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

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liyuemathematician
2026-08-18 19:49:47 +08:00
committed by GitHub
parent f28afaf635
commit ff6d4d12ee
4 changed files with 99 additions and 13 deletions
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43
src/llamafactory/hparams/model_args.py
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@@ -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":
raise ValueError("KTransformers thin integration currently supports LoRA finetuning only.")
finetuning_type = getattr(finetuning_args, "finetuning_type", None)
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_full_weight_grad": False,
"kt_train_mode": finetuning_type,
"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."""