[BUG] fixed fp8 conflict with aqlm (#4307)

Fixes fp8 iterface which broke in AQLM merge.

Author: robertgshaw2-redhat

.buildkite/test-pipeline.yaml

@@ -96,6 +96,9 @@ steps:
 - label: Metrics Test
   command: pytest -v -s metrics
 
+- label: Quantization Test
+  command: pytest -v -s quantization
+
 - label: Benchmarks
   working_dir: "/vllm-workspace/.buildkite"
   commands:

vllm/model_executor/layers/linear.py

@@ -34,9 +34,19 @@ def create_weights(self, layer: torch.nn.Module,
                        output_partition_sizes: List[int], input_size: int,
                        output_size: int, params_dtype: torch.dtype,
                        **extra_weight_attrs):
-        """Create weights for a linear layer.
-
-        The weights will be set as attributes of the layer."""
+        """Create weights for a linear layer. 
+           The weights will be set as attributes of the layer.
+        
+        Args:
+            layer: The layer that is using the LinearMethodBase factory.
+            input_size_per_partition: Size of the weight input dim on rank X.
+            output_partition_sizes: Sizes of the output dim of each logical 
+                weight on rank X. E.g., output_partition_sizes for QKVLinear
+                is a list contains the width of Wq, Wk, Wv on rank X.
+            input_size: Size of the input dim of the weight across all ranks.
+            output_size: Size of the output dim of the weight across all ranks.
+            params_dtype: Datatype of the parameters.
+        """
         raise NotImplementedError
 
     @abstractmethod

vllm/model_executor/layers/quantization/fp8.py

@@ -64,12 +64,13 @@ def create_weights(
         self,
         layer: torch.nn.Module,
         input_size_per_partition: int,
-        output_size_per_partition: int,
+        output_partition_sizes: List[int],
         input_size: int,
         output_size: int,
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
+        output_size_per_partition = sum(output_partition_sizes)
         weight = Parameter(torch.empty(output_size_per_partition,
                                        input_size_per_partition,
                                        dtype=params_dtype),