fix: projector lookup for gemma4 modules (#10382)

Co-authored-by: yiluoAK_47 <yiluoAK_47@163.com>

.ai/CLAUDE.md

@@ -0,0 +1,105 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Commands
+
+```bash
+# Code style (auto-fix)
+make style
+
+# Code quality check (no modifications)
+make quality
+
+# Run all tests
+make test
+
+# Run a single test file
+WANDB_DISABLED=true pytest -vv --import-mode=importlib tests/path/to/test_file.py
+
+# Run tests matching a pattern
+WANDB_DISABLED=true pytest -vv --import-mode=importlib tests/ -k "test_name"
+
+# License header check
+make license
+
+# Build package
+make build
+```
+
+The project uses `uv` as the preferred package manager. Commands automatically use `uv run` / `uvx` if `uv` is available.
+
+## Architecture
+
+LlamaFactory has two parallel architectures controlled by the `USE_V1` environment variable:
+
+- **v0 (default):** `api, webui > chat, eval, train > data, model > hparams > extras`
+- **v1 (experimental, `USE_V1=1`):** `trainers > core > accelerator, plugins, config > utils`
+
+Most active development happens in v0. The v1 architecture lives in `src/llamafactory/v1/`.
+
+### Entry Points
+
+CLI entry point is `llamafactory-cli` / `lmf` → `src/llamafactory/cli.py:main()`, which dispatches to `launcher.py` based on `USE_V1`.
+
+Available subcommands: `train`, `chat`, `api`, `export`, `webchat`, `webui`, `env`, `version`, `help`.
+
+### Training Flow (v0)
+
+```
+run_exp() [tuner.py]
+  → read_args() → parse YAML/JSON config
+  → get_train_args() → produces typed argument dataclasses
+  → routes to: run_sft / run_dpo / run_ppo / run_rm / run_pt / run_kto
+  → optional: export_model()
+```
+
+Training is invoked with a YAML config: `llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml`
+
+### Configuration System
+
+All training parameters are YAML/JSON config files. Argument parsing in `src/llamafactory/hparams/parser.py` produces four typed dataclasses:
+- `ModelArguments` — model/tokenizer selection, quantization
+- `DataArguments` — datasets, templates, preprocessing
+- `FinetuningArguments` — LoRA rank/target, training method (sft/dpo/ppo/rm/pt/kto)
+- `TrainingArguments` — extends HuggingFace's `TrainingArguments`
+
+### Key Modules
+
+| Module | Purpose |
+|--------|---------|
+| `src/llamafactory/model/loader.py` | Loads model + tokenizer; applies quantization, LoRA, patches |
+| `src/llamafactory/model/patcher.py` | Model-specific compatibility patches |
+| `src/llamafactory/data/template.py` | Prompt templates; `TEMPLATES` dict maps model family → format |
+| `src/llamafactory/data/mm_plugin.py` | Multi-modal (image/video/audio) data handling |
+| `src/llamafactory/data/processor/` | Per-stage data processors (supervised, pairwise, pretrain, etc.) |
+| `src/llamafactory/train/sft/` | SFT trainer; other stages follow same structure |
+| `src/llamafactory/chat/` | Inference engines: `hf_engine`, `vllm_engine`, `sglang_engine`, `kt_engine` |
+| `src/llamafactory/extras/constants.py` | Enums and constants used across the project |
+
+### Adding Support for a New Model
+
+1. Add a prompt template to `src/llamafactory/data/template.py` in the `TEMPLATES` dict
+2. Add any necessary model patches in `src/llamafactory/model/patcher.py`
+3. Add multi-modal support in `src/llamafactory/data/mm_plugin.py` if needed
+
+### Distributed Training
+
+Multi-GPU automatically uses `torchrun`. Additional backends:
+- **Ray:** Optional Ray cluster support
+- **HyperParallel FSDP2:** `src/llamafactory/train/hyper_parallel/`
+- **Megatron-core:** `src/llamafactory/train/mca/`
+
+### Testing
+
+- `tests/` — v0 tests; `tests_v1/` — v1 tests
+- Most training tests require GPU hardware
+- pytest markers: `@pytest.mark.slow`, `@pytest.mark.runs_on(['cuda'])`
+- Always set `WANDB_DISABLED=true` when running tests
+
+### Code Style
+
+- Ruff for linting and formatting (line length 119, Google-style docstrings)
+- Python 3.11+ syntax
+- Double quotes for strings
+- All new files must include Apache 2.0 license header (checked by `make license`)

.github/workflows/tests_npu.yml

@@ -49,6 +49,12 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v6
 
+      - name: Set nginx-cache for Ascend CI
+        run: |
+          sed -Ei 's@(ports|archive).ubuntu.com@cache-service.nginx-pypi-cache.svc.cluster.local:8081@g' /etc/apt/sources.list
+          pip config set global.index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple
+          pip config set global.trusted-host cache-service.nginx-pypi-cache.svc.cluster.local
+
       - name: Install uv
         uses: astral-sh/setup-uv@v7
         with:

examples/v1/train_freeze/train_freeze_sft.yaml

@@ -1,5 +1,4 @@
 model: Qwen/Qwen3-4B
-trust_remote_code: true
 model_class: llm
 
 template: qwen3_nothink

examples/v1/train_full/train_full_deepspeed.yaml

@@ -1,5 +1,4 @@
 model: Qwen/Qwen3-0.6B
-
 model_class: llm
 
 template: qwen3_nothink

examples/v1/train_full/train_full_fsdp2.yaml

@@ -1,5 +1,4 @@
 model: Qwen/Qwen3-0.6B
-trust_remote_code: true
 model_class: llm
 
 template: qwen3_nothink

examples/v1/train_full/train_full_ulysses_cp.yaml

@@ -0,0 +1,23 @@
+model: Qwen/Qwen3-0.6B
+trust_remote_code: true
+model_class: llm
+
+template: qwen3_nothink
+
+# FSDP Config
+dist_config:
+  name: fsdp2
+  dcp_path: null
+  cp_mode: ulysses
+  cp_size: 2
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_ulysses_cp
+micro_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+bf16: false
+max_steps: 10

examples/v1/train_lora/train_lora_sft.yaml

@@ -1,5 +1,4 @@
 model: Qwen/Qwen3-4B
-trust_remote_code: true
 model_class: llm
 
 template: qwen3_nothink
@@ -28,7 +27,6 @@ train_dataset: data/v1_sft_demo.yaml
 ### training
 output_dir: ./outputs/test_lora
 micro_batch_size: 1
-global_batch_size: 4
 cutoff_len: 2048
 learning_rate: 1.0e-4
 bf16: true

examples/v1/train_lora/train_lora_sft_rank0.yaml

@@ -0,0 +1,40 @@
+model: Qwen/Qwen3-4B
+model_class: llm
+
+template: qwen3_nothink
+
+# PEFT Configuration
+peft_config:
+  name: lora
+  r: 16
+  lora_alpha: 32
+  lora_dropout: 0.05
+  target_modules: all
+
+# Kernel Config
+kernel_config:
+  name: auto
+  include_kernels: auto
+
+# FSDP Config
+dist_config:
+  name: fsdp2
+  dcp_path: null
+
+init_config:
+  name: init_on_rank0
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: ./outputs/test_lora
+micro_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+bf16: true
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

examples/v1/train_qlora/quantization.yaml

@@ -1,5 +1,4 @@
 model: Qwen/Qwen3-0.6B
-trust_remote_code: true
 model_class: llm
 
 template: qwen3_nothink

src/llamafactory/data/collator.py

@@ -380,6 +380,19 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             for i, feature in enumerate(features):
                 feature["token_type_ids"] = token_type_ids[i]
 
+        if "mm_token_type_ids" in mm_inputs: # need tensor-like for gemma4
+            mm_token_type_ids = mm_inputs.pop("mm_token_type_ids")
+            max_len = max(len(ids) for ids in mm_token_type_ids)
+            padded = []
+            for ids in mm_token_type_ids:
+                pad_len = max_len - len(ids)
+                if self.tokenizer.padding_side == "right":
+                    padded.append(ids + [0] * pad_len)
+                else:
+                    padded.append([0] * pad_len + ids)
+
+            mm_inputs["mm_token_type_ids"] = torch.tensor(padded, dtype=torch.long)
+
         features: dict[str, torch.Tensor] = super().__call__(features)
 
         bsz, seq_len = features["input_ids"].shape[:2]

src/llamafactory/data/mm_plugin.py

@@ -607,6 +607,194 @@ class Gemma3nPlugin(Gemma3Plugin):
         return messages
 
 
+@dataclass
+class Gemma4Plugin(BasePlugin):
+    r"""Plugin for the Gemma4 multimodal model."""
+
+    @override
+    def _regularize_videos(self, videos: list["VideoInput"], **kwargs) -> "RegularizedVideoOutput":
+        r"""Regularize videos, also tracking per-video FPS and frame indices for timestamp generation."""
+        results, fps_per_video, durations, frames_indices = [], [], [], []
+        for video in videos:
+            frames: list[ImageObject] = []
+            if _check_video_is_nested_images(video):
+                frames = video
+                fps_per_video.append(kwargs.get("video_fps", 2.0))
+                durations.append(len(frames) / kwargs.get("video_fps", 2.0))
+                frames_indices.append(list(range(len(frames))))
+            else:
+                container = av.open(video, "r")
+                video_stream = next(stream for stream in container.streams if stream.type == "video")
+                sample_indices = self._get_video_sample_indices(video_stream, **kwargs)
+                original_fps = float(video_stream.average_rate)
+                # for correctly calculate timestamps
+                frames_indices.append([idx / original_fps * kwargs.get("video_fps", 2.0) for idx in sample_indices])
+                container.seek(0)
+                for frame_idx, frame in enumerate(container.decode(video_stream)):
+                    if frame_idx in sample_indices:
+                        frames.append(frame.to_image())
+
+                if video_stream.duration is None:
+                    durations.append(len(frames) / kwargs.get("video_fps", 2.0))
+                else:
+                    durations.append(float(video_stream.duration * video_stream.time_base))
+
+            frames = self._regularize_images(frames, **kwargs)["images"]
+            results.append(frames)
+
+        return {"videos": results, "fps_per_video": fps_per_video, "durations": durations, "frames_indices": frames_indices}
+
+    @override
+    def _get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: "MMProcessor",
+    ) -> dict[str, Union[list[int], "torch.Tensor"]]:
+        image_processor = getattr(processor, "image_processor", None)
+        video_processor = getattr(processor, "video_processor", None)
+        feature_extractor = getattr(processor, "feature_extractor", None)
+        mm_inputs = {}
+
+        if len(images) != 0:
+            regularized = self._regularize_images(
+                images,
+                image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
+                image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
+            )["images"]
+            mm_inputs.update(image_processor(regularized, return_tensors="pt"))
+
+        if len(videos) != 0:
+            video_data = self._regularize_videos(
+                videos,
+                image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
+                image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
+                video_fps=getattr(processor, "video_fps", 2.0),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
+            )
+            video_metadata = [
+                {"fps": getattr(processor, "video_fps", 2.0), "duration": duration, "total_num_frames": len(video), "frames_indices": sample_indices}
+                for video, duration, sample_indices in zip(video_data["videos"], video_data["durations"], video_data["frames_indices"])
+            ]
+            mm_inputs.update(
+                video_processor(
+                    videos=video_data["videos"],
+                    video_metadata=video_metadata,
+                    return_tensors="pt",
+                    return_metadata=True,
+                    do_sample_frames=False,
+                )
+            )
+
+        if len(audios) != 0: # only for gemma4n
+            audios = self._regularize_audios(
+                audios,
+                sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
+            )["audios"]
+
+            mm_inputs.update(
+                feature_extractor(
+                audios,
+                padding="max_length",
+                return_tensors="pt",
+            )
+        )
+
+        return mm_inputs
+
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        self._validate_messages(messages, images, videos, audios)
+        messages = deepcopy(messages)
+
+        boi_token: str = getattr(processor, "boi_token")
+        eoi_token: str = getattr(processor, "eoi_token")
+        boa_token: str = getattr(processor, "boa_token")
+        eoa_token: str = getattr(processor, "eoa_token")
+        image_token: str = getattr(processor, "image_token")
+        video_token: str = getattr(processor, "video_token")
+        audio_token: str = getattr(processor, "audio_token")
+
+        if self.expand_mm_tokens:
+            mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+            num_image_soft_tokens: list[int] = list(
+                mm_inputs.get("num_soft_tokens_per_image", [getattr(processor, "image_seq_length", 256)] * len(images))
+            )
+            num_video_soft_tokens: list[int] = list(mm_inputs.get("num_soft_tokens_per_video", [1] * len(videos)))
+            video_metadata = mm_inputs.get("video_metadata", [])
+        else:
+            num_image_soft_tokens = [1] * len(images)
+            num_video_soft_tokens = [1] * len(videos)
+            video_metadata = [None] * len(videos)
+
+        audio_iter = iter(audios)
+        image_iter = iter(num_image_soft_tokens)
+        video_iter = iter(zip(num_video_soft_tokens, video_metadata))
+
+        for message in messages:
+            content = message["content"]
+
+            while IMAGE_PLACEHOLDER in content:
+                n = next(image_iter)
+                content = content.replace(IMAGE_PLACEHOLDER, f"{boi_token}{image_token * n}{eoi_token}", 1)
+
+            while VIDEO_PLACEHOLDER in content:
+                num_soft_tokens_per_frame, metadata = next(video_iter)
+                if self.expand_mm_tokens:
+                    timestamp_strs = [f"{int(t // 60):02d}:{int(t % 60):02d}" for t in metadata.timestamps]
+                    frame_strs = [f"{ts} {boi_token}{video_token * num_soft_tokens_per_frame}{eoi_token}" for ts in timestamp_strs]
+                    video_str = " ".join(frame_strs)
+                else:
+                    video_str = f"{boi_token}{video_token * num_soft_tokens_per_frame}{eoi_token}"
+                content = content.replace(VIDEO_PLACEHOLDER, video_str, 1)
+
+            while AUDIO_PLACEHOLDER in content:
+                current_audio = next(audio_iter)
+                if self.expand_mm_tokens:
+                    num_audio_tokens = processor._compute_audio_num_tokens(current_audio, processor.feature_extractor.sampling_rate)
+                    audio_str = f"{boa_token}{audio_token * num_audio_tokens}{eoa_token}"
+                else:
+                    audio_str = f"{boa_token}{audio_token}{eoa_token}"
+
+                content = content.replace(AUDIO_PLACEHOLDER, audio_str, 1)
+
+            message["content"] = content
+
+        return messages
+
+    @override
+    def get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        imglens: list[int],
+        vidlens: list[int],
+        audlens: list[int],
+        batch_ids: list[list[int]],
+        processor: Optional["MMProcessor"],
+    ) -> dict[str, Union[list[int], "torch.Tensor"]]:
+        self._validate_input(processor, images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+        # Pop metadata keys that must not be passed to the model.
+        for key in ("num_soft_tokens_per_image", "num_soft_tokens_per_video", "video_metadata",
+                    "_gemma4_fps_per_video", "_gemma4_frames_indices", "_gemma4_num_audio_soft_tokens"):
+            mm_inputs.pop(key, None)
+
+        mm_inputs["mm_token_type_ids"] = processor.create_mm_token_type_ids(batch_ids)
+
+        return mm_inputs
+
+
 @dataclass
 class InternVLPlugin(BasePlugin):
     @override
@@ -1489,10 +1677,11 @@ class Qwen2VLPlugin(BasePlugin):
 
     @override
     def _regularize_videos(self, videos: list["VideoInput"], **kwargs) -> "RegularizedVideoOutput":
-        results, fps_per_video, durations = [], [], []
+        results, fps_per_video, durations, frames_indices = [], [], [], []
         for video in videos:
             frames: list[ImageObject] = []
             if _check_video_is_nested_images(video):
+                # we assume already sample frames from videos
                 for frame in video:
                     if not is_valid_image(frame) and not isinstance(frame, dict) and not os.path.exists(frame):
                         raise ValueError("Invalid image found in video frames.")
@@ -1500,10 +1689,14 @@ class Qwen2VLPlugin(BasePlugin):
                 frames = video
                 fps_per_video.append(kwargs.get("video_fps", 2.0))
                 durations.append(len(frames) / kwargs.get("video_fps", 2.0))
+                frames_indices.append(list(range(len(frames))))
             else:
                 container = av.open(video, "r")
                 video_stream = next(stream for stream in container.streams if stream.type == "video")
                 sample_indices = self._get_video_sample_indices(video_stream, **kwargs)
+                original_fps = float(video_stream.average_rate)
+                # for qwen3vl video timestamp calculation
+                frames_indices.append([idx / original_fps * kwargs.get("video_fps", 2.0) for idx in sample_indices]) # hack usage when do_sample_frames=False
                 container.seek(0)
                 for frame_idx, frame in enumerate(container.decode(video_stream)):
                     if frame_idx in sample_indices:
@@ -1522,7 +1715,7 @@ class Qwen2VLPlugin(BasePlugin):
             frames = self._regularize_images(frames, **kwargs)["images"]
             results.append(frames)
 
-        return {"videos": results, "fps_per_video": fps_per_video, "durations": durations}
+        return {"videos": results, "fps_per_video": fps_per_video, "durations": durations, "frames_indices": frames_indices}
 
     @override
     def _get_mm_inputs(
@@ -1637,8 +1830,8 @@ class Qwen3VLPlugin(Qwen2VLPlugin):
                 video_maxlen=getattr(processor, "video_maxlen", 128),
             )
             video_metadata = [
-                {"fps": getattr(processor, "video_fps", 24.0), "duration": duration, "total_num_frames": len(video)}
+                {"fps": getattr(processor, "video_fps", 2.0), "duration": duration, "total_num_frames": len(video), "frames_indices": sample_indices}
-                for video, duration in zip(videos["videos"], videos["durations"])
+                for video, duration, sample_indices in zip(videos["videos"], videos["durations"], videos["frames_indices"])
             ]
             mm_inputs.update(
                 video_processor(
@@ -1646,6 +1839,7 @@ class Qwen3VLPlugin(Qwen2VLPlugin):
                     video_metadata=video_metadata,
                     fps=getattr(processor, "video_fps", 2.0),
                     return_metadata=True,
+                    do_sample_frames=False, # avoid changing frames_indices
                 )
             )
             temporal_patch_size: int = getattr(image_processor, "temporal_patch_size", 2)
@@ -1677,7 +1871,7 @@ class Qwen3VLPlugin(Qwen2VLPlugin):
             image_grid_thw = mm_inputs.get("image_grid_thw", [])
             video_grid_thw = mm_inputs.get("video_grid_thw", [])
             num_frames = video_grid_thw[0][0] if len(video_grid_thw) > 0 else 0  # hard code for now
-            video_metadata = mm_inputs.get("video_metadata", {})
+            video_metadata = mm_inputs.get("video_metadata", [])
 
         else:
             image_grid_thw = [None] * len(images)
@@ -2200,8 +2394,9 @@ PLUGINS = {
     "base": BasePlugin,
     "ernie_vl": ErnieVLPlugin,
     "gemma3": Gemma3Plugin,
-    "glm4v": GLM4VPlugin,
     "gemma3n": Gemma3nPlugin,
+    "gemma4": Gemma4Plugin,
+    "glm4v": GLM4VPlugin,
     "intern_vl": InternVLPlugin,
     "kimi_vl": KimiVLPlugin,
     "llama4": Llama4Plugin,

src/llamafactory/data/template.py

@@ -997,6 +997,55 @@ register_template(
 )
 
 
+register_template(
+    name="gemma4",
+    format_user=StringFormatter(slots=["<|turn>user\n{{content}}<turn|>\n<|turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<turn|>\n"]),
+    format_system=StringFormatter(slots=["<|turn>system\n<|think|>{{content}}<turn|>\n"]), #  default thought singal contained
+    format_observation=StringFormatter(
+        slots=["<|turn>tool\n{{content}}<turn|>\n<|turn>model\n"]
+    ), # seem not consistent with the chattemplate
+    format_tools=ToolFormatter(tool_format="gemma4"),
+    format_function=FunctionFormatter(slots=["<|tool>{{content}}<tool|>"], tool_format="gemma4"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<turn|>"],
+    default_system="You are a helpful assistant.", # important for thinking
+    thought_words=("<|channel>thought\n", "<channel|>"),
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(
+        "gemma4",
+        image_token="<|image|>",
+        video_token="<|video|>",
+    ),
+    template_class=ReasoningTemplate,
+)
+
+
+register_template(
+    name="gemma4n",
+    format_user=StringFormatter(slots=["<|turn>user\n{{content}}<turn|>\n<|turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<turn|>\n"]),
+    format_system=StringFormatter(slots=["<|turn>system\n<|think|>{{content}}<turn|>\n"]), #  default thought singal contained
+    format_observation=StringFormatter(
+        slots=["<|turn>tool\n{{content}}<turn|>\n<|turn>model\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="gemma4"),
+    format_function=FunctionFormatter(slots=["<|tool>{{content}}<tool|>"], tool_format="gemma4"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<turn|>"],
+    default_system="You are a helpful assistant.", # important for thinking
+    thought_words=("<|channel>thought\n", "<channel|>"),
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(
+        "gemma4",
+        image_token="<|image|>",
+        video_token="<|video|>",
+        audio_token="<|audio|>",
+    ),
+    template_class=ReasoningTemplate,
+)
+
+
 register_template(
     name="glm4",
     format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),

src/llamafactory/data/tool_utils.py

@@ -209,6 +209,164 @@ class DefaultToolUtils(ToolUtils):
 
         return results
 
+class Gemma4ToolUtils(ToolUtils):
+    r"""Gemma-4 tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        def _format_parameters(properties: dict[str, Any]) -> str:
+            parts: list[str] = []
+            for name, schema in properties.items():
+                item_parts: list[str] = []
+                if schema.get("description"):
+                    item_parts.append(f'description:<|"|>{schema["description"]}<|"|>')
+                if schema.get("type"):
+                    item_parts.append(f'type:<|"|>{str(schema["type"]).upper()}<|"|>')
+                parts.append(f"{name}:{{{','.join(item_parts)}}}")
+
+            return ",".join(parts)
+
+        declarations: list[str] = []
+        for tool in tools:
+            function_data = tool.get("function", tool) if tool.get("type") == "function" else tool
+            declaration = (
+                f"declaration:{function_data['name']}"
+                + "{"
+                + f'description:<|"|>{function_data.get("description", "")}<|"|>'
+            )
+
+            params = function_data.get("parameters")
+            if params:
+                param_parts: list[str] = []
+                if params.get("properties"):
+                    param_parts.append(f"properties:{{{_format_parameters(params['properties'])}}}")
+
+                if params.get("required"):
+                    required_text = ",".join(f'<|"|>{item}<|"|>' for item in params["required"])
+                    param_parts.append(f"required:[{required_text}]")
+
+                if params.get("type"):
+                    param_parts.append(f'type:<|"|>{str(params["type"]).upper()}<|"|>')
+
+                declaration += f",parameters:{{{','.join(param_parts)}}}"
+
+            response_declaration = function_data.get("response")
+            if response_declaration:
+                response_parts: list[str] = []
+                if response_declaration.get("description"):
+                    response_parts.append(f'description:<|"|>{response_declaration["description"]}<|"|>')
+
+                response_type = str(response_declaration.get("type", "")).upper()
+
+                if response_type == "OBJECT":
+                    response_parts.append(f'type:<|"|>{response_type}<|"|>')
+
+                declaration += f",response:{{{','.join(response_parts)}}}"
+
+            declarations.append(declaration + "}")
+
+        return "\n".join(declarations)
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        regex = re.compile(r"<\|tool_call\>call:([^{\s]+)\{(.*?)\}<tool_call\|>", re.DOTALL)
+        matches = re.findall(regex, content)
+        if not matches:
+            return content
+
+        def _parse_arguments(arg_text: str) -> Any:
+            text = arg_text.strip()
+            if not text:
+                return {}
+
+            # `function_formatter` writes dict arguments as `k:v,...` inside `{...}`.
+            # The extractor captures only the inner text, so re-wrap it to parse as JSON object.
+            object_like_text = "{" + text + "}"
+            # Convert Gemma string markers (<|"|>value<|"|>) to valid JSON strings.
+            normalized = re.sub(
+                r"<\|\"\|\>(.*?)<\|\"\|\>",
+                lambda m: json.dumps(m.group(1), ensure_ascii=False),
+                object_like_text,
+                flags=re.DOTALL,
+            )
+            # Quote unquoted object keys so the payload can be parsed by json.loads.
+            normalized = re.sub(r'(^|[{\s,])([A-Za-z_][A-Za-z0-9_]*)(\s*:)', r'\1"\2"\3', normalized)
+            try:
+                return json.loads(normalized)
+            except json.JSONDecodeError:
+                pass
+
+            try:
+                return json.loads(text)
+            except json.JSONDecodeError:
+                return text
+
+        results: list[FunctionCall] = []
+        for name, arg_block in matches:
+            parsed_arguments = _parse_arguments(arg_block)
+            if isinstance(parsed_arguments, str):
+                arguments = parsed_arguments
+            else:
+                arguments = json.dumps(parsed_arguments, ensure_ascii=False)
+            results.append(FunctionCall(name.strip(), arguments))
+
+        return results
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        def _format_argument(argument: Any, escape_keys: bool = True) -> str:
+            if isinstance(argument, str):
+                return f'<|"|>{argument}<|"|>'
+
+            if isinstance(argument, bool):
+                return "true" if argument else "false"
+
+            if isinstance(argument, dict):
+                items: list[str] = []
+                for key in sorted(argument.keys()):
+                    formatted_key = f'<|"|>{key}<|"|>' if escape_keys else str(key)
+                    formatted_value = _format_argument(argument[key], escape_keys=escape_keys)
+                    items.append(f"{formatted_key}:{formatted_value}")
+                return "{" + ",".join(items) + "}"
+
+            if isinstance(argument, (list, tuple)):
+                return "[" + ",".join(_format_argument(item, escape_keys=escape_keys) for item in argument) + "]"
+
+            if argument is None:
+                return "null"
+
+            return str(argument)
+
+        function_texts: list[str] = []
+        for function in functions:
+            name = function.name
+            raw_arguments = function.arguments
+
+            try:
+                parsed_arguments = json.loads(raw_arguments)
+            except (TypeError, json.JSONDecodeError):
+                parsed_arguments = raw_arguments
+
+            call_text = f"<|tool_call>call:{name}" + "{"
+            if isinstance(parsed_arguments, dict):
+                args_text = []
+                for key in sorted(parsed_arguments.keys()):
+                    value_text = _format_argument(parsed_arguments[key], escape_keys=False)
+                    args_text.append(f"{key}:{value_text}")
+
+                call_text += ",".join(args_text)
+            elif isinstance(parsed_arguments, str):
+                call_text += parsed_arguments
+            else:
+                call_text += _format_argument(parsed_arguments, escape_keys=False)
+
+            call_text += "}<tool_call|>"
+            function_texts.append(call_text)
+
+        return "".join(function_texts)
 
 class GLM4ToolUtils(ToolUtils):
     r"""GLM-4 tool using template."""
@@ -361,6 +519,8 @@ class MiniMaxM2ToolUtils(ToolUtils):
             prompt += "\n</invoke>"
             function_texts.append(prompt)
 
+        return "\n".join(function_texts)
+
     @override
     @staticmethod
     def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
@@ -721,6 +881,7 @@ class LFM2ToolUtils(ToolUtils):
 
 TOOLS = {
     "default": DefaultToolUtils(),
+    "gemma4": Gemma4ToolUtils(),
     "glm4": GLM4ToolUtils(),
     "llama3": Llama3ToolUtils(),
     "lfm2": LFM2ToolUtils(),

src/llamafactory/extras/constants.py

@@ -865,6 +865,34 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "Gemma-4-26B-A4B-Thinking": {
+            DownloadSource.DEFAULT: "google/gemma-4-26B-A4B-it",
+        },
+        "Gemma-4-31B-Thinking": {
+            DownloadSource.DEFAULT: "google/gemma-4-31B-it",
+        },
+    },
+    template="gemma4",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Gemma-4-E2B-Thinking": {
+            DownloadSource.DEFAULT: "google/gemma-4-E2B-it",
+        },
+        "Gemma-4-E4B-Thinking": {
+            DownloadSource.DEFAULT: "google/gemma-4-E4B-it",
+        },
+    },
+    template="gemma4n",
+    multimodal=True,
+)
+
+
 register_model_group(
     models={
         "GLM-4-9B": {

src/llamafactory/extras/packages.py

@@ -70,6 +70,10 @@ def is_matplotlib_available():
     return _is_package_available("matplotlib")
 
 
+def is_hyper_parallel_available():
+    return _is_package_available("hyper_parallel")
+
+
 def is_mcore_adapter_available():
     return _is_package_available("mcore_adapter")
 

src/llamafactory/hparams/finetuning_args.py

@@ -482,6 +482,24 @@ class FinetuningArguments(
             )
         },
     )
+    use_hyper_parallel: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to use HyperParallel distributed training backend (FSDP/TP). "
+                "Only supported for the 'sft' stage with full fine-tuning."
+            )
+        },
+    )
+    hyper_parallel_args: str | None = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to a JSON file containing HyperParallel strategy arguments "
+                "(e.g., tp_size, param_dtype). Used when use_hyper_parallel=True."
+            )
+        },
+    )
     use_muon: bool = field(
         default=False,
         metadata={"help": "Whether or not to use the Muon optimizer."},

src/llamafactory/model/adapter.py

@@ -125,7 +125,7 @@ def _setup_freeze_tuning(
 
     model_type = getattr(model.config, "model_type", None)
     if not finetuning_args.freeze_multi_modal_projector and model_type in COMPOSITE_MODELS:
-        trainable_layers.append(COMPOSITE_MODELS[model_type].projector_key)
+        trainable_layers.extend(COMPOSITE_MODELS[model_type].projector_keys)
 
     forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
     for name, param in model.named_parameters():

src/llamafactory/model/model_utils/liger_kernel.py

@@ -45,7 +45,7 @@ def apply_liger_kernel(
         from liger_kernel.transformers import apply_liger_kernel_to_gemma3 as apply_liger_kernel
     elif model_type == "gemma3_text":
         from liger_kernel.transformers import apply_liger_kernel_to_gemma3_text as apply_liger_kernel
-    elif model_type == "glm4":
+    elif model_type in ["glm", "glm4"]: # for glm4-9b, glm4-32B respectively
         from liger_kernel.transformers import apply_liger_kernel_to_glm4 as apply_liger_kernel
     elif model_type == "glm4v":
         from liger_kernel.transformers import apply_liger_kernel_to_glm4v as apply_liger_kernel

src/llamafactory/model/model_utils/misc.py

@@ -35,7 +35,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
         forbidden_modules.add("output")
 
     if model_type in COMPOSITE_MODELS:
-        forbidden_modules.add(COMPOSITE_MODELS[model_type].projector_key)
+        forbidden_modules.update(COMPOSITE_MODELS[model_type].projector_keys)
 
     if freeze_vision_tower and model_type in COMPOSITE_MODELS:
         forbidden_modules.update(COMPOSITE_MODELS[model_type].vision_model_keys)

src/llamafactory/model/model_utils/moe.py

@@ -147,6 +147,11 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
 
         _set_z3_leaf_modules(model, [Qwen3NextSparseMoeBlock])
 
+    if model_type == "qwen3_5_moe":
+        from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeSparseMoeBlock
+
+        _set_z3_leaf_modules(model, [Qwen3_5MoeSparseMoeBlock])
+
 
 def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.moe_aux_loss_coef:

src/llamafactory/model/model_utils/visual.py

@@ -39,16 +39,26 @@ transformers_logger = transformers.utils.logging.get_logger(__name__)
 @dataclass
 class CompositeModel:
     model_type: str
-    projector_key: str
+    projector_keys: list[str]
     vision_model_keys: list[str]
     language_model_keys: list[str]
     lora_conflict_keys: list[str]
 
-    def get_projector(self, module: "torch.nn.Module") -> "torch.nn.Module":
-        for key in self.projector_key.split("."):
-            module = getattr(module, key)
 
-        return module
+    def get_projectors(self, module: "torch.nn.Module") -> list["torch.nn.Module"]:
+        mm_projectors: list[torch.nn.Module] = []
+        for projector_key in self.projector_keys:
+            project_module = module
+            for key in projector_key.split("."):
+                project_module = getattr(project_module, key, None)
+                if project_module is None: # i,e gemma4 bigger one, there is no embed_audio
+                    logger.warning_rank0(f"Projector key {projector_key} not found in module {module.__class__.__name__}.")
+                    break
+
+            if project_module is not None:
+                mm_projectors.append(project_module)
+
+        return mm_projectors
 
 
 COMPOSITE_MODELS: dict[str, "CompositeModel"] = {}
@@ -56,7 +66,7 @@ COMPOSITE_MODELS: dict[str, "CompositeModel"] = {}
 
 def _register_composite_model(
     model_type: str,
-    projector_key: Optional[str] = None,
+    projector_keys: list[str] | None = None,
     vision_model_keys: Optional[list[str]] = None,
     language_model_keys: Optional[list[str]] = None,
     lora_conflict_keys: Optional[list[str]] = None,
@@ -65,7 +75,7 @@ def _register_composite_model(
 
     Args:
         model_type: model type
-        projector_key: multi_modal_projector
+        projector_keys: multi_modal_projector
         vision_model_keys: vision_tower
         language_model_keys: language_model
         lora_conflict_keys: None
@@ -73,7 +83,7 @@ def _register_composite_model(
     """
     COMPOSITE_MODELS[model_type] = CompositeModel(
         model_type=model_type,
-        projector_key=projector_key or "multi_modal_projector",
+        projector_keys=projector_keys or ["multi_modal_projector"],
         vision_model_keys=vision_model_keys or ["vision_tower"],
         language_model_keys=language_model_keys or ["language_model", "lm_head"],
         lora_conflict_keys=lora_conflict_keys or [],
@@ -136,12 +146,16 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
     if getattr(model, "quantization_method", None):
         model_type = getattr(model.config, "model_type", None)
         if model_type in COMPOSITE_MODELS:
-            mm_projector = COMPOSITE_MODELS[model_type].get_projector(model)
+            mm_projectors = COMPOSITE_MODELS[model_type].get_projectors(model)
         else:
             return
 
-        logger.info_rank0(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
+        logger.info_rank0(
-        mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
+            f"Casting multimodal projector outputs in {model_args.compute_dtype}: "
+            f"{COMPOSITE_MODELS[model_type].projector_keys}."
+        )
+        for mm_projector in mm_projectors:
+            mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
 
 
 def configure_visual_model(config: "PretrainedConfig") -> None:
@@ -166,9 +180,9 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
             forbidden_modules.update(vision_model_keys)
 
         if finetuning_args.freeze_multi_modal_projector:
-            projector_key = COMPOSITE_MODELS[model_type].projector_key
+            projector_keys = COMPOSITE_MODELS[model_type].projector_keys
-            logger.info_rank0(f"Set multi model projector not trainable: {projector_key}.")
+            logger.info_rank0(f"Set multi model projector not trainable: {projector_keys}.")
-            forbidden_modules.add(projector_key)
+            forbidden_modules.update(projector_keys)
 
         if finetuning_args.freeze_language_model:
             language_model_keys = COMPOSITE_MODELS[model_type].language_model_keys
@@ -200,7 +214,7 @@ def patch_target_modules(
 
 _register_composite_model(
     model_type="dots_ocr",
-    projector_key="vision_tower.merger",
+    projector_keys=["vision_tower.merger"],
     vision_model_keys=["vision_tower"],
     language_model_keys=["model", "lm_head"],
     lora_conflict_keys=["merger"],
@@ -219,10 +233,18 @@ _register_composite_model(
 )
 
 
+_register_composite_model(
+    model_type="gemma4",
+    projector_keys=["model.embed_vision", "model.embed_audio"],
+    vision_model_keys=["vision_tower", "audio_tower"],
+    lora_conflict_keys=["per_layer_projection_norm"],
+)
+
+
 # copied from qwen2vl
 _register_composite_model(
     model_type="glm4v",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -231,7 +253,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="glm4v_moe",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -240,7 +262,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="glm_ocr",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -257,7 +279,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="Keye",
-    projector_key="mlp_AR",
+    projector_keys=["mlp_AR"],
     vision_model_keys=["visual.vision_model.patch_embedding", "visual.vision_model.encoder"],
     language_model_keys=["model", "lm_head"],
     lora_conflict_keys=["patch_embedding"],
@@ -292,7 +314,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="minicpmv",
-    projector_key="resampler",
+    projector_keys=["resampler"],
     vision_model_keys=["vpm"],
     language_model_keys=["llm"],
 )
@@ -300,7 +322,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="minicpmo",
-    projector_key="resampler",
+    projector_keys=["resampler"],
     vision_model_keys=["vpm", "apm", "audio_avg_pooler", "audio_projection_layer", "tts"],
     language_model_keys=["llm"],
     lora_conflict_keys=["audio_projection_layer"],
@@ -309,7 +331,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="mistral3",
-    projector_key="model.multi_modal_projector",
+    projector_keys=["model.multi_modal_projector"],
 )
 
 
@@ -332,7 +354,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen2_5_omni_thinker",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger", "audio_tower.proj"],
     vision_model_keys=["visual.patch_embed", "visual.blocks", "audio_tower"],
     language_model_keys=["model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -341,7 +363,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen2_vl",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -350,7 +372,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen2_5_vl",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -359,7 +381,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen3_vl",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -368,7 +390,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen3_vl_moe",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger"],
     vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -377,7 +399,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen3_omni_moe_thinker",
-    projector_key="visual.merger",
+    projector_keys=["visual.merger", "audio_tower.proj"],
     vision_model_keys=[
         "visual.pos_embed",
         "visual.patch_embed",
@@ -392,7 +414,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen3_5",
-    projector_key="model.visual.merger",
+    projector_keys=["model.visual.merger"],
     vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
@@ -401,7 +423,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="qwen3_5_moe",
-    projector_key="model.visual.merger",
+    projector_keys=["model.visual.merger"],
     vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],

src/llamafactory/train/hyper_parallel/__init__.py

@@ -0,0 +1,18 @@
+# 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.
+
+from .workflow import run_sft
+
+
+__all__ = ["run_sft"]

src/llamafactory/train/hyper_parallel/workflow.py

@@ -0,0 +1,183 @@
+# 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.
+
+from typing import TYPE_CHECKING, Optional
+
+from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
+from ...extras.constants import IGNORE_INDEX
+from ...extras.logging import get_logger
+from ...extras.misc import calculate_tps
+from ...extras.packages import is_hyper_parallel_available, is_transformers_version_greater_than
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..callbacks import SaveProcessorCallback
+from ..sft.metric import ComputeAccuracy, ComputeSimilarity, eval_logit_processor
+from ..trainer_utils import asft_loss_func, create_modelcard_and_push, create_ref_model, dft_loss_func, eaft_loss_func
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def run_sft(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[list["TrainerCallback"]] = None,
+):
+    if not is_hyper_parallel_available():
+        raise ImportError(
+            "hyper_parallel is not installed. Please install it with `pip install hyper_parallel`."
+        )
+
+    from hyper_parallel.integration.llamafactory import (  # pylint: disable=C0415
+        HyperParallelArguments,
+        HyperParallelTrainer,
+    )
+
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
+
+    ref_model = None
+    if finetuning_args.use_asft_loss:
+        ref_model = create_ref_model(model_args, finetuning_args)
+
+    data_collator = SFTDataCollatorWith4DAttentionMask(
+        template=template,
+        model=model if not training_args.predict_with_generate else None,
+        pad_to_multiple_of=8 if training_args.do_train else None,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
+        block_diag_attn=model_args.block_diag_attn,
+        attn_implementation=getattr(model.config, "_attn_implementation", None),
+        compute_dtype=model_args.compute_dtype,
+        **tokenizer_module,
+    )
+
+    # Metric utils
+    metric_module = {}
+    if training_args.predict_with_generate:
+        metric_module["compute_metrics"] = ComputeSimilarity(tokenizer=tokenizer)
+    elif finetuning_args.compute_accuracy:
+        metric_module["compute_metrics"] = ComputeAccuracy()
+        metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
+
+    # Keyword arguments for `model.generate`
+    gen_kwargs = generating_args.to_dict(obey_generation_config=True)
+    if is_transformers_version_greater_than("4.58.0"):
+        extra_ids = getattr(tokenizer, "additional_special_tokens_ids", None)
+        if not isinstance(extra_ids, list):
+            extra_special_tokens = getattr(tokenizer, "_extra_special_tokens", [])
+            string_tokens = [str(t) for t in extra_special_tokens]
+            extra_ids = tokenizer.convert_tokens_to_ids(string_tokens)
+        all_eos_ids = [tokenizer.eos_token_id] + [i for i in extra_ids if i != -1]
+        gen_kwargs["eos_token_id"] = list(dict.fromkeys(all_eos_ids))
+    else:
+        gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
+    gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
+
+    hp_args = HyperParallelArguments.from_finetuning_args(finetuning_args)
+
+    callbacks = list(callbacks or [])
+    processor = tokenizer_module.get("processor")
+    if processor is not None:
+        callbacks.append(SaveProcessorCallback(processor))
+
+    compute_loss_func = None
+    if finetuning_args.use_dft_loss:
+        compute_loss_func = dft_loss_func
+    elif finetuning_args.use_eaft_loss:
+        compute_loss_func = lambda outputs, labels, num_items_in_batch=None: eaft_loss_func(  # noqa: E731
+            outputs, labels, num_items_in_batch, finetuning_args.eaft_alpha
+        )
+    elif finetuning_args.use_asft_loss:
+        from functools import partial
+
+        compute_loss_func = partial(asft_loss_func, asft_alpha=finetuning_args.asft_alpha)
+
+    trainer = HyperParallelTrainer(
+        hp_args=hp_args,
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        gen_kwargs=gen_kwargs,
+        ref_model=ref_model,
+        compute_loss_func=compute_loss_func,
+        **dataset_module,
+        **tokenizer_module,
+        **metric_module,
+    )
+
+    if finetuning_args.use_badam:
+        from types import MethodType
+
+        from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore[import]
+
+        trainer.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, trainer.accelerator)
+        trainer.add_callback(BAdamCallback)
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        if finetuning_args.include_effective_tokens_per_second:
+            train_result.metrics["effective_tokens_per_sec"] = calculate_tps(
+                dataset_module["train_dataset"], train_result.metrics, stage="sft"
+            )
+
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            keys = ["loss"]
+            if isinstance(dataset_module.get("eval_dataset"), dict):
+                keys += sum(
+                    [[f"eval_{key}_loss", f"eval_{key}_accuracy"] for key in dataset_module["eval_dataset"].keys()],
+                    [],
+                )
+            else:
+                keys += ["eval_loss", "eval_accuracy"]
+
+            plot_loss(training_args.output_dir, keys=keys)
+
+    if training_args.predict_with_generate:
+        tokenizer.padding_side = "left"
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        logger.warning_rank0_once("Batch generation can be very slow. Consider using `scripts/vllm_infer.py` instead.")
+        predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(dataset_module["eval_dataset"], predict_results, generating_args.skip_special_tokens)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llamafactory/train/tuner.py

@@ -24,7 +24,12 @@ from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.misc import find_available_port, get_device_name, get_torch_device, infer_optim_dtype
-from ..extras.packages import is_mcore_adapter_available, is_ray_available, is_transformers_version_greater_than
+from ..extras.packages import (
+    is_hyper_parallel_available,
+    is_mcore_adapter_available,
+    is_ray_available,
+    is_transformers_version_greater_than,
+)
 from ..hparams import RayArguments, get_infer_args, get_ray_args, get_train_args, read_args
 from ..model import load_model, load_tokenizer
 from .callbacks import LogCallback, PissaConvertCallback, ReporterCallback
@@ -71,7 +76,16 @@ def _training_function(config: dict[str, Any]) -> None:
 
     callbacks.append(ReporterCallback(model_args, data_args, finetuning_args, generating_args))  # add to last
 
-    if finetuning_args.stage in ["pt", "sft", "dpo"] and finetuning_args.use_mca:
+    if finetuning_args.stage == "sft" and finetuning_args.use_hyper_parallel:
+        if not is_hyper_parallel_available():
+            raise ImportError(
+                "hyper_parallel is not installed. Please install it with `pip install hyper_parallel`."
+            )
+        from .hyper_parallel import run_sft as run_sft_hp
+
+        run_sft_hp(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+
+    elif finetuning_args.stage in ["pt", "sft", "dpo"] and finetuning_args.use_mca:
         if not is_mcore_adapter_available():
             raise ImportError("mcore_adapter is not installed. Please install it with `pip install mcore-adapter`.")
         if finetuning_args.stage == "pt":

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."},
     )
+    logging_steps: int = field(
+        default=1,
+        metadata={"help": "Log metrics every N optimizer steps."},
+    )
 
     def __post_init__(self) -> None:
         self.dist_config = get_plugin_config(self.dist_config)

src/llamafactory/v1/core/base_trainer.py

@@ -36,6 +36,12 @@ from ..accelerator.helper import ReduceOp
 from ..accelerator.interface import Dim, DistributedInterface
 from ..config import TrainingArguments
 from ..utils import logging
+from ..utils.callbacks import (
+    CallbackHandler,
+    LoggingCallback,
+    TrainerCallback,
+    TrainerState,
+)
 from ..utils.helper import compute_valid_tokens
 from ..utils.types import BatchInput, HFModel, ModelOutput, Tensor, TorchDataset
 from .utils.batching import BatchGenerator
@@ -52,6 +58,7 @@ class BaseTrainer:
         model: HFModel,
         renderer: Renderer,
         train_dataset: TorchDataset,
+        callbacks: list[TrainerCallback] | None = None,
     ) -> None:
         self.args = args
         self.model = model
@@ -64,6 +71,7 @@ class BaseTrainer:
         # cached variables
         self.device = DistributedInterface().current_device
         self.dp_size = DistributedInterface().get_world_size(Dim.DP)
+        self.cp_size = DistributedInterface().get_world_size(Dim.CP)
         self.model_input_names = self.renderer.processor.model_input_names
 
         self._create_batch_generator()
@@ -99,6 +107,29 @@ class BaseTrainer:
             self._init_optimizer()
             self._init_lr_scheduler()
 
+        # Callbacks
+        self.callback_handler = CallbackHandler([LoggingCallback()], trainer=self)
+        for cb in callbacks or []:
+            self.callback_handler.add_callback(cb)
+
+        # Callbacks: TrainerState tracks progress across the full run.
+        self.state = TrainerState(num_training_steps=self.num_training_steps)
+
+        if self.args.dist_config is not None and self.args.dist_config.get("cp_size", 1) > 1:
+            # qwen3.5 is not supported because of the different attention implementation, which will be supported in the future.
+            if model.config.model_type == "qwen3_5":
+                raise RuntimeError(
+                    "Sequence parallel is not supported for qwen3.5 model due to its different attention implementation, which will be supported in the future."
+                )
+            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."
+                )
+                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:
         self.train_batch_generator = BatchGenerator(
             dataset=self.train_dataset,
@@ -157,7 +188,7 @@ class BaseTrainer:
         """
         batch_size, _ = batch["labels"].shape
         model_inputs = {
-            k: v.to(self.device, non_blocking=True) for k, v in batch.items() if k in self.model_input_names
+            k: v.to(self.device, non_blocking=True) for k, v in batch.items() if isinstance(v, torch.Tensor)
         }
         labels = batch["labels"].to(self.device, non_blocking=True)
         outputs: ModelOutput = model(**model_inputs)
@@ -174,16 +205,31 @@ class BaseTrainer:
     def fit(self) -> None:
         """Train the model."""
         self.model.train()
+        self.callback_handler.on_train_begin(self.args, self.state)
         for epoch in range(self.args.num_train_epochs):
+            self.state.epoch = epoch
             self.train_batch_generator.set_epoch(epoch)
+            self.callback_handler.on_epoch_begin(self.args, self.state)
+
             for micro_batches in self.train_batch_generator:
                 self.global_step += 1
+
+                self.state.global_step = self.global_step
+                self.callback_handler.on_step_begin(self.args, self.state)
+
                 step_loss = 0
                 step_valid_tokens = compute_valid_tokens(micro_batches)
                 step_valid_tokens = DistributedInterface().all_reduce(step_valid_tokens, op=ReduceOp.SUM)
                 num_micro = len(micro_batches)
                 for i, micro_batch in enumerate(micro_batches):
-                    loss = self.compute_loss(micro_batch)
+                    if self.args.dist_config and self.args.dist_config.get("cp_size", 1) > 1:
+                        from ..plugins.model_plugins.parallelization.sequence_parallel import (
+                            SequenceParallelLossPlugin,
+                        )
+
+                        loss = SequenceParallelLossPlugin("sequence_parallel_loss")(self.model, micro_batch)
+                    else:
+                        loss = self.compute_loss(micro_batch)
                     mini_step_valid_tokens = compute_valid_tokens([micro_batch])
                     # fsdp uses mean reduction so we need to scale the loss by dp_size
                     loss = loss * mini_step_valid_tokens * self.dp_size / (step_valid_tokens + 1e-6)
@@ -200,7 +246,24 @@ class BaseTrainer:
                     # deepspeed: engine.step() already ran inside backward at the sync boundary
                     grad_norm = self._deepspeed_engine.get_grad_norm()
                 else:
-                    grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm).item()
+                    if self.args.dist_config and self.args.dist_config.get("cp_size", 1) > 1:
+                        from torch.nn.utils.clip_grad import _clip_grads_with_norm_, _get_total_norm
+
+                        parameters = self.model.parameters()
+                        if isinstance(parameters, torch.Tensor):
+                            parameters = [parameters]
+                        else:
+                            parameters = list(parameters)
+                        grads = [p.grad for p in parameters if p.grad is not None]
+                        grad_norm = _get_total_norm(grads)
+                        grad_norm = grad_norm.to(self.device)
+                        _clip_grads_with_norm_(parameters, self.args.max_grad_norm, grad_norm)
+                        if isinstance(grad_norm, torch.distributed._tensor.DTensor):
+                            grad_norm = grad_norm.full_tensor().item()
+                    else:
+                        grad_norm = torch.nn.utils.clip_grad_norm_(
+                            self.model.parameters(), self.args.max_grad_norm
+                        ).item()
 
                     # isfinite(): argument 'input' (position 1) must be Tensor, not float
                     if not torch.isfinite(torch.tensor(grad_norm)):  # type: ignore # pyright: ignore [reportUnknownReturnType]
@@ -213,14 +276,41 @@ class BaseTrainer:
 
                 step_loss, grad_norm = DistributedInterface().all_reduce([step_loss, grad_norm])
                 DistributedInterface().sync()
-                if DistributedInterface().get_rank() == 0:
+
-                    print(f"Epoch {epoch}, Step {self.global_step}, Loss: {step_loss:.4f}, Grad Norm: {grad_norm:.4f}")
+                # Update state with step metrics
+                current_lr = (
+                    self.lr_scheduler.get_last_lr()[0]
+                    if hasattr(self.lr_scheduler, "get_last_lr")
+                    else self.args.learning_rate
+                )
+                self.state.loss = step_loss
+                self.state.grad_norm = grad_norm
+                self.state.learning_rate = current_lr
+
+                self.callback_handler.on_step_end(self.args, self.state)
+
+                # Logging: trainer decides when to log
+                if self.global_step % self.args.logging_steps == 0:
+                    logs = {
+                        "epoch": epoch,
+                        "step": self.global_step,
+                        "loss": step_loss,
+                        "grad_norm": grad_norm,
+                        "learning_rate": current_lr,
+                    }
+                    self.callback_handler.on_log(self.args, self.state, logs)
 
                 # Check if max_steps is reached
                 if self.global_step >= self.num_training_steps:
                     logger.info_rank0(f"Reached max_steps ({self.num_training_steps}), stopping training.")
+                    self.callback_handler.on_epoch_end(self.args, self.state)
+                    self.callback_handler.on_train_end(self.args, self.state)
                     return
 
+            self.callback_handler.on_epoch_end(self.args, self.state)
+
+        self.callback_handler.on_train_end(self.args, self.state)
+
     def save_model(self) -> None:
         """Save the model."""
         if self.args.dist_config is not None and self.args.dist_config.name in ("deepspeed", "fsdp2"):
@@ -234,3 +324,5 @@ class BaseTrainer:
             model_to_save.save_pretrained(self.args.output_dir, max_shard_size="4GB")
             self.renderer.processor.save_pretrained(self.args.output_dir, max_shard_size="4GB")
             logger.info_rank0(f"Model saved to {self.args.output_dir}")
+
+        self.callback_handler.on_save(self.args, self.state)

src/llamafactory/v1/core/model_engine.py

@@ -140,6 +140,9 @@ class ModelEngine:
                 **init_kwargs,
             )
 
+        init_mode = self.args.init_config.name if self.args.init_config is not None else "init_on_default"
+        model._init_mode = init_mode
+
         if self.args.peft_config is None:
             if self.is_train:
                 logger.info_rank0("Fine-tuning mode: full tuning")
@@ -147,6 +150,9 @@ class ModelEngine:
             else:
                 logger.info_rank0("Inference the original model")
         else:
+            if self.args.peft_config.name == "lora" and init_mode == "init_on_meta":
+                raise ValueError("Currently lora stage does not support loading model by meta.")
+
             from ..plugins.model_plugins.peft import PeftPlugin
 
             model = PeftPlugin(self.args.peft_config.name)(model, self.args.peft_config, self.is_train)

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

@@ -146,6 +146,8 @@ class Renderer:
         for sample in samples:
             if "messages" in sample:
                 model_input = self.render_messages(sample["messages"], sample.get("tools"))
+                if "position_ids" not in model_input:
+                    model_input["position_ids"] = list(range(1, len(model_input["input_ids"]) + 1))
             elif "chosen_messages" in sample and "rejected_messages" in sample:
                 chosen_input = self.render_messages(sample["chosen_messages"], sample.get("tools"))
                 rejected_input = self.render_messages(sample["rejected_messages"], sample.get("tools"))

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

@@ -0,0 +1,59 @@
+# Copyright 2025 Bytedance Ltd. and/or its affiliates. and the LlamaFactory team.
+#
+# This code is inspired by the Bytedance's verl library.
+# https://github.com/verl-project/verl/blob/77476af84cc074edf5a6437f8d5ea418d7a54916/verl/utils/ulysses.py
+#
+# 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.
+
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+from torch import Tensor
+
+
+def all_to_all_tensor(
+    local_input: Tensor,
+    scatter_dim: int,
+    gather_dim: int,
+    group: Optional[dist.ProcessGroup] = None,
+):
+    seq_world_size = dist.get_world_size(group)
+    input_list = [t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)]
+    output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)]
+    dist.all_to_all(output_list, input_list, group=group)
+    return torch.cat(output_list, dim=gather_dim).contiguous()
+
+
+class SeqAllToAll4D(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        group: dist.ProcessGroup,
+        local_input: Tensor,
+        scatter_dim: int,
+        gather_dim: int,
+    ) -> Tensor:
+        ctx.group = group
+        ctx.scatter_dim = scatter_dim
+        ctx.gather_dim = gather_dim
+        return all_to_all_tensor(local_input, scatter_dim, gather_dim, group)
+
+    @staticmethod
+    def backward(ctx: Any, *grad_output: Tensor) -> tuple[None, Tensor, None, None]:
+        return (
+            None,
+            all_to_all_tensor(grad_output[0], ctx.gather_dim, ctx.scatter_dim, ctx.group),
+            None,
+            None,
+        )

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

@@ -0,0 +1,199 @@
+# 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 sys
+from functools import partial
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import transformers
+
+from ....accelerator.interface import Dim, DistributedInterface
+from ....utils import logging
+from ....utils.plugin import BasePlugin
+from ....utils.types import ModelOutput
+from .ulysses import (
+    UlyssesAttention,
+    get_ulysses_sequence_parallel_group,
+    get_ulysses_sequence_parallel_rank,
+    get_ulysses_sequence_parallel_world_size,
+    set_ulysses_sequence_parallel_group,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class SequenceParallelModelPlugin(BasePlugin):
+    def __call__(self, model, model_args):
+        return super().__call__(model, model_args)
+
+
+class SequenceParallelLossPlugin(BasePlugin):
+    def __call__(self, model, inputs, *args, **kwargs):
+        return super().__call__(model, inputs, *args, **kwargs)
+
+
+def new_flash_attn_forward(
+    query_states,
+    key_states,
+    value_states,
+    attention_mask,
+    sequence_parallel_size=1,
+    dropout=0,
+    deterministic=False,
+    is_causal=True,
+    group=None,
+    mode="ulysses",
+    attn_fn=None,
+    target_dtype=None,
+    **kwargs,
+):
+    if mode == "ulysses":
+        dist_attn = UlyssesAttention(sequence_process_group=group, attn_fn=attn_fn)
+        attn_output = dist_attn(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            query_length=query_states.shape[1] * sequence_parallel_size,
+            deterministic=deterministic,
+            dropout_p=dropout,
+            causal=is_causal,
+            position_ids=kwargs.get("position_ids", None),
+            target_dtype=target_dtype,
+        )
+    else:
+        raise NotImplementedError("Other sequence parallel modes are to be implemented.")
+
+    return attn_output
+
+
+@SequenceParallelModelPlugin("ulysses").register()
+def apply_sequence_parallel(model, model_args):
+    # Replace _flash_attention_forward with new_flash_attn_forward
+    module = sys.modules[model.__module__]
+    cp_size = model_args.get("cp_size", 1)
+
+    set_ulysses_sequence_parallel_group(DistributedInterface().get_group(Dim.CP))
+
+    try:
+        num_attention_heads, num_key_value_heads = model.config.num_attention_heads, model.config.num_attention_heads
+    except AttributeError:
+        num_attention_heads, num_key_value_heads = (
+            model.config.text_config.num_attention_heads,
+            model.config.text_config.num_key_value_heads,
+        )
+
+    assert num_attention_heads % cp_size == 0, "num_attention_heads must be divisible by cp_size"
+    assert num_key_value_heads % cp_size == 0 or cp_size % num_key_value_heads == 0, (
+        "num_key_value_heads must be divisible by cp_size"
+    )
+
+    origin_attn = transformers.modeling_flash_attention_utils._flash_attention_forward
+    new_flash_attention_forward = partial(
+        new_flash_attn_forward,
+        group=get_ulysses_sequence_parallel_group(),
+        mode="ulysses",
+        attn_fn=origin_attn,
+        sequence_parallel_size=cp_size,
+    )
+
+    for module_name, module in list(sys.modules.items()):
+        try:
+            if (
+                hasattr(module, "__file__")
+                and "transformers" in module.__file__
+                and getattr(module._flash_attention_forward, "__name__", "") == "_flash_attention_forward"
+            ):
+                module._flash_attention_forward = new_flash_attention_forward
+                logger.info_rank0(
+                    f"Replaced _flash_attention_forward in module {module_name} with new_flash_attn_forward for sequence parallel."
+                )
+        except (AttributeError, TypeError):
+            continue
+
+
+def padding_and_split_data(data, device_mesh=None):
+    if device_mesh is not None:
+        cp_size = device_mesh["cp"].size()
+        cp_rank = device_mesh["cp"].get_local_rank()
+        cp_group = device_mesh["cp"].get_group()
+        for k, v in data.items():
+            if isinstance(v, torch.Tensor) and v.ndim > 1:
+                data_len = torch.tensor(v.shape[-1], device=v.device, dtype=torch.int64)
+                global_data_len = [torch.empty_like(data_len) for _ in range(cp_size)]
+                dist.all_gather(global_data_len, data_len, group=cp_group)
+                max_data_len = max(global_data_len)
+                pad_size = max_data_len - v.shape[-1] + (cp_size - max_data_len % cp_size) % cp_size
+                if k == "labels":
+                    pad_value = -100
+                elif k == "loss_weights":
+                    pad_value = 0.0
+                else:
+                    pad_value = 0
+                pad_data = F.pad(v, (0, pad_size), value=pad_value)
+                data[k] = torch.chunk(pad_data, chunks=cp_size, dim=-1)[cp_rank].contiguous()
+    return data
+
+
+@SequenceParallelLossPlugin("sequence_parallel_loss").register()
+def sequence_parallel_loss(model, model_inputs):
+    device_mesh = DistributedInterface().get_device_mesh(Dim.CP)
+
+    model_inputs = {
+        k: v.to(dist.get_rank(), non_blocking=True) for k, v in model_inputs.items() if isinstance(v, torch.Tensor)
+    }
+
+    model_inputs = padding_and_split_data(model_inputs, device_mesh)
+
+    batch_size, _ = model_inputs["labels"].shape
+
+    outputs: ModelOutput = model(**model_inputs)
+
+    logits = outputs.logits.float()
+
+    labels = model_inputs["labels"]
+
+    cp_group = get_ulysses_sequence_parallel_group()
+    cp_world_size = get_ulysses_sequence_parallel_world_size(cp_group)
+    cp_rank = get_ulysses_sequence_parallel_rank(cp_group)
+
+    # use all_gather to collect labels from all sequence parallel processes
+    global_labels = [torch.empty_like(labels) for _ in range(cp_world_size)]
+    dist.all_gather(global_labels, labels, group=cp_group)
+    labels = torch.cat(global_labels, dim=1).contiguous()
+    shift_labels = labels[..., 1:].view(-1).contiguous()
+    shift_labels = F.pad(shift_labels, (0, 1), value=-100)
+    shift_labels = torch.chunk(shift_labels, chunks=cp_world_size, dim=-1)[cp_rank].contiguous()
+
+    # use all_gather to collect loss_weights from all sequence parallel processes
+    loss_weights = model_inputs["loss_weights"]
+    global_loss_weights = [torch.empty_like(loss_weights) for _ in range(cp_world_size)]
+    dist.all_gather(global_loss_weights, loss_weights, group=cp_group)
+    shift_loss_weights = torch.cat(global_loss_weights, dim=1).contiguous()
+    shift_loss_weights = shift_loss_weights[..., 1:].contiguous()
+
+    shift_logits = logits.view(shift_labels.size(0), -1).contiguous()
+
+    # use all_gather to collect log_probs from all sequence parallel processes
+    log_probs = -F.cross_entropy(shift_logits, shift_labels, reduction="none").view(batch_size, -1)
+    global_log_probs = dist.nn.all_gather(log_probs, group=cp_group)
+    global_log_probs = torch.cat(global_log_probs, dim=1).contiguous()
+    log_probs = global_log_probs[..., :-1].contiguous()
+
+    loss = (-log_probs * shift_loss_weights).sum() / (shift_loss_weights.sum() + 1e-6)
+
+    return loss

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

@@ -0,0 +1,163 @@
+# Copyright 2025 Bytedance Ltd. and/or its affiliates. and the LlamaFactory team.
+#
+# This code is inspired by the Bytedance's verl library.
+# https://github.com/verl-project/verl/blob/77476af84cc074edf5a6437f8d5ea418d7a54916/verl/utils/ulysses.py
+#
+# 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.
+
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+from torch import Tensor
+from torch.distributed import ProcessGroup
+
+from .seq_comm import SeqAllToAll4D
+
+
+_ULYSSES_SEQUENCE_PARALLEL_GROUP = None
+
+
+def set_ulysses_sequence_parallel_group(group: dist.ProcessGroup):
+    """Set ulysses sequence parallel process group."""
+    global _ULYSSES_SEQUENCE_PARALLEL_GROUP
+    _ULYSSES_SEQUENCE_PARALLEL_GROUP = group
+
+
+def get_ulysses_sequence_parallel_group() -> Optional[dist.ProcessGroup]:
+    """Get ulysses sequence parallel process group."""
+    global _ULYSSES_SEQUENCE_PARALLEL_GROUP
+    return _ULYSSES_SEQUENCE_PARALLEL_GROUP
+
+
+def get_ulysses_sequence_parallel_world_size(group: ProcessGroup = None) -> int:
+    """Get ulysses sequence parallel world size."""
+    group = get_ulysses_sequence_parallel_group() if group is None else group
+    return dist.get_world_size(group) if group else 1
+
+
+def get_ulysses_sequence_parallel_rank(group: ProcessGroup = None) -> int:
+    """Get ulysses sequence parallel rank."""
+    group = get_ulysses_sequence_parallel_group() if group is None else group
+    return dist.get_rank(group) if group else 0
+
+
+class UlyssesAttention(torch.nn.Module):
+    """Initialization.
+
+    Arguments:
+        local_attention (Module): local attention with q,k,v
+        sequence_process_group (ProcessGroup): sequence parallel process group
+        scatter_idx (int): scatter_idx for all2all comm
+        gather_idx (int): gather_idx for all2all comm
+        attn_type (AttnType): attention type enum
+    """
+
+    def __init__(
+        self,
+        sequence_process_group: dist.ProcessGroup = None,
+        scatter_idx: int = 2,
+        gather_idx: int = 1,
+        attn_fn: Optional[callable] = None,
+    ) -> None:
+
+        super().__init__()
+        self.spg = sequence_process_group
+        self.scatter_idx = scatter_idx
+        self.gather_idx = gather_idx
+        self.attn_fn = attn_fn
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        attention_mask: torch.Tensor,
+        query_length: int,
+        dropout_p=0.0,
+        softmax_scale=None,
+        position_ids: Optional[torch.Tensor] = None,
+        causal=True,
+        deterministic=False,
+        target_dtype=None,
+        *args: Any,
+    ) -> Tensor:
+        """Forward.
+
+        Arguments:
+            query (Tensor): query input to the layer
+            key (Tensor): key input to the layer
+            value (Tensor): value input to the layer
+            attention_mask (Tensor): attention mask for the layer
+            query_length (int): the length of the query sequence
+            dropout_p (float, optional): dropout probability. Defaults to 0.0.
+            softmax_scale (float, optional): scale factor for softmax. Defaults to None,
+            position_ids (torch.Tensor, optional): position ids for the attention. Defaults to None.
+            causal (bool, optional): whether to apply causal mask. Defaults to True.
+            deterministic (bool, optional): whether to apply dropout in deterministic way. Defaults to False.
+            target_dtype (torch.dtype, optional): target dtype for attention output. Defaults to None.
+            args: other args
+
+        Returns:
+            * output (Tensor): context output
+        """
+        # TODO Merge three alltoall calls into one
+        # 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)
+        v = SeqAllToAll4D.apply(self.spg, value, self.scatter_idx, self.gather_idx)
+
+        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)
+        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)
+
+        context_layer = self.attn_fn(
+            q,
+            k,
+            v,
+            attention_mask,
+            query_length=query_length,
+            is_causal=causal,
+            dropout=dropout_p,
+            position_ids=position_ids,
+            softmax_scale=softmax_scale,
+            deterministic=deterministic,
+            target_dtype=target_dtype,
+        )
+
+        if isinstance(context_layer, tuple):
+            context_layer = context_layer[0]
+
+        # (bs, seq_len, head_cnt/N, head_size) -> (bs, seq_len/N, head_cnt, head_size)
+        # scatter 1, gather 2
+        output = SeqAllToAll4D.apply(self.spg, context_layer, self.gather_idx, self.scatter_idx)
+
+        # out e.g., [s/p::h]
+        return output

src/llamafactory/v1/plugins/model_plugins/peft.py

@@ -150,9 +150,6 @@ def load_adapter(model: HFModel, adapter_name_or_path: Union[list[str], str], is
 
 @PeftPlugin("lora").register()
 def get_lora_model(model: HFModel, config: LoraConfigDict, is_train: bool = False) -> HFModel:
-    if model.device.type == "meta":
-        raise ValueError("Currently lora stage does not support loading model by meta.")
-
     adapter_name_or_path = config.get("adapter_name_or_path")
 
     if adapter_name_or_path:

src/llamafactory/v1/plugins/trainer_plugins/distributed/fsdp2.py

@@ -17,6 +17,7 @@ import gc
 import os
 
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 from peft.tuners.lora import LoraLayer
 from torch.distributed.checkpoint.state_dict import StateDictOptions, get_model_state_dict, set_model_state_dict
@@ -84,10 +85,7 @@ class FSDP2Engine:
             )
 
         if self.device_mesh is not None:
-            try:
+            self.fsdp_mesh = self.device_mesh
-                self.fsdp_mesh = self.device_mesh["dp"]
-            except Exception:
-                self.fsdp_mesh = self.device_mesh
 
             logger.info(f"Using Device Mesh: {self.fsdp_mesh}")
         else:
@@ -244,23 +242,57 @@ class FSDP2Engine:
             logger.info(f"Restored {len(saved_buffers)} non-persistent buffers")
 
     def shard_model(self, model: HFModel) -> HFModel:
-        if model.device.type == "meta":
+        init_mode = getattr(model, "_init_mode", "init_on_default")
+
+        if init_mode == "init_on_rank0":
+            if getattr(model.config, "tie_word_embeddings", False):
+                model.tie_weights()
+
+            if self.rank == 0:
+                logger.info("init_on_rank0 detected: sharding then scattering Rank 0 CPU weights.")
+                full_sd = {k: v.clone() for k, v in model.state_dict().items()}
+            else:
+                full_sd = {}
+
+            # Reuse existing helper to save persistent=False buffers (e.g. inv_freq) before shard
+            saved_buffers = self._save_non_persistent_buffers(model) if self.rank == 0 else {}
+
+            model = self.prepare_model(model)
+
+            device = get_current_accelerator()
+            model.to_empty(device=device)
+
+            # Scatter params from Rank 0 into all DTensor shards
+            # Broadcast the full state dict from the global rank-0 process to all ranks in this group.
+            options = StateDictOptions(full_state_dict=True, cpu_offload=True, broadcast_from_rank0=True)
+            set_model_state_dict(model, full_sd, options=options)
+
+            # Broadcast and restore non-persistent buffers
+            buffers_to_sync = [saved_buffers]
+            dist.broadcast_object_list(buffers_to_sync, src=0, group=self.fsdp_mesh.get_group())
+            self._restore_non_persistent_buffers(model, buffers_to_sync[0])
+
+            if self.rank == 0:
+                logger.info("init_on_rank0 sync complete.")
+
+        elif init_mode == "init_on_meta":
             non_persistent_buffers = self._save_non_persistent_buffers(model)
 
-            if getattr(model.config, "tie_word_embeddings", None):
+            if getattr(model.config, "tie_word_embeddings", False):
                 model.tie_weights()
 
             model = self.prepare_model(model)
             model = self.materialize_and_load(model, hf_model_path=model.config.name_or_path, dcp_path=self.dcp_path)
 
             # fix tied broken for no-fsdp-wrap case
-            if getattr(model.config, "tie_word_embeddings", None):
+            if getattr(model.config, "tie_word_embeddings", False):
                 model.tie_weights()
 
             self._restore_non_persistent_buffers(model, non_persistent_buffers)
 
         else:
             model = self.prepare_model(model)
+
         return model
 
     def _load_from_dcp(self, model: HFModel, dcp_path: str):

src/llamafactory/v1/utils/callbacks/__init__.py

@@ -0,0 +1,24 @@
+# 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.
+
+from .logging_callback import LoggingCallback
+from .trainer_callback import CallbackHandler, TrainerCallback, TrainerState
+
+
+__all__ = [
+    "CallbackHandler",
+    "LoggingCallback",
+    "TrainerCallback",
+    "TrainerState",
+]

src/llamafactory/v1/utils/callbacks/logging_callback.py

@@ -0,0 +1,64 @@
+# 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.
+
+from __future__ import annotations
+
+import json
+import os
+from typing import TYPE_CHECKING, Any
+
+from .. import logging
+from .trainer_callback import TrainerCallback, TrainerState
+
+
+if TYPE_CHECKING:
+    from ...config import TrainingArguments
+
+
+logger = logging.get_logger(__name__)
+
+
+class LoggingCallback(TrainerCallback):
+    """Logs training metrics to stdout on rank-0 and appends to ``state.log_history``.
+
+    On each logging step the entry is also persisted as a JSON line in
+    ``<output_dir>/trainer_log.jsonl`` so that training history survives crashes.
+    """
+
+    def on_log(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        logs: dict[str, Any],
+        **kwargs: Any,
+    ) -> None:
+        # Persist in history regardless of rank
+        state.log_history.append(dict(logs))
+
+        # Everything below is rank-0 only
+        from ...accelerator.interface import DistributedInterface  # lazy import
+
+        if DistributedInterface().get_rank() != 0:
+            return
+
+        # Human-readable output to stdout
+        display_logs = {**logs, "total_steps": state.num_training_steps}
+        parts = ", ".join(f"{k}: {v:.4f}" if isinstance(v, float) else f"{k}: {v}" for k, v in display_logs.items())
+        logger.info_rank0(parts)
+
+        # Append to JSONL log file in output_dir
+        os.makedirs(args.output_dir, exist_ok=True)
+        log_file = os.path.join(args.output_dir, "trainer_log.jsonl")
+        with open(log_file, "a", encoding="utf-8") as f:
+            f.write(json.dumps(display_logs, ensure_ascii=False) + "\n")

src/llamafactory/v1/utils/callbacks/trainer_callback.py

@@ -0,0 +1,147 @@
+# 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.
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+
+if TYPE_CHECKING:
+    from ...config import TrainingArguments
+
+
+@dataclass
+class TrainerState:
+    """A read-only snapshot of training progress passed to every callback hook.
+
+    Attributes:
+        epoch: Current epoch (0-indexed).
+        global_step: Number of optimizer steps completed so far.
+        num_training_steps: Total number of optimizer steps planned.
+        loss: Scalar loss value of the most recent step.
+        grad_norm: Gradient-norm value of the most recent step.
+        learning_rate: Current learning rate seen by the optimizer.
+        log_history: List of per-step log dicts emitted by ``LoggingCallback``.
+    """
+
+    epoch: int = 0
+    global_step: int = 0
+    num_training_steps: int = 0
+    loss: float = 0.0
+    grad_norm: float = 0.0
+    learning_rate: float = 0.0
+    log_history: list[dict[str, Any]] = field(default_factory=list)
+
+
+class TrainerCallback:
+    """Abstract base class for training callbacks.
+
+    Subclass and override whichever hooks you need.  All hooks receive:
+
+    - ``args``      – the :class:`~llamafactory.v1.config.TrainingArguments`.
+    - ``state``     – a :class:`TrainerState` snapshot (read-only).
+    - ``**kwargs``  – extra keyword arguments (model, optimizer, …).
+
+    Callbacks are *observers*: they should NOT mutate training flow.
+
+    Hook call order::
+
+        on_train_begin
+          for each epoch:
+            on_epoch_begin
+              for each step:
+                on_step_begin
+                  (forward / backward / optimizer.step)
+                on_step_end
+                [on_log]   ← if this step is a logging step
+            on_epoch_end
+        on_train_end
+    """
+
+    def on_train_begin(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called once before the first training step."""
+
+    def on_train_end(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called once after the last training step."""
+
+    def on_epoch_begin(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called at the beginning of each epoch."""
+
+    def on_epoch_end(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called at the end of each epoch."""
+
+    def on_step_begin(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called before the forward/backward pass of each optimizer step."""
+
+    def on_step_end(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called after the optimizer step."""
+
+    def on_log(self, args: TrainingArguments, state: TrainerState, logs: dict[str, Any], **kwargs: Any) -> None:
+        """Called when the trainer emits a log entry."""
+
+    def on_save(self, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        """Called after the model checkpoint has been written to disk."""
+
+
+class CallbackHandler:
+    """Owns a list of :class:`TrainerCallback` instances and fans out hook calls.
+
+    Usage::
+
+        handler = CallbackHandler([LoggingCallback(), MyWandbCallback()], trainer=trainer)
+        handler.on_train_begin(args, state)
+    """
+
+    def __init__(self, callbacks: list[TrainerCallback] | None = None, trainer: Any = None) -> None:
+        self.callbacks: list[TrainerCallback] = list(callbacks or [])
+        self.trainer = trainer
+
+    def add_callback(self, callback: TrainerCallback) -> None:
+        """Append a callback to the handler."""
+        self.callbacks.append(callback)
+
+    def _call(self, event: str, args: TrainingArguments, state: TrainerState, **kwargs: Any) -> None:
+        if self.trainer is not None:
+            kwargs.setdefault("model", getattr(self.trainer, "model", None))
+            kwargs.setdefault("optimizer", getattr(self.trainer, "optimizer", None))
+            kwargs.setdefault("lr_scheduler", getattr(self.trainer, "lr_scheduler", None))
+            kwargs.setdefault("train_dataloader", getattr(self.trainer, "train_batch_generator", None))
+
+        for cb in self.callbacks:
+            getattr(cb, event)(args, state, **kwargs)
+
+    def on_train_begin(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_train_begin", args, state)
+
+    def on_train_end(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_train_end", args, state)
+
+    def on_epoch_begin(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_epoch_begin", args, state)
+
+    def on_epoch_end(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_epoch_end", args, state)
+
+    def on_step_begin(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_step_begin", args, state)
+
+    def on_step_end(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_step_end", args, state)
+
+    def on_log(self, args: TrainingArguments, state: TrainerState, logs: dict[str, Any]) -> None:
+        self._call("on_log", args, state, logs=logs)
+
+    def on_save(self, args: TrainingArguments, state: TrainerState) -> None:
+        self._call("on_save", args, state)

tests/data/test_mm_plugin.py

@@ -57,7 +57,7 @@ TEXT_MESSAGES = [
 ]
 
 VIDEO_MESSAGES = [
-    {"role": "user", "content": "<video>What is in this viode?"},
+    {"role": "user", "content": "<video>What is in this video?"},
     {"role": "assistant", "content": "A cat."},
 ]
 
@@ -210,6 +210,34 @@ def test_gemma3_plugin():
     _check_plugin(**check_inputs)
 
 
+@pytest.mark.runs_on(["cpu", "mps"])
+@pytest.mark.skipif(not is_transformers_version_greater_than("5.6.0"), reason="Requires transformers>=5.6.0")
+def test_gemma4_plugin():
+    tokenizer_module = _load_tokenizer_module(model_name_or_path="google/gemma-4-31B-it")
+    processor = tokenizer_module["processor"]
+    gemma4_plugin = get_mm_plugin(name="gemma4", image_token="<|image|>", video_token="<|video|>")
+    check_inputs = {"plugin": gemma4_plugin, **tokenizer_module}
+    # validate
+    mm_inputs = gemma4_plugin._get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, processor)
+    num_image_soft_tokens = 256 # when we use default max_soft_tokens=280
+    image_token = getattr(processor, "image_token")
+    boi_token = getattr(processor, "boi_token")
+    eoi_token = getattr(processor, "eoi_token")
+
+    expected_mm_type_ids = [[int(token_id == getattr(processor, "image_token_id")) for token_id in token_ids] for token_ids in BATCH_IDS]
+    check_inputs["expected_mm_messages"] = [
+        {"role": "user", "content": f"{boi_token}{image_token * num_image_soft_tokens}{eoi_token}What is in this image?"},
+        {"role": "assistant", "content": "A cat."},
+    ]
+    for key in ("num_soft_tokens_per_image",):
+        mm_inputs.pop(key, None)
+
+    mm_inputs["mm_token_type_ids"] = expected_mm_type_ids
+    check_inputs["expected_mm_inputs"] = mm_inputs
+    check_inputs["expected_no_mm_inputs"] = {"mm_token_type_ids": expected_mm_type_ids}
+    _check_plugin(**check_inputs)
+
+
 @pytest.mark.runs_on(["cpu", "mps"])
 @pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
 def test_internvl_plugin():

tests_v1/plugins/model_plugins/test_ulysses_cp.py

@@ -0,0 +1,62 @@
+# 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.multiprocessing as mp
+
+from llamafactory.v1.accelerator.interface import DistributedInterface
+from llamafactory.v1.config.model_args import ModelArguments
+from llamafactory.v1.core.model_engine import ModelEngine
+from llamafactory.v1.plugins.model_plugins.parallelization.sequence_parallel import (
+    SequenceParallelModelPlugin,
+    sequence_parallel_loss,
+)
+from llamafactory.v1.utils.env import find_available_port
+from llamafactory.v1.utils.pytest import dist_env
+
+
+def _test_sequence_parallel_loss(local_rank: int, world_size: int, master_port: int, cp_size: int, dp_size: int):
+    with dist_env(local_rank, world_size, master_port):
+        model_args = ModelArguments(model="llamafactory/tiny-random-qwen3")
+
+        # Initialize distributed interface with config
+        dist_config = {"cp_mode": "ulysses", "cp_size": cp_size, "dp_size": dp_size}
+        DistributedInterface(dist_config)
+
+        # Now create model engine
+        model_engine = ModelEngine(model_args=model_args)
+
+        # Apply sequence parallel plugin
+        SequenceParallelModelPlugin(dist_config.get("cp_mode", "ulysses"))(model_engine.model, dist_config)
+
+        model_inputs = {
+            "input_ids": torch.tensor([[1, 2, 3, 4, 5]]),
+            "labels": torch.tensor([[1, 2, 3, 4, 5]]),
+            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]]),
+            "position_ids": torch.tensor([[1, 2, 3, 4, 5]]),
+            "loss_weights": torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0]]),
+        }
+
+        loss = sequence_parallel_loss(model_engine.model, model_inputs)
+        assert loss is not None
+
+
+@pytest.mark.runs_on(["cuda", "npu"])
+@pytest.mark.require_distributed(2)
+@pytest.mark.parametrize("cp_size, dp_size", [(2, 1)])
+def test_sequence_parallel_loss(cp_size, dp_size):
+    master_port = find_available_port()
+    world_size = cp_size * dp_size
+    mp.spawn(_test_sequence_parallel_loss, args=(world_size, master_port, cp_size, dp_size), nprocs=world_size)