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[Fix][Spec Decode] Fix llama4 draft loading with different quantization #27136

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merged 2 commits into from
Oct 21, 2025
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[Fix][Spec Decode] Fix llama4 draft loading with different quantization #27136

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43bc12f
[fix][spec decode] Fix llama4 draft model loading with different quan…
linzebing Oct 18, 2025
35b96a2
make gemini happy
linzebing Oct 18, 2025
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27 changes: 17 additions & 10 deletions vllm/model_executor/models/llama4_eagle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,16 +60,23 @@ def __init__(
prefix=maybe_prefix(prefix, "embed_tokens"),
)

self.layers = nn.ModuleList(
[
Llama4DecoderLayer(
vllm_config=vllm_config,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
config=self.config,
)
for i in range(self.config.num_hidden_layers)
]
)
# Temporarily modify vllm_config.quant_config for draft model layers
original_quant_config = vllm_config.quant_config
vllm_config.quant_config = quant_config
try:
self.layers = nn.ModuleList(
[
Llama4DecoderLayer(
vllm_config=vllm_config,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
config=self.config,
)
for i in range(self.config.num_hidden_layers)
]
)
finally:
# Restore original quant_config
vllm_config.quant_config = original_quant_config
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when do we see the exception?

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When target model and draft model have different quantization, e.g. target model is bf16, while draft model uses fp8 quantization.

self.fc = torch.nn.Linear(
self.config.hidden_size * 2, self.config.hidden_size, bias=False
)
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