[Bugfix] Only add Attention.kv_scale if kv cache quantization is enabled

vllm/attention/layer.py

@@ -9,6 +9,7 @@
 from vllm.config import CacheConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.fp8 import Fp8KVCacheMethod
 
 
 class Attention(nn.Module):
@@ -56,15 +57,19 @@ def __init__(
         quant_method = quant_config.get_quant_method(
             self) if quant_config else None
         if quant_method is not None:
-            if self.kv_cache_dtype == "fp8_e5m2":
-                raise ValueError("fp8_e5m2 kv-cache is not supported with "
-                                 "fp8 checkpoints.")
-            # When FP8 quantization is enabled, we make a parameter
-            # "kv_scale" so that it can be loaded from FP8 checkpoint.
-            # The kv_scale will then be converted back
-            # to self._kv_scale in a native float32 value after weight loading.
-            self.quant_method = quant_method
-            self.quant_method.create_weights(self)
+            assert isinstance(quant_method, Fp8KVCacheMethod)
+            # TODO (mgoin): kv cache dtype should be specified in the FP8
+            # checkpoint config and become the "auto" behavior
+            if "fp8" in self.kv_cache_dtype:
+                if self.kv_cache_dtype == "fp8_e5m2":
+                    raise ValueError("fp8_e5m2 kv-cache is not supported with "
+                                     "fp8 checkpoints.")
+                # When FP8 quantization is enabled, we make a parameter
+                # "kv_scale" so that it can be loaded from FP8 checkpoint.
+                # The kv_scale will then be converted back to self._kv_scale
+                # in a native float32 value after weight loading.
+                self.quant_method = quant_method
+                self.quant_method.create_weights(self)
 
         # During model initialization, the default dtype is set as the model
         # weight and activation dtype.