sgl-project/sglang · error · ValueError

Found static activation scheme for checkpoint that was not s

Error message

Found static activation scheme for checkpoint that was not serialized fp8.

What it means

When an FP8 MoE quant config declares activation_scheme="static", the loader expects per-expert input scales (w13/w2 input_scale tensors) which only exist in FP8-serialized checkpoints. If the checkpoint was not FP8-serialized, those scales cannot exist, so create_fp8_moe_weight_ raises this contradiction.

Source

Thrown at python/sglang/srt/layers/quantization/fp8.py:1384

        # If loading fp8 checkpoint, pass the weight loaders.
        # If loading an fp16 checkpoint, do not (we will quantize in
        #   process_weights_after_loading()
        if quant_config.is_checkpoint_fp8_serialized:
            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
            set_weight_attrs(w2_weight_scale, extra_weight_attrs)

            if _is_hip and _use_hip_int4:
                extra_weight_attrs.update(
                    {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
                )
                set_weight_attrs(w13_weight_scale1, extra_weight_attrs)
                set_weight_attrs(w2_weight_scale1, extra_weight_attrs)

        # INPUT_SCALES
        if quant_config.activation_scheme == "static":
            if not quant_config.is_checkpoint_fp8_serialized:
                raise ValueError(
                    "Found static activation scheme for checkpoint that "
                    "was not serialized fp8."
                )

            w13_input_scale = torch.nn.Parameter(
                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
            )
            layer.register_parameter("w13_input_scale", w13_input_scale)
            set_weight_attrs(w13_input_scale, extra_weight_attrs)

            w2_input_scale = torch.nn.Parameter(
                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
            )
            layer.register_parameter("w2_input_scale", w2_input_scale)
            set_weight_attrs(w2_input_scale, extra_weight_attrs)

        else:
            layer.w13_input_scale = None

View on GitHub (pinned to 0132848349)

Solutions

1. Use a checkpoint that actually ships FP8 weights and static input scales
2. Change activation_scheme to "dynamic" (works with on-the-fly quantization)
3. Remove/fix the quantization_config if the model is not quantized

Example fix

// before
{"quant_method":"fp8","activation_scheme":"static"}  // on BF16 weights
// after
{"quant_method":"fp8","activation_scheme":"dynamic"}

qcfg = model_config.quantization_config
if qcfg.get("activation_scheme") == "static" and not qcfg.get("is_checkpoint_fp8_serialized", False):
    raise SystemExit("static activation scheme requires an FP8-serialized checkpoint")

def static_scheme_consistent(qcfg: dict) -> bool:
    return qcfg.get("activation_scheme") != "static" or bool(qcfg.get("is_checkpoint_fp8_serialized"))

When it happens

Trigger: Creating MoE weights with quant_config.activation_scheme == "static" and quant_config.is_checkpoint_fp8_serialized == False — a config claiming static FP8 activation quantization over a non-FP8 checkpoint.

Common situations: Hand-written quantization_configs copied from FP8 models onto BF16 weights; configs produced by tools that always emit "static" regardless of serialization; misconverted checkpoints.

Related errors

• QuantConfig has static quantization, but found activation sc
• The hpc_ops MoE runner backend requires static activation sc
• The hpc_ops MoE runner backend only supports FP8-quantized M
• For FP8 Fused MoE layer, we require either per tensor or cha
• The output_size of gate's and up's weight = {intermediate_si

AI-assisted analysis of sgl-project/sglang@0132848349 (2026-08-28). Data as JSON: /api/errors/3ed5a48a68a8c574. Report an issue: GitHub.