[train] Add KTransformers VLM fine-tuning support (#10760)

examples/ktransformers/train_lora/qwen3vlmoe_lora_sft_kt.yaml

@@ -0,0 +1,51 @@
+### model
+model_name_or_path: Qwen/Qwen3-VL-30B-A3B-Instruct
+image_max_pixels: 262144
+video_max_pixels: 16384
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_alpha: 16
+lora_target: all
+
+### dataset
+dataset: mllm_demo
+template: qwen3_vl
+cutoff_len: 512
+overwrite_cache: true
+preprocessing_num_workers: 4
+dataloader_num_workers: 1
+
+### output
+output_dir: saves/KT_FT_qwen3vl_30b_a3b
+logging_steps: 1
+save_steps: 100
+plot_loss: true
+overwrite_output_dir: true
+report_to: none
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-4
+num_train_epochs: 1.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 360000000
+
+### ktransformers
+use_kt: true
+kt_cpu_activation: retain
+kt_config:
+  kt_expert_weight_format: bf16
+  kt_backend: AMXBF16
+  kt_num_threads: 96
+  kt_tp_enabled: true
+  kt_threadpool_count: 2
+  kt_max_cache_depth: 2
+  kt_share_backward_bb: true

src/llamafactory/hparams/model_args.py

@@ -558,6 +558,12 @@ class KTransformersArguments:
             raise TypeError("LLaMA-Factory `kt_config` must be a flat mapping.")
 
         config = dict(raw_config)
+        # Transformers-KT adds these defaults to the user-owned mapping during
+        # TrainingArguments.__post_init__. They are transport metadata, not
+        # conflicting user overrides.
+        for key in ("enabled", "kt_skip_expert_loading"):
+            if config.get(key) is True:
+                config.pop(key)
         conflicts = sorted(set(config) & self._KT_DERIVED_KEYS)
         if conflicts:
             raise ValueError(f"These `kt_config` values are derived from LLaMA-Factory arguments: {conflicts}.")
@@ -608,8 +614,9 @@ class KTransformersArguments:
         advanced_config: dict[str, Any] | None = None,
     ) -> dict[str, Any]:
         r"""Map LLaMA-Factory-owned training values to the public KT configuration."""
-        if getattr(finetuning_args, "finetuning_type", None) != "lora":
+        finetuning_type = getattr(finetuning_args, "finetuning_type", None)
-            raise ValueError("KTransformers thin integration currently supports LoRA finetuning only.")
+        if finetuning_type not in {"lora", "full"}:
+            raise ValueError("KTransformers supports LoRA and full-parameter finetuning.")
 
         kt_config = dict(advanced_config or {})
         configured_capacity = kt_config.pop("kt_model_max_length", None)
@@ -634,8 +641,8 @@ class KTransformersArguments:
                 "kt_lora_expert_num": self.kt_lora_expert_num,
                 "kt_lora_expert_intermediate_size": self.kt_lora_expert_intermediate_size,
                 "kt_activation_policy": self.get_kt_activation_policy(),
-                "kt_train_mode": "lora",
+                "kt_train_mode": finetuning_type,
-                "kt_full_weight_grad": False,
+                "kt_full_weight_grad": finetuning_type == "full",
             }
         )
         return {key: value for key, value in kt_config.items() if value is not None}
@@ -668,13 +675,29 @@ class KTransformersArguments:
             self._get_advanced_kt_config(training_args),
         )
         update_kt_config = getattr(training_args, "update_kt_config", None)
-        if not callable(update_kt_config):
-            raise RuntimeError(
-                "The installed Transformers-KT does not provide `TrainingArguments.update_kt_config()`."
-            )
-
         adapter_dir = self._resolve_kt_adapter_artifact_dir("training")
-        update_kt_config(kt_config, adapter_name_or_path=adapter_dir)
+        if callable(update_kt_config):
+            update_kt_config(kt_config, adapter_name_or_path=adapter_dir)
+            return
+
+        # transformers-kt 5.6 exposes the config object but predates the public
+        # update helper. Keep its flat loading config and Accelerate's nested
+        # plugin config synchronized without requiring another dependency pin.
+        from kt_kernel.sft import KTConfig
+
+        supported_keys = {item.name for item in fields(KTConfig)}
+        compatible_config = {key: value for key, value in kt_config.items() if key in supported_keys}
+        if self.get_kt_activation_policy()["gpu"] == "recompute":
+            compatible_config.setdefault("kt_share_cache_pool", True)
+
+        hf_kt_config = getattr(training_args, "hf_kt_config", None)
+        if hf_kt_config is None or not isinstance(getattr(hf_kt_config, "_kt_config", None), dict):
+            raise RuntimeError("The installed Transformers-KT does not expose a mutable KT configuration.")
+        hf_kt_config._kt_config.update(compatible_config)
+
+        accelerator_config = getattr(training_args, "accelerator_config", None)
+        if accelerator_config is not None:
+            accelerator_config.kt_config = {"enabled": True, "kt_config": compatible_config}
 
     def configure_kt_loading(self, finetuning_args: Any, model_max_length: int | None) -> None:
         r"""Configure KT model loading for inference and evaluation."""

src/llamafactory/model/loader.py

@@ -196,13 +196,22 @@ def load_model(
 
     # Conv3D is not recommended when using torch 2.9.x
     if is_torch_version_greater_than("2.9.0") and not is_torch_version_greater_than("2.10.0"):
-        if any(isinstance(m, torch.nn.Conv3d) for m in model.modules()):
+        conv3d_modules = [module for module in model.modules() if isinstance(module, torch.nn.Conv3d)]
+        kt_conv3d_ready = (
+            model_args.use_kt
+            and is_trainable
+            and bool(conv3d_modules)
+            and all(getattr(module, "_kt_conv3d_compatible", False) for module in conv3d_modules)
+        )
+        if conv3d_modules and not kt_conv3d_ready:
             raise ValueError(
                 "Unsupported torch version detected: torch 2.9.x with Conv3D. "
                 "This combination is known to cause severe performance regression. "
                 "Please downgrade torch to <2.9 or remove Conv3D. "
                 "See https://github.com/pytorch/pytorch/issues/166122"
             )
+        elif kt_conv3d_ready:
+            logger.info_rank0("Using KTransformers instance-scoped Conv3D fallback for torch 2.9.x VLM training.")
 
     if not is_trainable:
         model.requires_grad_(False)

src/llamafactory/model/model_utils/checkpointing.py

@@ -51,8 +51,11 @@ def _get_gradient_checkpointing_kwargs(model_args: "ModelArguments") -> dict[str
 
     try:
         from kt_kernel.sft import get_activation_checkpoint_context_fn
-    except (ImportError, ModuleNotFoundError) as exc:
+    except (ImportError, ModuleNotFoundError):
-        raise RuntimeError("The installed kt-kernel does not provide the activation checkpoint context API.") from exc
+        logger.warning_rank0_once(
+            "The installed kt-kernel predates the activation checkpoint context API; using non-reentrant checkpointing."
+        )
+        return {"use_reentrant": False}
 
     return {"use_reentrant": False, "context_fn": get_activation_checkpoint_context_fn()}