sgl-project/sglang · error · ValueError

The original encoder only has {num_hidden_layers} layers, bu

Error message

The original encoder only has {num_hidden_layers} layers, but you requested {len(self.encoder.layers)} layers.

What it means

SiglipVisionModel builds its encoder with an optional layer-count override; if the constructed encoder ends up with more layers than config.num_hidden_layers, the checkpoint is inconsistent with the request and init aborts. This mirrors HF's check that you cannot request more layers than the pretrained encoder has.

Source

Thrown at python/sglang/srt/models/siglip.py:268

        embed_dim = config.hidden_size

        self.embeddings = SiglipVisionEmbeddings(
            config, use_data_parallel=use_data_parallel
        )

        self.encoder = SiglipEncoder(
            config=config,
            qkv_backend=qkv_backend,
            act_layer=act_layer,
            flatten_batch=flatten_batch,
            use_data_parallel=use_data_parallel,
            quant_config=quant_config,
            prefix=add_prefix("encoder", prefix),
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.encoder.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.encoder.layers)} layers."
            )

        # VisionAttention in SiglipEncoderLayer is multihead attention
        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

    @property
    def device(self) -> torch.device:
        return self.embeddings.patch_embedding.weight.device

    def forward(
        self,
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.embeddings(pixel_values.to(self.device)).to(
            self.post_layernorm.weight.dtype
        )

View on GitHub (pinned to 0132848349)

Solutions

1. Set the layer override to a value <= num_hidden_layers (layer trimming only reduces)
2. Drop the override entirely to use all pretrained layers
3. Check config.json num_hidden_layers matches the checkpoint you loaded

Example fix

# before
override = 40  # checkpoint only has 27
# after
override = 16  # or None to use all layers

n = config.num_hidden_layers
assert override is None or override <= n, f"override {override} > {n} layers"

When it happens

Trigger: Passing a num_hidden_layers_override (or a config where the override exceeds num_hidden_layers) when constructing SiglipVisionModel, making len(self.encoder.layers) > config.num_hidden_layers at python/sglang/srt/models/siglip.py:268.

Common situations: Server args like --num-hiddenLayers-override style vision-layer trimming features, or a mismatched config.json where num_hidden_layers was edited down; typically an override value typo (larger instead of smaller).

Related errors

• The original encoder only has {num_hidden_layers} layers, bu
• The original encoder only has {num_hidden_layers} layers, bu
• patch_size must be greater than 1, otherwise this doesn't ma
• In ps_version 'v1', the height and width have not been swapp

AI-assisted analysis of sgl-project/sglang@0132848349 (2026-08-28). Data as JSON: /api/errors/f92459b3de02e001. Report an issue: GitHub.