[Feature] Add Multi-Token Prediction (MTP) module implementation

tests/train/test_trainer.py

@@ -112,6 +112,7 @@ def create_pg(self, device):
 
     @patch("xtuner.v1.train.trainer.is_hf_model_path", Mock(return_value=True))
     @patch("xtuner.v1.train.trainer.Trainer.build_engine", Mock(side_effect=lambda *args, **kwargs: FakeEngine()))
+    @patch("xtuner.v1.train.trainer.Trainer._prepare_model_input", Mock(return_value=[]))
     @prepare
     def test_save_hf_interval(self):
         """Test save_hf is called at correct intervals during training."""
@@ -184,6 +185,7 @@ def test_save_hf_interval(self):
 
     @patch("xtuner.v1.train.trainer.is_hf_model_path", Mock(return_value=True))
     @patch("xtuner.v1.train.trainer.Trainer.build_engine", Mock(side_effect=lambda *args, **kwargs: FakeEngine()))
+    @patch("xtuner.v1.train.trainer.Trainer._prepare_model_input", Mock(return_value=[]))
     @prepare
     def test_save_checkpoint_interval(self):
         self.create_pg(DEVICE)
@@ -258,6 +260,7 @@ def test_save_checkpoint_interval(self):
 
     @patch("xtuner.v1.train.trainer.is_hf_model_path", Mock(return_value=True))
     @patch("xtuner.v1.train.trainer.Trainer.build_engine", Mock(side_effect=lambda *args, **kwargs: FakeEngine()))
+    @patch("xtuner.v1.train.trainer.Trainer._prepare_model_input", Mock(return_value=[]))
     @prepare
     def test_resume(self):
         self.create_pg(DEVICE)
@@ -738,6 +741,7 @@ def __call__(self, checkpoint, step, epoch, total_step, total_epoch):
         assert len(loaded.get_hooks(HookStage.AFTER_SAVE_DCP)) == 1
 
 
+@patch("xtuner.v1.train.trainer.Trainer._prepare_model_input", Mock(return_value=[]))
 @patch("xtuner.v1.train.trainer.Trainer.build_engine", Mock(side_effect=lambda *args, **kwargs: FakeEngine()))
 def test_resume_and_load_checkpoint_cfg(tmp_path: Path):
     # 0. prepare environment

xtuner/v1/model/__init__.py

@@ -23,7 +23,7 @@
 from .dense.qwen3 import Qwen3Dense0P6BConfig, Qwen3Dense4BConfig, Qwen3Dense8BConfig, Qwen3DenseConfig
 from .moe.deepseek_v3 import DeepSeekV3Config
 from .moe.gpt_oss import GptOss21BA3P6Config, GptOss117BA5P8Config, GptOssConfig
-from .moe.moe import BalancingLossConfig, MoE, MoEModelOutputs, ZLossConfig
+from .moe.moe import BalancingLossConfig, MoE, MoEConfig, MoEModelOutputs, ZLossConfig
 from .moe.qwen3 import Qwen3MoE30BA3Config, Qwen3MoEConfig, Qwen3MoEFoPEConfig
 
 
@@ -87,6 +87,7 @@ def get_model_config_from_hf(model_path: Path):
     "get_model_config",
     "get_model_config_from_hf",
     "MoE",
+    "MoEConfig",
     "MoEModelOutputs",
     "BalancingLossConfig",
     "ZLossConfig",

xtuner/v1/model/base.py

@@ -1374,6 +1374,9 @@ def _load_fused_hf_param(
                 continue
             _loaded_tensor.append(weight.to(local_tensor.device))
 
+        if not _loaded_tensor:
+            return missing_keys
+
         if not hf_keys:
             # fp8 pad
             assert self.config.float8_cfg is not None

xtuner/v1/model/compose/qwen3_5/qwen3_5_config.py

@@ -1,4 +1,4 @@
-from xtuner.v1.model.base import TransformerConfig
+from xtuner.v1.model.moe.moe import MoEConfig
 from xtuner.v1.model.moe.qwen3_5_text import Qwen3_5_VLTextMoE35BA3BConfig
 from xtuner.v1.utils import get_logger
 
@@ -19,7 +19,7 @@ class Qwen3_5_ProjectorConfig(Qwen3VLProjectorConfig):
 class Qwen3_5_BaseConfig(Qwen3VLBaseConfig):
     vision_config: Qwen3_5_VisionConfig
     projector_config: Qwen3_5_ProjectorConfig
-    text_config: TransformerConfig
+    text_config: MoEConfig
 
     image_token_id: int = 248056
     video_token_id: int = 248057
@@ -30,4 +30,4 @@ class Qwen3_5_BaseConfig(Qwen3VLBaseConfig):
 class Qwen3_5_VLMoE35BA3Config(Qwen3_5_BaseConfig):
     vision_config: Qwen3_5_VisionConfig = Qwen3_5_VisionConfig()
     projector_config: Qwen3_5_ProjectorConfig = Qwen3_5_ProjectorConfig()
-    text_config: TransformerConfig = Qwen3_5_VLTextMoE35BA3BConfig()
+    text_config: MoEConfig = Qwen3_5_VLTextMoE35BA3BConfig()

xtuner/v1/model/dense/qwen3vl_text.py

@@ -1,9 +1,10 @@
 import re
 
 import torch
+import torch.nn.functional as F
 
 from xtuner.v1.data_proto import SequenceContext
-from xtuner.v1.loss import CELossContext
+from xtuner.v1.loss import BaseLossContext
 from xtuner.v1.model.base import ModelOutputs
 
 from .qwen3 import Qwen3Dense, Qwen3Dense4BConfig, Qwen3Dense8BConfig
@@ -34,10 +35,10 @@ def _deepstack_process(
         hidden_states[visual_pos_masks, :] = local_this
         return hidden_states
 
-    def forward(
+    def forward(  # type: ignore[override]
         self,
         seq_ctx: SequenceContext,  # todo(@yehaochen): support intra layer micro-batch
-        loss_ctx: CELossContext,
+        loss_ctx: dict[str, BaseLossContext | list[BaseLossContext]] | None = None,
     ) -> ModelOutputs:
         input_ids = seq_ctx.input_ids
         position_ids = seq_ctx.position_ids
@@ -78,11 +79,18 @@ def forward(
 
         hidden_states = self.norm(hidden_states)
 
-        loss, (logits, extra_info) = self.lm_head(hidden_states, loss_ctx)
-        output["loss"] = loss
-        output["logits"] = logits
-        output["extra_info"] = extra_info
-        return ModelOutputs(**output)  # type: ignore[typeddict-item]
+        if loss_ctx is None:
+            # Inference mode
+            logits = F.linear(hidden_states, self.lm_head.weight, self.lm_head.bias)
+            output["logits"] = logits
+        else:
+            # Training mode
+            loss, (logits, extra_info) = self.lm_head(hidden_states, loss_ctx["lm"])  # type: ignore[call-overload]
+            output["loss"] = loss
+            output["logits"] = logits
+            output["extra_info"] = extra_info
+
+        return ModelOutputs(**output)
 
 
 class Qwen3VLTextDense4BConfig(Qwen3Dense4BConfig):