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[Bugfix] Allow fp8_e5m2 KV cache on W4A16 compressed-tensors models (V100/SM70) #49

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[Bugfix] Allow fp8_e5m2 KV cache on W4A16 compressed-tensors models (V100/SM70) #49

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12 changes: 12 additions & 0 deletions vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,6 +180,18 @@ def get_quant_method(
return quant_method

if isinstance(layer, Attention):
# Only register a KV-cache quant method when the checkpoint
# actually ships KV scales. Without this guard, compressed-
# tensors W4A16 models (which set kv_cache_scheme=None) are
# routed through CompressedTensorsKVCacheMethod and then
# misclassified by should_load_quant_weights() in
# attention.py:166 as "FP8 checkpoints", which refuses
# --kv-cache-dtype fp8_e5m2 at attention.py:167. That ban
# is the only FP8 KV path Triton supports on V100/SM70
# (Triton on SM70 rejects fp8e4nv), so without this fix
# V100 deployments cannot use FP8 KV cache on a W4A16 model.
if self.kv_cache_scheme is None:
return None
return CompressedTensorsKVCacheMethod(self)
if isinstance(layer, FusedMoE):
return CompressedTensorsMoEMethod.get_moe_method(
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