[Model] RowParallelLinear: pass bias to quant_method.apply

tests/spec_decode/e2e/test_integration_dist_tp2.py

@@ -83,6 +83,9 @@ def test_target_model_tp_gt_1(baseline_llm_generator, test_llm_generator,
         # cleaned up properly, and its server host thread leaks, causing the
         # second run of the test to fail with internal NCCL error.
         "use_async": True,
+
+        # precision
+        "dtype": "float32",
     }])
 @pytest.mark.parametrize("baseline_llm_kwargs", [{}])
 @pytest.mark.parametrize(

vllm/model_executor/layers/linear.py

@@ -687,6 +687,7 @@ def __init__(self,
         self.reduce_results = reduce_results
 
         # Divide the weight matrix along the last dimension.
+        self.tp_rank = get_tensor_model_parallel_rank()
         self.tp_size = get_tensor_model_parallel_world_size()
         self.input_size_per_partition = divide(input_size, self.tp_size)
         assert self.quant_method is not None
@@ -741,18 +742,19 @@ def forward(self, input_):
 
         # Matrix multiply.
         assert self.quant_method is not None
-        output_parallel = self.quant_method.apply(self, input_parallel)
+        # Only fuse bias add into GEMM for rank 0 (this ensures that
+        # bias will not get added more than once in TP>1 case)
+        bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
+        output_parallel = self.quant_method.apply(self,
+                                                  input_parallel,
+                                                  bias=bias_)
         if self.reduce_results and self.tp_size > 1:
-            output_ = tensor_model_parallel_all_reduce(output_parallel)
+            output = tensor_model_parallel_all_reduce(output_parallel)
         else:
-            output_ = output_parallel
+            output = output_parallel
+
+        output_bias = self.bias if self.skip_bias_add else None
 
-        if not self.skip_bias_add:
-            output = output_ + self.bias if self.bias is not None else output_
-            output_bias = None
-        else:
-            output = output_
-            output_bias = self.bias
         return output, output_bias
 
     def extra_repr(self) -> str: