sgl-project/sglang · error · ValueError

Dit target weight name {target_name} already exists in lora_

Error message

Dit target weight name {target_name} already exists in lora_adapters[{lora_nickname}]

What it means

load_lora_adapter stores LoRA B weights keyed by target parameter name inside lora_adapters[nickname]; if two source entries in the checkpoint map to the same target_name (after SwiGLU fc1 name swapping), the second insert is rejected to avoid silently overwriting weights.

Source

Thrown at python/sglang/multimodal_gen/runtime/pipelines_core/lora/pipeline.py:914

            if merge_index is not None:
                to_merge_params[target_name][merge_index] = weight
                # A/B of one fused layer must be laid out together (GQA B cannot stack).
                if target_name.endswith((".lora_A", ".lora_B")):
                    continue
                if len(to_merge_params[target_name]) == num_params_to_merge:
                    sorted_tensors = [
                        to_merge_params[target_name][i]
                        for i in range(num_params_to_merge)
                    ]
                    # Use stack instead of cat because it needs to be compatible with TP.
                    weight = torch.stack(sorted_tensors, dim=0)
                    del to_merge_params[target_name]
                else:
                    continue

            weight = _swap_peft_swiglu_fc1_lora_b(name, target_name, weight)
            if target_name in self.lora_adapters[lora_nickname]:
                raise ValueError(
                    f"Dit target weight name {target_name} already exists in lora_adapters[{lora_nickname}]"
                )
            self.lora_adapters[lora_nickname][target_name] = weight.to(self.device)

        _store_fused_lora_groups(
            self.lora_adapters[lora_nickname],
            to_merge_params,
            adapter_lora_alpha,
            self.device,
        )
        transformer = self.modules["transformer"]
        if isinstance(transformer, BaseDiT):
            self.lora_adapters[lora_nickname] = transformer.prepare_lora_adapter(
                self.lora_adapters[lora_nickname]
            )

        self.loaded_adapter_paths[lora_nickname] = lora_path
        self.loaded_adapter_alphas[lora_nickname] = adapter_lora_alpha

View on GitHub (pinned to 0132848349)

Solutions

1. Inspect the checkpoint keys (safetensors index) and remove the duplicate entry
2. Re-export the LoRA adapter from the original training framework
3. If this is a pipeline bug for your model arch, report it with the checkpoint key list

from safetensors import safe_open
with safe_open(path, framework='pt') as f:
    keys = list(f.keys())
assert len(keys) == len(set(keys)) and len(keys) <= expected_param_count

try:
    pipeline.set_lora(...)
except ValueError as e:
    if 'already exists in lora_adapters' in str(e): log_checkpoint_issue(path)

When it happens

Trigger: A LoRA checkpoint containing duplicate/aliased weight names, e.g. both the original and _swap_peft_swiglu_fc1_lora_b-renamed variants of the same fc1 lora_b tensor; loading such an adapter via set_lora.

Common situations: Exporting a PEFT adapter for a SwiGLU model that duplicates gate/up names; converting checkpoints between naming conventions that produce collisions; corrupted or re-saved adapters.

Related errors

• scale_shift_table must have shape [9, D]
• LoRA batch_info must provide max_len or seg_lens.
• LoRA batch_info must provide max_len or seg_lens.
• lora_nickname cannot be empty
• Failed to set LoRA adapter: {str(e)}

AI-assisted analysis of sgl-project/sglang@0132848349 (2026-08-28). Data as JSON: /api/errors/3ec1c942be02d72e. Report an issue: GitHub.