[train] Harden KTransformers MoE LoRA SFT integration (#10738)

docs/zh/advanced/ktransformers.md

@@ -0,0 +1,119 @@
+# KTransformers LoRA SFT
+
+KTransformers（KT）将 MoE routed experts 放在 CPU 执行，LLaMA-Factory 继续负责数据、LoRA 参数和训练入口。
+当前生产范围是 routed-BF16 LoRA 与 routed-INT8 LoRA；Accelerate 配置只负责 FSDP2，不再保存 KT 参数。
+
+## 安装检查
+
+必须同时安装带 KT 公共接口的 `ktransformers`、`transformers-kt` 和 `accelerate-kt`。启动前可检查：
+
+```bash
+python - <<'PY'
+from accelerate import Accelerator
+from kt_kernel.sft import resolve_kt_pretrained_artifacts
+from transformers import TrainingArguments
+
+assert hasattr(TrainingArguments, "update_kt_config")
+assert "adapter_only" in __import__("inspect").signature(Accelerator.get_state_dict).parameters
+print(resolve_kt_pretrained_artifacts)
+PY
+```
+
+## 配置
+
+KT 只有一个用户配置源：训练 YAML。LoRA rank、alpha、dropout 和 runtime capacity 由 LLaMA-Factory
+标准字段派生；不要在 `kt_config` 中重复填写。
+
+BF16 示例：
+
+```yaml
+finetuning_type: lora
+lora_rank: 8
+lora_alpha: 16
+lora_target: all
+
+use_kt: true
+disable_gradient_checkpointing: false
+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
+```
+
+INT8 还需要相互匹配的 routed expert 与 BF16 non-expert cache：
+
+```yaml
+kt_weight_path: /abs/path/to/routed-int8-experts
+kt_non_expert_weight_path: /abs/path/to/bf16-non-expert-cache
+kt_config:
+  kt_expert_weight_format: int8
+  kt_backend: auto
+  kt_weight_lifecycle: persistent
+```
+
+完整配置见：
+
+- `examples/ktransformers/train_lora/qwen3_5moe_lora_sft_kt.yaml`
+- `examples/ktransformers/train_lora/deepseek_v3_int8_lora_sft_kt.yaml`
+
+Activation 策略：
+
+| `disable_gradient_checkpointing` | `kt_cpu_activation` | CPU / GPU |
+| --- | --- | --- |
+| `false` | `recompute` 或省略 | recompute / recompute |
+| `false` | `retain` | retain / recompute |
+| `true` | `retain` 或省略 | retain / retain |
+| `true` | `recompute` | 不支持，启动前报错 |
+
+## 启动与复用
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
+  --config_file examples/ktransformers/accelerate/fsdp2_kt_bf16.yaml \
+  src/train.py examples/ktransformers/train_lora/qwen3_5moe_lora_sft_kt.yaml
+```
+
+输出 adapter 同时包含 standard PEFT 与 fused expert LoRA。
+
+## 新进程加载
+
+对话或评测必须使用本地的完整 KT adapter 目录，并重复训练时的 LoRA 形状配置：`finetuning_type`、
+`lora_rank`、`lora_alpha`、`lora_dropout`，以及相同的 KT base weight 配置。routed INT8 尤其要沿用训练时
+的 `kt_weight_path` 和 `kt_non_expert_weight_path`。
+
+```yaml
+model_name_or_path: /abs/path/to/base-model
+adapter_name_or_path: /abs/path/to/output/checkpoint-300
+finetuning_type: lora
+lora_rank: 8
+lora_alpha: 16
+lora_dropout: 0.0
+
+use_kt: true
+kt_cpu_activation: retain
+kt_config:
+  kt_expert_weight_format: bf16
+  kt_backend: AMXBF16
+  kt_num_threads: 96
+```
+
+```bash
+llamafactory-cli chat path/to/kt_adapter_infer.yaml
+llamafactory-cli eval path/to/kt_adapter_eval.yaml
+```
+
+目录必须包含 standard PEFT adapter 文件；使用 fused routed-expert LoRA 时，还必须包含
+`fused_expert_lora.safetensors` 和 `kt_adapter_manifest.json`。LLaMA-Factory 先加载 standard PEFT，随后由
+KT 校验并恢复 fused artifact。`adapter_folder` 可以选择本地子目录；越出 adapter 根目录的路径和 Hub
+adapter ID 会在加载模型前报错，Hub bundle 需要先完整下载到本地。
+
+续训应保留原训练 YAML，并使用 `resume_from_checkpoint`。分布式 optimizer checkpoint 暂要求相同 world
+size。artifact 缺失、hash 不匹配或来源模型不一致时会直接失败，不会退回源 checkpoint。
+
+不要同时启用 Transformers/FSDP activation checkpointing、Unsloth GC，也不要把 `kt_config` 放入
+Accelerate YAML。每次训练都应确认 loss/grad finite、base model 未修改，并验证 standard/router/fused LoRA
+均包含非零更新。

docs/zh/index.rst

@@ -34,6 +34,7 @@ LlamaFactory 文档
 
    advanced/lora-and-quantization/lora
    advanced/lora-and-quantization/quantization
+   advanced/ktransformers
    advanced/distributed/fsdp
    advanced/distributed/deepspeed
    advanced/distributed/parallel-dp-tp-ep-sp-cp