batch_size}, "
@@ -137,12 +138,19 @@ class BatchGenerator(Iterator):
         else:
             raise NotImplementedError("Iterable dataset is not supported yet.")
 
+        if self.batching_strategy == BatchingStrategy.NORMAL:
+            batch_size = self.micro_batch_size * self.num_micro_batch
+        else:
+            from ...plugins.trainer_plugins.batching import BatchingPlugin
+
+            batch_size = BatchingPlugin(self.batching_strategy).get_data_provider_batch_size(self._batch_info)
+
         generator_seed = torch.Generator()
         generator_seed.manual_seed(self.seed)
 
         self._data_provider = StatefulDataLoader(
             self.dataset,
-            batch_size=self.micro_batch_size * self.num_micro_batch,
+            batch_size=batch_size,
             sampler=sampler,
             num_workers=self.batching_workers,
             collate_fn=self.renderer.process_samples,
@@ -156,8 +164,7 @@ class BatchGenerator(Iterator):
         else:
             from ...plugins.trainer_plugins.batching import BatchingPlugin
 
-            self._length = BatchingPlugin(self.batching_strategy).compute_length(self._data_provider)
-            raise NotImplementedError("Batching strategy other than NORMAL is not supported yet.")
+            self._length = BatchingPlugin(self.batching_strategy).compute_length(self._data_provider, self._batch_info)
 
     def __len__(self) -> int:
         return self._length
@@ -190,7 +197,7 @@ class BatchGenerator(Iterator):
         else:
             from ...plugins.trainer_plugins.batching import BatchingPlugin
 
-            BatchingPlugin(self.batching_strategy).fill_buffer(self._buffer, self._batch_info)
+            BatchingPlugin(self.batching_strategy).fill_buffer(self._buffer, self._batch_info, self._next_samples)
 
     def _generate_batch(self) -> list[BatchInput] | None:
         if self.batching_strategy == BatchingStrategy.NORMAL:
@@ -200,6 +207,20 @@ class BatchGenerator(Iterator):
 
             return BatchingPlugin(self.batching_strategy).generate_batch(self._buffer, self._batch_info)
 
+    def _next_samples(self, restart: bool) -> list[ModelInput] | None:
+        try:
+            return next(self._data_iter)
+        except StopIteration:
+            if not restart:
+                return None
+
+            # Dynamic batching may restart the provider to fill one token-budgeted batch.
+            self._data_iter = iter(self._data_provider)
+            try:
+                return next(self._data_iter)
+            except StopIteration:
+                return None
+
     def state_dict(self) -> dict[str, Any]:
         return {
             "buffer": self._buffer.state_dict(),

src/llamafactory/v1/plugins/model_plugins/kernels/base.py

@@ -34,7 +34,7 @@ class BaseKernel(ABC):
     """
 
     _kernel_id: Any = ""  # kernel ID, any hashable value to identify a kernel implementation
-    _device: DeviceType = DeviceType.CPU  # "cuda", "npu", "cpu", etc.
+    _device: list[DeviceType] = [DeviceType.CPU]  # "cuda", "npu", "cpu", etc.
 
     @classmethod
     def get_kernel_id(cls) -> str:
@@ -42,8 +42,8 @@ class BaseKernel(ABC):
         return cls._kernel_id
 
     @classmethod
-    def get_device(cls) -> str:
-        """Returns the device type associated with the kernel (e.g., "cuda", "npu", "cpu")."""
+    def get_device(cls) -> list[DeviceType]:
+        """Returns the device type list associated with the kernel (e.g., ["cuda", "npu", "cpu"])."""
         return cls._device
 
     @classmethod
@@ -58,7 +58,7 @@ class BaseKernel(ABC):
             it should raise an error instead of silently switching.
             Kernels can override this method to implement custom dependency checks.
         """
-        if cls._device != get_current_accelerator().type:
+        if get_current_accelerator().type not in cls._device:
             return False
         return True
 

src/llamafactory/v1/plugins/model_plugins/kernels/interface.py

@@ -138,3 +138,48 @@ def apply_default_kernels(model: HFModel, include_kernels: str = None) -> HFMode
         apply_kernel(kernel, model=model)
 
     return model
+
+
+@KernelPlugin("liger_kernel").register()
+def apply_liger_kernels(
+    model: HFModel,
+    include_kernels: str = None,
+    require_logits: bool = False,
+) -> HFModel:
+    """Applies Liger kernel to the model.
+
+    Args:
+        model (HFModel): The model instance to apply kernels to.
+        include_kernels (str, optional): If ``"auto"`` or ``True``, apply Liger with
+                                         library defaults. If a comma-separated list (e.g.
+                                         ``rope,rms_norm``), enable only those ops; names match
+                                         ``apply_liger_kernel_to_*`` kwargs: ``rope``, ``rms_norm``,
+                                         ``swiglu``, ``cross_entropy``, ``fused_linear_cross_entropy``.
+                                         If ``None`` or ``False``, do nothing. Defaults to ``None``.
+        require_logits (bool, optional): When true, disables ``fused_linear_cross_entropy`` in favor
+                                         of non-fused CE so the forward pass returns ``logits``. Needed
+                                         for trainers that compute weighted loss from logits (e.g. v1
+                                         SFT with ``loss_weights``). Defaults to ``False`` (fused CE
+                                         when supported). The v1 ``run_sft`` entrypoint sets
+                                         ``require_logits`` to true for ``liger_kernel`` when the key
+                                         is omitted so SFT weighted loss keeps working.
+
+    Returns:
+        HFModel: The model with Liger kernel applied.
+    """
+    if not include_kernels:
+        return model
+    if include_kernels == "auto" or include_kernels is True:
+        use_kernels = "auto"
+    else:
+        use_kernels = [k.strip() for k in include_kernels.split(",") if k.strip()]
+        if not use_kernels:
+            return model
+
+    try:
+        from .liger_kernel_ops import LigerKernel
+    except ImportError as e:
+        logger.warning_rank0(f"[Kernel] Failed to import liger_kernel ops, skip. Error: {e}")
+        return model
+
+    return LigerKernel.apply(use_kernels=use_kernels, model=model, require_logits=require_logits)

src/llamafactory/v1/plugins/model_plugins/kernels/liger_kernel_ops.py

@@ -0,0 +1,148 @@
+# 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.
+
+"""The definition of Liger Kernel.
+
+Init Phase:
+1. Define LigerKernel class.
+2. Register Liger kernel.
+
+"""
+
+import inspect
+
+from ....accelerator.helper import DeviceType, get_current_accelerator
+from ....utils.logging import get_logger
+from ....utils.types import HFModel
+from .base import BaseKernel
+
+
+logger = get_logger(__name__)
+
+_LIGER_FN_BY_MODEL_TYPE: dict[str, str] = {
+    "qwen3": "apply_liger_kernel_to_qwen3",
+    "qwen3_moe": "apply_liger_kernel_to_qwen3_moe",
+    "qwen3_next": "apply_liger_kernel_to_qwen3_next",
+    "qwen3_5": "apply_liger_kernel_to_qwen3_5",
+    "qwen3_5_text": "apply_liger_kernel_to_qwen3_5_text",
+    "qwen3_5_moe": "apply_liger_kernel_to_qwen3_5_moe",
+    "qwen3_5_moe_text": "apply_liger_kernel_to_qwen3_5_moe_text",
+}
+
+
+class LigerKernel(BaseKernel):
+    """Liger Kernel for optimized model training."""
+
+    _device = [DeviceType.CUDA, DeviceType.NPU]
+
+    @classmethod
+    def check_deps(cls) -> bool:
+        """Checks if the required dependencies for the kernel are available."""
+        try:
+            import liger_kernel  # noqa: F401
+
+            return super().check_deps()
+        except ImportError:
+            logger.warning_rank0(
+                "Liger kernel is not installed, the kernel_config liger_kernel will be ignored. Please install it from https://github.com/linkedin/Liger-Kernel."
+            )
+            return False
+
+    @classmethod
+    def apply(cls, **kwargs) -> "HFModel":
+        """Applies the Liger kernel to the model.
+
+        Args:
+            **kwargs: Must include ``model``. Optional ``use_kernels`` is a list of Liger op
+                names to enable exclusively, or the string ``"auto"`` to use each
+                ``apply_liger_kernel_to_*`` function's signature defaults (same as calling
+                upstream with only ``model``). Optional ``require_logits`` forces non-fused
+                cross entropy when supported.
+
+        Returns:
+            HFModel: The model with Liger kernel applied.
+
+        Raises:
+            ValueError: If the model is not provided.
+            RuntimeError: If dependencies are not met.
+        """
+        model = kwargs.get("model")
+        use_kernels = kwargs.get("use_kernels", None)
+        if model is None:
+            raise ValueError(f"HFModel instance is required for {cls.__name__}.")
+
+        if not cls.check_deps():
+            raise RuntimeError(
+                f"current device is not supported by liger_kernel. Current device is {get_current_accelerator().type}, supported devices are {cls.get_device()}"
+            )
+
+        require_logits = kwargs.get("require_logits", False)
+
+        model_type = getattr(model.config, "model_type", None)
+
+        if model_type not in _LIGER_FN_BY_MODEL_TYPE:
+            logger.warning_rank0("Current model does not support liger kernel.")
+            return model
+
+        import liger_kernel.transformers as liger_transformers
+
+        apply_liger_kernel = getattr(liger_transformers, _LIGER_FN_BY_MODEL_TYPE[model_type])
+
+        sig = inspect.signature(apply_liger_kernel).parameters
+        togglable = [name for name in sig if name != "model"]
+
+        def _normalize_op_name(raw: str) -> str:
+            key = raw.strip().lower().replace("-", "_")
+            aliases = {
+                "rmsnorm": "rms_norm",
+                "flce": "fused_linear_cross_entropy",
+                "lce": "fused_linear_cross_entropy",
+                "fused_ce": "fused_linear_cross_entropy",
+            }
+            return aliases.get(key, key)
+
+        if use_kernels is not None and len(use_kernels) == 0:
+            return model
+
+        if use_kernels != "auto":
+            selected = {_normalize_op_name(k) for k in use_kernels}
+            ops = selected - set(togglable)
+            if ops:
+                raise ValueError(
+                    f"Unknown Liger op(s) {sorted(ops)} for model_type={model_type}. Valid: {sorted(togglable)}"
+                )
+            if "cross_entropy" in selected and "fused_linear_cross_entropy" in selected:
+                raise ValueError("cross_entropy and fused_linear_cross_entropy cannot both be enabled.")
+            call_kwargs = {name: (name in selected) for name in togglable}
+            call_kwargs["model"] = model
+        else:
+            # Mirror ``liger_kernel`` signature defaults so patches match upstream defaults
+            # and logging reflects enabled ops (omitted kwargs only live in the callee).
+            call_kwargs = {"model": model}
+            for name in togglable:
+                param = sig[name]
+                if param.default is not inspect.Parameter.empty:
+                    call_kwargs[name] = param.default
+
+        if require_logits and "fused_linear_cross_entropy" in sig:
+            logger.warning_rank0("Current training stage does not support chunked cross entropy.")
+            call_kwargs["fused_linear_cross_entropy"] = False
+            call_kwargs["cross_entropy"] = True
+
+        apply_liger_kernel(**call_kwargs)
+
+        applied = sorted(name for name, on in call_kwargs.items() if name != "model" and on)
+        logger.info_rank0(f"These Liger ops are applied to the model: {applied}")
+
+        return model

src/llamafactory/v1/plugins/model_plugins/kernels/ops/mlp/npu_fused_moe.py

@@ -228,6 +228,30 @@ class NpuMoeFused:
         routed_out = self.experts(hidden_states, routing_weights, router_indices)
         return routed_out
 
+    @staticmethod
+    def npu_moe_experts_v5_forward(
+        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
+    ) -> torch.Tensor:
+        """Forward pass for Transformers v5+ MoE experts using NPU fused operations.
+
+        Transformers v5 stores expert weights in F.linear layout:
+        gate_up_proj: [num_experts, 2 * intermediate_dim, hidden_dim]
+        down_proj: [num_experts, hidden_dim, intermediate_dim]
+        The NPU grouped matmul path expects matmul layout, so both weights are transposed.
+        """
+        hidden_states = hidden_states.reshape(-1, self.hidden_dim)
+        permuted_hidden_states, row_ids_map = torch_npu.npu_moe_token_permute(
+            hidden_states, top_k_index.to(torch.int32)
+        )
+        tokens_per_expert = torch.histc(top_k_index.float(), bins=self.num_experts, min=0, max=self.num_experts).long()
+
+        gate_up_proj = self.gate_up_proj.transpose(1, 2)
+        down_proj = self.down_proj.transpose(1, 2)
+        intermediate_hidden_states = GmmFunction.apply(permuted_hidden_states, gate_up_proj, tokens_per_expert)
+        intermediate_activations = torch_npu.npu_swiglu(intermediate_hidden_states, dim=-1)
+        output = GmmFunction.apply(intermediate_activations, down_proj, tokens_per_expert)
+        return torch_npu.npu_moe_token_unpermute(output, row_ids_map, probs=top_k_weights)
+
 
 class Qwen3NpuMoeFused:
     """Container for Qwen3 NPU fused MoE forward functions."""
@@ -283,16 +307,30 @@ class Qwen3NpuMoeFused:
 
 
 # moe patch config mapping
-kernel_moe_mapping = {
-    "Qwen3VLMoeForConditionalGeneration": {
-        "Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_forward,
-        "Qwen3VLMoeTextSparseMoeBlock": NpuMoeFused.npu_moe_sparse_block_forward,
+if is_transformers_version_greater_than("5.0.0"):
+    kernel_moe_mapping = {
+        "Qwen3MoeForCausalLM": {
+            "Qwen3MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3VLMoeForConditionalGeneration": {
+            "Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3_5MoeForCausalLM": {
+            "Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3_5MoeForConditionalGeneration": {
+            "Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
     }
-}
-
-if not is_transformers_version_greater_than("5.0.0"):
-    kernel_moe_mapping["Qwen3MoeForCausalLM"] = {
-        "Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward
+else:
+    kernel_moe_mapping = {
+        "Qwen3MoeForCausalLM": {
+            "Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward,
+        },
+        "Qwen3VLMoeForConditionalGeneration": {
+            "Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_forward,
+            "Qwen3VLMoeTextSparseMoeBlock": NpuMoeFused.npu_moe_sparse_block_forward,
+        },
     }
 
 

src/llamafactory/v1/plugins/model_plugins/kernels/ops/rms_norm/npu_rms_norm.py

@@ -51,22 +51,17 @@ def _should_use_residual_rmsnorm(module):
         bool: ``True`` if the module uses residual parameterization, ``False`` otherwise.
 
     .. note::
-        This detection ensures compatibility with future model versions (e.g., Qwen3.6, Qwen4.0)
-        without hardcoding version numbers. Two methods are used: weight value inspection
-        (most reliable) and class name pattern matching (backward compatibility).
+        This must follow the module's forward semantics. Do not infer it from trained
+        weight values because standard RMSNorm weights can also be close to zero.
     """
-    if hasattr(module, "weight") and module.weight is not None:
-        weight_mean = module.weight.data.mean().item()
-        if abs(weight_mean) < 0.3:
-            return True
+    residual_rmsnorm_classes = {
+        "Qwen3_5RMSNorm",
+        "Qwen3_5MoeRMSNorm",
+        "Qwen3NextRMSNorm",
+    }
 
     class_name = module.__class__.__name__
-    residual_patterns = ["Qwen3_5", "Qwen3_6", "Qwen4"]
-    for pattern in residual_patterns:
-        if pattern in class_name:
-            return True
-
-    return False
+    return class_name in residual_rmsnorm_classes
 
 
 def npu_rms_norm_forward(self, hidden_states):
@@ -82,7 +77,7 @@ def npu_rms_norm_forward(self, hidden_states):
     _eps = getattr(self, "variance_epsilon", None) or getattr(self, "eps", 1e-6)
 
     if hasattr(self, "weight") and self.weight is not None:
-        if _should_use_residual_rmsnorm(self):
+        if getattr(self, "_npu_use_residual_rmsnorm", False):
             effective_weight = 1.0 + self.weight.float()
         else:
             effective_weight = self.weight.float()
@@ -162,6 +157,7 @@ class NpuRMSNormKernel(BaseKernel):
                 if "Gated" in module.__class__.__name__:
                     module.forward = types.MethodType(npu_gated_rms_norm_forward, module)
                 else:
+                    module._npu_use_residual_rmsnorm = _should_use_residual_rmsnorm(module)
                     module.forward = types.MethodType(npu_rms_norm_forward, module)
 
         return model

src/llamafactory/v1/plugins/model_plugins/kernels/registry.py

@@ -58,7 +58,7 @@ class Registry:
         device = kernel_cls.get_device()
 
         # The device type of the current accelerator does not match the device type required by the kernel, skip registration
-        if device != get_current_accelerator().type:
+        if get_current_accelerator().type not in device:
             return
 
         if not kernel_id:

src/llamafactory/v1/plugins/model_plugins/parallelization/ulysses.py

@@ -114,7 +114,6 @@ class UlyssesAttention(torch.nn.Module):
         # TODO (Reza): change the api on the megatron-deepspeed side so that we only receive all data (q,k, and v) together!
         # in shape : e.g.,  [s/p:h:]
         # (bs, seq_len/N, head_cnt, head_size) -> (bs, seq_len, head_cnt/N, head_size)
-
         # scatter 2, gather 1
         q = SeqAllToAll4D.apply(self.spg, query, self.scatter_idx, self.gather_idx)
         k = SeqAllToAll4D.apply(self.spg, key, self.scatter_idx, self.gather_idx)
@@ -123,19 +122,24 @@ class UlyssesAttention(torch.nn.Module):
         if softmax_scale is None:
             softmax_scale = q.shape[-1] ** -0.5
 
-        if attention_mask is None:
-            if position_ids is not None:
-                attention_mask = torch.ones_like(position_ids).to(torch.int64)
-            else:
-                attention_mask = torch.ones(q.shape[0], q.shape[1], dtype=torch.int64, device=q.device)
+        if position_ids is not None:
+            global_position_ids = [
+                torch.empty_like(position_ids) for _ in range(get_ulysses_sequence_parallel_world_size(self.spg))
+            ]
+            dist.all_gather(global_position_ids, position_ids, group=self.spg)
+            position_ids = torch.cat(global_position_ids, dim=-1).contiguous()
+            attention_mask = None
         else:
-            attention_mask = attention_mask.to(torch.int64)
+            if attention_mask is None:
+                attention_mask = torch.ones(q.shape[0], q.shape[1], dtype=torch.int64, device=q.device)
+            else:
+                attention_mask = attention_mask.to(torch.int64)
 
-        global_attention_mask = [
-            torch.empty_like(attention_mask) for _ in range(get_ulysses_sequence_parallel_world_size(self.spg))
-        ]
-        dist.all_gather(global_attention_mask, attention_mask, group=self.spg)
-        attention_mask = torch.cat(global_attention_mask, dim=1)
+            global_attention_mask = [
+                torch.empty_like(attention_mask) for _ in range(get_ulysses_sequence_parallel_world_size(self.spg))
+            ]
+            dist.all_gather(global_attention_mask, attention_mask, group=self.spg)
+            attention_mask = torch.cat(global_attention_mask, dim=1)
 
         context_layer = self.attn_fn(
             q,

src/llamafactory/v1/plugins/trainer_plugins/batching.py

@@ -12,23 +12,272 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections.abc import Callable
+from math import ceil
+from typing import Any
+
+import torch
+from torch.utils.data import default_collate
+
+from ...utils.constants import IGNORE_INDEX
+from ...utils.helper import pad_and_truncate
 from ...utils.objects import StatefulBuffer
 from ...utils.plugin import BasePlugin
-from ...utils.types import BatchInfo, BatchInput, DataLoader
+from ...utils.types import BatchInfo, BatchInput, DataLoader, ModelInput
 
 
 class BatchingPlugin(BasePlugin):
-    def compute_length(self, data_provider: DataLoader) -> int:
+    def get_data_provider_batch_size(self, batch_info: BatchInfo) -> int:
+        """Return the raw data provider batch size for this batching strategy."""
+        return self["get_data_provider_batch_size"](batch_info)
+
+    def compute_length(self, data_provider: DataLoader, batch_info: BatchInfo) -> int:
         """Compute the length of the batch generator.
 
         The approximate length is used to calculate the lr schedule.
         """
-        raise NotImplementedError()
+        return self["compute_length"](data_provider, batch_info)
 
-    def fill_buffer(self, buffer: StatefulBuffer, batch_info: BatchInfo) -> None:
+    def fill_buffer(
+        self,
+        buffer: StatefulBuffer,
+        batch_info: BatchInfo,
+        next_samples: Callable[[bool], list[ModelInput] | None],
+    ) -> None:
         """Fill the buffer with data."""
-        raise NotImplementedError()
+        return self["fill_buffer"](buffer, batch_info, next_samples)
 
     def generate_batch(self, buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
         """Generate a batch from the buffer."""
-        raise NotImplementedError()
+        return self["generate_batch"](buffer, batch_info)
+
+
+def _get_dynamic_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
+    """Return sample counts for micro batches formed by one padded-token budget."""
+    budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
+    cutoff_len = batch_info["cutoff_len"]
+    sizes = []
+    index = 0
+    while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
+        max_sample_len = 0
+        used = 0
+        is_complete = False
+        while index + used < len(samples):
+            sample_len = min(len(samples[index + used]["input_ids"]), cutoff_len)
+            padded_tokens = max(max_sample_len, sample_len) * (used + 1)
+            if used > 0 and padded_tokens > budget:
+                is_complete = True
+                break
+
+            max_sample_len = max(max_sample_len, sample_len)
+            used += 1
+            if max_sample_len * used >= budget:
+                is_complete = True
+                break
+
+        if used == 0 or not is_complete:
+            break
+
+        sizes.append(used)
+        index += used
+
+    return sizes
+
+
+def _get_dynamic_padding_free_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
+    budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
+    cutoff_len = batch_info["cutoff_len"]
+    sizes = []
+    index = 0
+
+    while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
+        current_tokens = 0
+        used = 0
+        is_complete = False
+
+        while index + used < len(samples):
+            sample = samples[index + used]
+            sample_len = min(len(sample["input_ids"]), cutoff_len)
+
+            if current_tokens + sample_len > budget:
+                is_complete = True
+                break
+
+            current_tokens += sample_len
+            used += 1
+
+        if used <= 0 or not is_complete:
+            break
+
+        sizes.append(used)
+        index += used
+
+    return sizes
+
+
+def _pack_padding_free_samples(samples: list[ModelInput], cutoff_len: int) -> BatchInput | None:
+    """Pack fixed samples into one padding-free sequence without a token budget."""
+    packed: dict[str, list[Any]] = {}
+    position_ids: list[int] = []
+
+    for sample_index, sample in enumerate(samples):
+        # Padding-free still truncates each sample by cutoff_len before packing
+        # all samples into one contiguous sequence.
+        sample_len = min(len(sample["input_ids"]), cutoff_len)
+        if sample_len <= 0:
+            continue
+
+        for key, value in sample.items():
+            if key in ("attention_mask", "position_ids") or isinstance(value, str):
+                continue
+
+            if key not in packed:
+                packed[key] = []
+
+            sliced_value = list(value[:sample_len])
+            if sample_index > 0 and sliced_value:
+                if key == "labels":
+                    sliced_value[0] = IGNORE_INDEX
+                elif key == "loss_weights":
+                    sliced_value[0] = 0.0
+
+            packed[key].extend(sliced_value)
+
+        position_ids.extend(range(sample_len))
+
+    if not position_ids:
+        return None
+
+    packed["position_ids"] = position_ids
+    packed["attention_mask"] = [1] * len(position_ids)
+    return {key: torch.tensor(value).unsqueeze(0) for key, value in packed.items()}
+
+
+@BatchingPlugin("padding_free").register("get_data_provider_batch_size")
+def get_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
+    return batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
+
+
+@BatchingPlugin("padding_free").register("compute_length")
+def compute_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
+    return len(data_provider)
+
+
+@BatchingPlugin("padding_free").register("fill_buffer")
+def fill_padding_free_buffer(
+    buffer: StatefulBuffer,
+    batch_info: BatchInfo,
+    next_samples: Callable[[bool], list[ModelInput] | None],
+) -> None:
+    while len(buffer) < batch_info["micro_batch_size"] * batch_info["num_micro_batch"]:
+        samples = next_samples(False)
+        if samples is None:
+            break
+
+        buffer.put(samples)
+
+
+@BatchingPlugin("padding_free").register("generate_batch")
+def generate_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
+    micro_batch_size = batch_info["micro_batch_size"]
+    num_micro_batch = batch_info["num_micro_batch"]
+    cutoff_len = batch_info["cutoff_len"]
+    batch_size = micro_batch_size * num_micro_batch
+    if len(buffer) < batch_size:
+        return None
+
+    samples = buffer.get(batch_size)
+    batch = []
+    for i in range(num_micro_batch):
+        micro_batch = samples[i * micro_batch_size : (i + 1) * micro_batch_size]
+        packed_micro_batch = _pack_padding_free_samples(micro_batch, cutoff_len)
+        if packed_micro_batch is None:
+            return None
+
+        batch.append(packed_micro_batch)
+
+    return batch
+
+
+@BatchingPlugin("dynamic_batching").register("get_data_provider_batch_size")
+def get_dynamic_batching_data_provider_batch_size(batch_info: BatchInfo) -> int:
+    return 1
+
+
+@BatchingPlugin("dynamic_batching").register("compute_length")
+def compute_dynamic_batching_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
+    batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
+    return ceil(len(data_provider) / batch_size)
+
+
+@BatchingPlugin("dynamic_batching").register("fill_buffer")
+def fill_dynamic_batching_buffer(
+    buffer: StatefulBuffer,
+    batch_info: BatchInfo,
+    next_samples: Callable[[bool], list[ModelInput] | None],
+) -> None:
+    while len(_get_dynamic_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
+        samples = next_samples(True)
+        if samples is None:
+            break
+
+        buffer.put(samples)
+
+
+@BatchingPlugin("dynamic_batching").register("generate_batch")
+def generate_dynamic_batching_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
+    micro_batch_sample_counts = _get_dynamic_micro_batch_sizes(buffer.samples, batch_info)
+    if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
+        return None
+
+    batch = []
+    cutoff_len = batch_info["cutoff_len"]
+    for num_samples in micro_batch_sample_counts:
+        samples = buffer.get(num_samples)
+        batch.append(default_collate(pad_and_truncate(samples, cutoff_len)))
+
+    return batch
+
+
+@BatchingPlugin("dynamic_padding_free").register("get_data_provider_batch_size")
+def get_dynamic_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
+    return 1
+
+
+@BatchingPlugin("dynamic_padding_free").register("compute_length")
+def compute_dynamic_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
+    batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
+    return ceil(len(data_provider) / batch_size)
+
+
+@BatchingPlugin("dynamic_padding_free").register("fill_buffer")
+def fill_dynamic_padding_free_buffer(
+    buffer: StatefulBuffer,
+    batch_info: BatchInfo,
+    next_samples: Callable[[bool], list[ModelInput] | None],
+) -> None:
+    while len(_get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
+        samples = next_samples(True)
+        if samples is None:
+            break
+        buffer.put(samples)
+
+
+@BatchingPlugin("dynamic_padding_free").register("generate_batch")
+def generate_dynamic_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
+    micro_batch_sample_counts = _get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)
+    if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
+        return None
+
+    batch = []
+    cutoff_len = batch_info["cutoff_len"]
+
+    for num_samples in micro_batch_sample_counts:
+        samples = buffer.get(num_samples)
+        packed_batch = _pack_padding_free_samples(samples, cutoff_len)
+        if packed_batch is None:
+            return None
+
+        batch.append(packed_batch)
+
+    return batch

src/llamafactory/v1/plugins/trainer_plugins/distributed/hub.py

@@ -61,6 +61,9 @@ def load_checkpoint_fsdp2(model: HFModel, optimizer: torch.optim.Optimizer, ckpt
 
 @DistributedPlugin("deepspeed").register()
 def shard_model_deepspeed(model: HFModel, dist_config: PluginConfig, **kwargs) -> HFModel:
+    if dist_config.get("cp_size", 1) > 1:
+        raise ValueError("CP currently requires `dist_config.name: fsdp2`.")
+
     from .deepspeed import DeepSpeedEngine
 
     return DeepSpeedEngine(

src/llamafactory/v1/utils/helper.py

@@ -13,22 +13,46 @@
 # limitations under the License.
 
 
+import random
+
+import numpy as np
 import torch
 from transformers import PreTrainedTokenizer
 from transformers import set_seed as hf_set_seed
 
+from ..accelerator.helper import is_torch_npu_available
 from ..accelerator.interface import DistributedInterface
 from .constants import IGNORE_INDEX
 from .types import BatchInput, ModelInput, Processor, Tensor
 
 
-def set_seed(seed: int) -> None:
+def enable_full_determinism(seed: int) -> None:
+    """Enable full deterministic mode for reproducible distributed training."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.use_deterministic_algorithms(True, warn_only=True)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.enabled = False
+    if is_torch_npu_available():
+        torch.npu.manual_seed(seed)
+        torch.npu.manual_seed_all(seed)
+
+
+def set_seed(seed: int, full_determinism: bool = False) -> None:
     """Set seed for reproducibility.
 
     Args:
         seed: Random seed.
+        full_determinism: Whether to enable full deterministic mode.
     """
-    hf_set_seed(seed)
+    if full_determinism:
+        enable_full_determinism(seed)
+    else:
+        hf_set_seed(seed)
 
 
 def is_tokenizer(processor: Processor) -> bool:

src/llamafactory/v1/utils/objects.py

@@ -33,6 +33,10 @@ class StatefulBuffer:
     def size(self) -> int:
         return self._buffer_size
 
+    @property
+    def samples(self) -> list[ModelInput]:
+        return self._buffer
+
     def put(self, samples: list[ModelInput]) -> None:
         """Add samples to the buffer."""
         num_tokens = sum(len(sample["input_ids"]) for sample in samples)

src/llamafactory/v1/utils/types.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Iterator
+from enum import StrEnum, unique
 from typing import TYPE_CHECKING, Any, Literal, NamedTuple, NotRequired, TypedDict, Union
 
 
@@ -54,6 +54,13 @@ else:
     ProcessGroup = None
 
 
+@unique
+class AttentionFunction(StrEnum):
+    EAGER = "eager"
+    SDPA = "sdpa"
+    FLASH_ATTENTION_2 = "flash_attention_2"
+
+
 class DatasetInfo(TypedDict, total=False):
     path: str
     """Local file path."""
@@ -171,8 +178,6 @@ class BatchInfo(TypedDict):
     """Number of micro batches."""
     cutoff_len: int
     """Cutoff length."""
-    data_iter: Iterator[list[ModelInput]]
-    """Data iterator."""
 
 
 class ModelOutput(NamedTuple):

tests_v1/accelerator/test_interface.py

@@ -58,10 +58,3 @@ def test_multi_device():
     master_port = find_available_port()
     world_size = 2
     mp.spawn(_all_reduce_tests, args=(world_size, master_port), nprocs=world_size)
-
-
-if __name__ == "__main__":
-    """
-    python tests_v1/accelerator/test_interface.py
-    """
-    test_all_device()

tests_v1/config/test_args_parser.py

@@ -70,13 +70,3 @@ def test_get_args_from_yaml(tmp_path: Path):
         assert training_args.bf16 is False
         assert training_args.dist_config is None
         assert sample_args.sample_backend == "hf"
-
-
-if __name__ == "__main__":
-    """
-    python -m tests_v1.config.test_args_parser
-    """
-    import tempfile
-
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        test_get_args_from_yaml(tmp_path=Path(tmp_dir))

tests_v1/core/test_data_engine.py

@@ -30,10 +30,3 @@ def test_map_dataset(num_samples: int):
     for index in indexes:
         print(data_engine[index])
         assert data_engine[index] == {"_dataset_name": "default", **original_data[index]}
-
-
-if __name__ == "__main__":
-    """
-    python -m tests_v1.core.test_data_engine
-    """
-    test_map_dataset(1)

tests_v1/core/test_model_loader.py

@@ -41,11 +41,3 @@ def test_tiny_qwen_with_kernel_plugin():
         assert model_engine.model.model.layers[0].input_layernorm.forward.__code__ != npu_rms_norm_forward.__code__
 
     assert "Qwen3ForCausalLM" in model_engine.model.__class__.__name__
-
-
-if __name__ == "__main__":
-    """
-    python -m tests_v1.core.test_model_loader
-    """
-    test_tiny_qwen()
-    test_tiny_qwen_with_kernel_plugin()

tests_v1/core/utils/test_batching.py

@@ -16,6 +16,164 @@ from llamafactory.v1.config import DataArguments, ModelArguments, TrainingArgume
 from llamafactory.v1.core.data_engine import DataEngine
 from llamafactory.v1.core.model_engine import ModelEngine
 from llamafactory.v1.core.utils.batching import BatchGenerator
+from llamafactory.v1.plugins.trainer_plugins.batching import (
+    BatchingPlugin,
+    _get_dynamic_micro_batch_sizes,
+    _get_dynamic_padding_free_micro_batch_sizes,
+)
+from llamafactory.v1.utils.constants import IGNORE_INDEX
+from llamafactory.v1.utils.objects import StatefulBuffer
+
+
+def _make_model_input(length: int, start: int = 0):
+    input_ids = list(range(start, start + length))
+    return {
+        "input_ids": input_ids,
+        "attention_mask": [1] * length,
+        "labels": input_ids.copy(),
+        "loss_weights": [1.0] * length,
+    }
+
+
+class _RestartableDataProvider:
+    def __init__(self, batches):
+        self.batches = batches
+        self.num_iters = 0
+
+    def __iter__(self):
+        self.num_iters += 1
+        return iter(self.batches)
+
+
+def test_padding_free():
+    buffer = StatefulBuffer()
+    # Input samples:
+    #   sample 0 input_ids: [0, 1]
+    #   sample 1 input_ids: [10, 11, 12, 13]
+    buffer.put([_make_model_input(2, 0), _make_model_input(4, 10)])
+    batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 3}
+
+    batch = BatchingPlugin("padding_free").generate_batch(buffer, batch_info)
+
+    # Output batch:
+    #   sample 1 is truncated to [10, 11, 12]
+    #   both samples are packed into one sequence: [[0, 1, 10, 11, 12]]
+    assert batch is not None
+    assert len(batch) == 1
+    assert batch[0]["input_ids"].shape == (1, 5)
+    assert batch[0]["input_ids"].tolist() == [[0, 1, 10, 11, 12]]
+    assert batch[0]["attention_mask"].tolist() == [[1, 1, 1, 1, 1]]
+    assert batch[0]["position_ids"].tolist() == [[0, 1, 0, 1, 2]]
+    assert batch[0]["labels"].tolist() == [[0, 1, IGNORE_INDEX, 11, 12]]
+    assert batch[0]["loss_weights"].tolist() == [[1.0, 1.0, 0.0, 1.0, 1.0]]
+    assert len(buffer) == 0
+
+
+def test_batching_plugin_data_provider_batch_sizes():
+    batch_info = {
+        "micro_batch_size": 2,
+        "num_micro_batch": 3,
+        "cutoff_len": 10,
+    }
+
+    assert BatchingPlugin("padding_free").get_data_provider_batch_size(batch_info) == 6
+    assert BatchingPlugin("dynamic_batching").get_data_provider_batch_size(batch_info) == 1
+    assert BatchingPlugin("dynamic_padding_free").get_data_provider_batch_size(batch_info) == 1
+
+
+def test_dynamic_batching():
+    # Input samples:
+    #   sample lengths: [3, 4, 6, 2, 8, 9]
+    #   input_ids:
+    #     [0, 1, 2]
+    #     [10, 11, 12, 13]
+    #     [20, 21, 22, 23, 24, 25]
+    #     [30, 31]
+    #     [40, 41, 42, 43, 44, 45, 46, 47]
+    #     [50, 51, 52, 53, 54, 55, 56, 57, 58]
+    samples = [
+        _make_model_input(3, 0),
+        _make_model_input(4, 10),
+        _make_model_input(6, 20),
+        _make_model_input(2, 30),
+        _make_model_input(8, 40),
+        _make_model_input(9, 50),
+    ]
+    batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
+
+    # Dynamic batching output plan:
+    #   dynamic batching reads one sample at a time and uses cutoff_len * micro_batch_size
+    #   as the padded-token budget for one training micro batch.
+    #   [3, 4, 6] fits within budget 20 as shape [3, 6]; adding [2] would exceed it.
+    assert _get_dynamic_micro_batch_sizes(samples, batch_info) == [3]
+
+    buffer = StatefulBuffer()
+    buffer.put(samples)
+    batch = BatchingPlugin("dynamic_batching").generate_batch(buffer, batch_info)
+
+    assert batch is not None
+    assert len(batch) == 1
+    assert batch[0]["input_ids"].shape == (3, 6)
+    assert batch[0]["input_ids"].tolist()[0] == [0, 1, 2, 0, 0, 0]
+    assert len(buffer) == 3
+
+
+def test_dynamic_batching_returns_none_when_token_budget_is_incomplete():
+    buffer = StatefulBuffer()
+    # Input buffer:
+    #   only one sample with length [6].
+    #   cutoff_len * micro_batch_size gives a padded-token budget of 20.
+    #   this buffer has not filled the budget and has no next sample to prove overflow,
+    #   so dynamic batching cannot produce a batch yet.
+    buffer.put([_make_model_input(6, 0)])
+    batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
+
+    assert _get_dynamic_micro_batch_sizes(buffer.samples, batch_info) == []
+    assert BatchingPlugin("dynamic_batching").generate_batch(buffer, batch_info) is None
+    # Batch generation does not read from the data iterator. It only returns None and keeps
+    # existing samples in the buffer; BatchGenerator._fill_buffer handles refilling.
+    assert len(buffer) == 1
+
+
+def test_dynamic_batching_fill_buffer_restarts_until_micro_batch_is_complete():
+    # Input data provider:
+    #   each iterator pass yields one sample with length [6].
+    #   each yielded item is a list[ModelInput], matching BatchGenerator._next_samples.
+    #   _fill_buffer keeps restarting the iterator until the next appended sample
+    #   proves that the previous dynamic micro batch has reached its budget boundary.
+    samples = [_make_model_input(6, 0)]
+    data_provider = _RestartableDataProvider([[sample] for sample in samples])
+
+    batch_generator = BatchGenerator.__new__(BatchGenerator)
+    batch_generator.batching_strategy = "dynamic_batching"
+    batch_generator.micro_batch_size = 2
+    batch_generator.num_micro_batch = 1
+    batch_generator._buffer = StatefulBuffer()
+    batch_generator._data_provider = data_provider
+    batch_generator._data_iter = iter(data_provider)
+    batch_generator._batch_info = {
+        "micro_batch_size": 2,
+        "num_micro_batch": 1,
+        "cutoff_len": 10,
+    }
+
+    batch_generator._fill_buffer()
+
+    # Filled buffer after restart:
+    #   existing buffer [6, 6, 6] is kept; the fourth [6] remains for the next batch
+    #   because adding it to the first dynamic micro batch would exceed the budget.
+    assert data_provider.num_iters == 4
+    assert _get_dynamic_micro_batch_sizes(batch_generator._buffer.samples, batch_generator._batch_info) == [3]
+
+    batch = batch_generator._generate_batch()
+
+    # Output batch:
+    #   dynamic batching returns [micro_batch_0]
+    #   micro_batch_0 consumes [6, 6, 6] => 3 samples, padded to shape [3, 6].
+    assert batch is not None
+    assert len(batch) == 1
+    assert batch[0]["input_ids"].shape == (3, 6)
+    assert len(batch_generator._buffer) == 1
 
 
 def test_normal_batching():
@@ -45,8 +203,166 @@ def test_normal_batching():
     assert batch[0]["input_ids"].shape == (4, 10)
 
 
-if __name__ == "__main__":
+def test_dynamic_padding_free():
+    """Test core logic of dynamic padding free strategy: pack samples by total token budget without padding."""
+    # Construct test samples (lengths: 3, 4, 6, 2, 8, 9)
+    # input_ids breakdown:
+    #   sample 0: [0,1,2] (length=3)
+    #   sample 1: [10,11,12,13] (length=4)
+    #   sample 2: [20,21,22,23,24,25] (length=6)
+    #   sample 3: [30,31] (length=2)
+    #   sample 4: [40-47] (length=8)
+    #   sample 5: [50-58] (length=9)
+    samples = [
+        _make_model_input(3, 0),
+        _make_model_input(4, 10),
+        _make_model_input(6, 20),
+        _make_model_input(2, 30),
+        _make_model_input(8, 40),
+        _make_model_input(9, 50),
+    ]
+    # Batch config: micro_batch_size=2 → token budget = cutoff_len * micro_batch_size = 10*2=20
+    batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
+
+    # Budget=20: 3+4+6+2=15 ≤20 (adding 8 would exceed) → first 4 samples are selected
+    assert _get_dynamic_padding_free_micro_batch_sizes(samples, batch_info) == [4]
+
+    buffer = StatefulBuffer()
+    buffer.put(samples)
+    batch = BatchingPlugin("dynamic_padding_free").generate_batch(buffer, batch_info)
+
+    assert batch is not None
+    assert len(batch) == 1  # num_micro_batch=1
+    packed_batch = batch[0]
+
+    # Total packed length: 3+4+6+2=15 → input_ids shape = (1,15) (no padding)
+    assert packed_batch["input_ids"].shape == (1, 15)
+
+    # Verify input_ids concatenation (first label of non-initial samples set to IGNORE_INDEX)
+    assert packed_batch["input_ids"].tolist() == [
+        [
+            0,
+            1,
+            2,  # Sample 0
+            10,
+            11,
+            12,
+            13,  # Sample 1
+            20,
+            21,
+            22,
+            23,
+            24,
+            25,  # Sample 2
+            30,
+            31,
+        ]  # Sample 3
+    ]
+
+    # Verify labels (first token of non-initial samples is IGNORE_INDEX)
+    assert packed_batch["labels"].tolist() == [
+        [
+            0,
+            1,
+            2,  # Sample 0
+            IGNORE_INDEX,
+            11,
+            12,
+            13,  # Sample 1
+            IGNORE_INDEX,
+            21,
+            22,
+            23,
+            24,
+            25,  # Sample 2
+            IGNORE_INDEX,
+            31,
+        ]  # Sample 3
+    ]
+
+    # Verify attention_mask
+    assert packed_batch["attention_mask"].tolist() == [[1] * 15]
+
+    # Verify position_ids
+    assert packed_batch["position_ids"].tolist() == [
+        [
+            0,
+            1,
+            2,  # Sample 0
+            0,
+            1,
+            2,
+            3,  # Sample 1
+            0,
+            1,
+            2,
+            3,
+            4,
+            5,  # Sample 2
+            0,
+            1,
+        ]  # Sample 3
+    ]
+
+    # Verify remaining samples in buffer: 6-4=2 samples (length 8,9)
+    assert len(buffer) == 2
+
+
+def test_dynamic_padding_free_returns_none_when_token_budget_is_incomplete():
+    buffer = StatefulBuffer()
+    buffer.put([_make_model_input(6, 0)])
+    batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
+
+    assert _get_dynamic_micro_batch_sizes(buffer.samples, batch_info) == []
+    assert BatchingPlugin("dynamic_padding_free").generate_batch(buffer, batch_info) is None
+    # Batch generation does not read from the data iterator. It only returns None and keeps
+    # existing samples in the buffer; BatchGenerator._fill_buffer handles refilling.
+    assert len(buffer) == 1
+
+
+def test_dynamic_padding_free_fill_buffer_restarts_until_micro_batch_is_complete():
+    """Test fill_buffer logic for dynamic_padding_free: restart data iterator until token budget is full.
+
+    Data provider yields one sample of length 6 per iteration.
+    _fill_buffer keeps restarting iterator until next sample exceeds budget.
+    Budget = 2 * 10 = 20 tokens.
+    3 samples (6*3=18) fit; 4th sample (24) exceeds budget.
+    So buffer will have 4 samples after fill_buffer.
     """
-    python -m tests_v1.core.utils.test_batching
-    """
-    test_normal_batching()
+    samples = [_make_model_input(6, 0)]
+    data_provider = _RestartableDataProvider([[sample] for sample in samples])
+
+    batch_generator = BatchGenerator.__new__(BatchGenerator)
+    batch_generator.batching_strategy = "dynamic_padding_free"
+    batch_generator.micro_batch_size = 2
+    batch_generator.num_micro_batch = 1
+    batch_generator._buffer = StatefulBuffer()
+    batch_generator._data_provider = data_provider
+    batch_generator._data_iter = iter(data_provider)
+    batch_generator._batch_info = {
+        "micro_batch_size": 2,
+        "num_micro_batch": 1,
+        "cutoff_len": 10,
+    }
+
+    # Execute fill buffer (will restart iterator multiple times to collect enough samples)
+    batch_generator._fill_buffer()
+
+    # Buffer after restarts:
+    #   3 samples can fit (18 tokens)
+    #   4th sample is kept in buffer for next batch
+    #   => num_iters = 4
+    assert data_provider.num_iters == 4
+    assert _get_dynamic_padding_free_micro_batch_sizes(
+        batch_generator._buffer.samples, batch_generator._batch_info
+    ) == [3]
+
+    batch = batch_generator._generate_batch()
+
+    # Output batch:
+    #   dynamic_padding_free returns [micro_batch_0]
+    #   3 samples packed into shape [1, 18]
+    assert batch is not None
+    assert len(batch) == 1
+    assert batch[0]["input_ids"].shape == (1, 18)
+    assert len(batch_generator._buffer) == 1

tests_v1/core/utils/test_rendering.py

@@ -227,17 +227,3 @@ def test_process_dpo_samples():
     assert model_inputs[0]["token_type_ids"] == [1] * len(hf_inputs) + [2] * len(hf_inputs)
     assert model_inputs[0]["extra_info"] == "test"
     assert model_inputs[0]["_dataset_name"] == "default"
-
-
-if __name__ == "__main__":
-    """
-    python -m tests_v1.core.utils.test_rendering
-    """
-    test_chatml_rendering()
-    test_chatml_parse()
-    test_chatml_rendering_remote(16)
-    test_qwen3_nothink_rendering()
-    test_qwen3_nothink_parse()
-    test_qwen3_nothink_rendering_remote(16)
-    test_process_sft_samples()
-    test_process_dpo_samples()

tests_v1/plugins/data_plugins/test_converter.py

@@ -117,12 +117,3 @@ def test_pair_converter(num_samples: int):
             ],
         }
         assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
-
-
-if __name__ == "__main__":
-    """
-    python -m tests_v1.plugins.data_plugins.test_converter
-    """
-    test_alpaca_converter(1)
-    test_sharegpt_converter()
-    test_pair_converter(1)

tests_v1/plugins/model_plugins/test_init_plugin.py

@@ -52,12 +52,3 @@ def test_init_on_default():
     )
     model_engine = ModelEngine(model_args=model_args)
     assert model_engine.model.device == DistributedInterface().current_device
-
-
-if __name__ == "__main__":
-    """
-    python tests_v1/plugins/model_plugins/test_init_plugin.py
-    """
-    test_init_on_meta()
-    test_init_on_rank0()
-    test_init_on_default()

tests_v1/sampler/test_cli_sampler.py

@@ -35,10 +35,3 @@ def test_sync_sampler():
         "role": "assistant",
         "content": [{"type": "text", "value": "This is a test."}],
     }
-
-
-if __name__ == "__main__":
-    """
-    python tests_v1/sampler/test_cli_sampler.py
-    """
-    test_sync_sampler()