[WIP]for full-fine-tune-support

[poryfly]

What does this PR do?

support full finetune for KT

Fixes # (issue)

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[gemini-code-assist]

Code Review

This pull request enhances the KT integration by adding configuration for training modes and full weight gradients, and updating the Trainer to account for KT-specific trainable parameters that are not standard nn.Module members. The changes include logic to log these parameters and inject them into the optimizer. Review feedback recommends preventing double-counting of parameters in logs, logging exceptions during parameter inspection instead of silent failure, and using unwrap_peft_model to correctly identify the training mode when PEFT is used.

[gemini-code-assist]

The current logic for counting KT trainable parameters might lead to double counting if some parameters returned by get_kt_trainable_params are already present in model.parameters(). This is consistent with the check performed during parameter injection in _prepare_for_training. Filtering these out ensures the reported counts are accurate.

                kt_trainable = get_kt_trainable_params(kt_model_for_count)
                model_param_ids = {id(p) for p in model.parameters()}
                kt_trainable = [p for p in kt_trainable if id(p) not in model_param_ids] if kt_trainable else []
                kt_trainable_numel = sum(p.numel() for p in kt_trainable)

[gemini-code-assist]

Catching a broad Exception and silently passing can make it difficult to diagnose issues if the parameter counting logic fails unexpectedly. It is recommended to at least log the exception at a debug level to aid in troubleshooting.

Suggested change

	except Exception:
	pass # Non-critical: best-effort count
	except Exception as e:
	logger.debug(f"Could not count KT trainable parameters: {e}")

[gemini-code-assist]

If kt_model is a PeftModel, the _kt_wrappers attribute might be located on the base model rather than the wrapper itself. Using unwrap_peft_model ensures that the check for _full_weight_grad is performed on the correct module level where these attributes are typically attached.

Suggested change

	has_full_weight_grad = any(
	getattr(w, "_full_weight_grad", False)
	for w in getattr(kt_model, "_kt_wrappers", [])
	)
	has_full_weight_grad = any(
	getattr(w, "_full_weight_grad", False)
	for w in getattr(unwrap_peft_model(kt_model), "_kt_wrappers", [])
	)