解决TORCHDYNAMO跟fp8 scale冲突的问题

vllm/attention/layer.py

@@ -290,7 +290,8 @@ class Attention(nn.Module, AttentionLayerBase):
             # if attn_metadata.enable_kv_scales_calculation:
             #     self.calc_kv_scales(query, key, value)
             torch.ops.vllm.maybe_calc_kv_scales(query, key, value,
-                                                self.layer_name)  
+                                                self.layer_name)
+            self.check_fp8_overflow = False
 
         output_dtype = query.dtype
         if self.query_quant is not None:
@@ -358,12 +359,6 @@ class Attention(nn.Module, AttentionLayerBase):
                     query, key, value, self.layer_name)
 
     def calc_kv_scales(self, query, key, value):
-        self.check_fp8_overflow = False
-        if self.calculate_kv_scales == False :
-            if self.kv_cache_dtype in {"fp8", "fp8_e4m3"} and torch.abs(query).max().item()<=200 : #check fp8  overflow
-                return
-            if self.kv_cache_dtype in {"fp8_e5m2"} and torch.abs(query).max().item()>=0.01 : #check fp8 too small
-                return
         bias=0.0 # add bias to avoid q values are too small(or zeros) and scales are not correct
         if torch.abs(query).max().item() < 0.01:
             if self.kv_cache_dtype in {"fp8_e5m2"}:
@@ -601,7 +596,13 @@ def maybe_calc_kv_scales(
 
     # Only calculate if the layer's calculate_kv_scales flag is True
     # This flag gets set to False after the first forward pass
-
+    if self.check_fp8_overflow :
+        if self.kv_cache_dtype in {"fp8", "fp8_e4m3"} and torch.abs(query).max().item()>200 : #check fp8  overflow
+            self.calculate_kv_scales = True
+        if self.kv_cache_dtype in {"fp8_e5m2"} and torch.abs(query).max().item()<0.01 : #check fp8 too small
+            self.calculate_kv_scales = True
+    if not self.calculate_kv_scales:
+        return
     self.calc_kv_scales(query, key, value)