DeepSeek-R1-Channel-INT8调用rmsquant融合。

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -666,7 +666,8 @@ class CompressedTensorsLinearMethod(LinearMethodBase):
     def apply(self,
               layer: torch.nn.Module,
               x: torch.Tensor,
-              bias: Optional[torch.Tensor] = None):
+              bias: Optional[torch.Tensor] = None, 
+              input_quant_args: Optional[list[torch.Tensor]] = None):
         """
         Use the output of create_weights and the CompressedTensorsScheme
         associated with the layer to apply the forward pass with the
@@ -677,7 +678,7 @@ class CompressedTensorsLinearMethod(LinearMethodBase):
         scheme = layer.scheme
         if scheme is None:
             raise ValueError("A scheme must be defined for each layer")
-        return scheme.apply_weights(layer, x, bias=bias)
+        return scheme.apply_weights(layer, x, bias=bias, input_quant_args=input_quant_args)
 
 
 class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -111,7 +111,8 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
         self.kernel.process_weights_after_loading(layer)
 
     def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+                      bias: Optional[torch.Tensor], 
+                      input_quant_args: Optional[list[torch.Tensor]] = None) -> torch.Tensor:
         
         # return self.kernel.apply_weights(layer, x, bias)
 
@@ -122,5 +123,5 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                                  input_zero_point=layer.input_zero_point,
                                  azp_adj=layer.azp_adj,
                                  bias=bias,
-                                 w8a8_strategy=self.w8a8_strategy)
-
+                                 w8a8_strategy=self.w8a8_strategy,
+                                 input_quant_args=input_quant_args)
\ No newline at end of file

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -392,20 +392,26 @@ def apply_int8_linear(
     azp_adj: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
     w8a8_strategy:Optional[int]=0,
+    input_quant_args: Optional[list[torch.Tensor]] = None
 ):
     # ops.scaled_int8_quant supports both dynamic and static quant.
     # * dynamic, layer.input_scale is None and x_scale computed from x.
     # * static, layer.input_scale is scalar and x_scale is input_scale.
-    symmetric = azp_adj is None
-    if input_scale is None and input_zero_point is None and symmetric is True:
-        x_q, x_scale=per_token_quant_int8(input)
-        x_zp =None
 
-    else:
-        x_q, x_scale, x_zp = ops.scaled_int8_quant(input,
-                                                   input_scale,
-                                                   input_zero_point,
-                                                   symmetric=symmetric)
+    if envs.USE_FUSED_RMS_QUANT and input_quant_args is not None:
+        assert len(input_quant_args) == 2
+        x_zp =None
+        x_q, x_scale = input_quant_args
+    else: # not USE_FUSED_RMS_QUANT
+        symmetric = azp_adj is None
+        if input_scale is None and input_zero_point is None and symmetric is True:
+            x_q, x_scale=per_token_quant_int8(input)
+            x_zp =None
+        else:
+            x_q, x_scale, x_zp = ops.scaled_int8_quant(input,
+                                                       input_scale,
+                                                       input_zero_point,
+                                                       symmetric=symmetric)
 
     if x_zp is not None:
         # Currently, static is always per-tensor and dynamic is per-token