[Bugfix] Change kv scaling factor by param json on nvidia gpu (vllm-p…

…roject#11688)

Signed-off-by: bjmsong <bjmsong@126.com>
Co-authored-by: bjmsong <bjmsong@126.com>

vllm/model_executor/models/exaone.py

@@ -606,8 +606,9 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")

vllm/model_executor/models/granite.py

@@ -545,8 +545,9 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")

vllm/model_executor/models/llama.py

@@ -452,8 +452,9 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")

vllm/model_executor/models/solar.py

@@ -565,8 +565,9 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")

vllm/worker/model_runner.py

@@ -1136,7 +1136,8 @@ def load_model(self) -> None:
                 self.prompt_adapter_manager.create_prompt_adapter_manager(
                     self.model))
 
-        if self.kv_cache_dtype == "fp8" and current_platform.is_rocm():
+        if self.kv_cache_dtype == "fp8" and (current_platform.is_rocm()
+                                             or current_platform.is_cuda()):
             # Currently only ROCm accepts kv-cache scaling factors
             # via quantization_param_path and this will be deprecated
             # in the future.