feat(server): update vllm version

router/src/main.rs

@@ -233,6 +233,10 @@ fn main() -> Result<(), RouterError> {
                             "Inferred max batch total tokens: {max_supported_batch_total_tokens}"
                         );
                     }
+                    if max_total_tokens as u32 > max_supported_batch_total_tokens {
+                        return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_supported_batch_total_tokens}")));
+                    }
+
                     max_supported_batch_total_tokens
                 }
             };
@@ -270,7 +274,7 @@ fn main() -> Result<(), RouterError> {
                 ngrok_authtoken,
                 ngrok_edge,
             )
-            .await?;
+                .await?;
             Ok(())
         })
 }

server/Makefile-vllm

@@ -1,4 +1,4 @@
-vllm_commit := d284b831c17f42a8ea63369a06138325f73c4cf9
+vllm_commit := 084ca75d4271f8f67be731bc58e0d41d8e0afd3a
 
 vllm:
     # Clone vllm

server/text_generation_server/utils/layers.py

@@ -219,36 +219,31 @@ def load(config, prefix: str, weights):
         )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
-        if not self.should_gather:
-            return super().forward(input)
-
         world_size = self.process_group.size()
-        if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
+        # Fast branch for single requests
+        if (
+            self.should_gather
+            and len(input.shape) == 2
+            and isinstance(self.linear, FastLinear)
+            and input.shape[0] == 1
+        ):
             out_dim = self.linear.weight.shape[0]
 
-            if input.shape[0] == 1:
-                world_out = input.new_empty(1, out_dim * world_size)
-                local_out = input.new_empty(1, out_dim)
-                gather_input = local_out
-            else:
-                world_out = input.new_empty(out_dim * world_size, input.shape[0])
-                gather_input = input.new_empty(out_dim, input.shape[0])
-                local_out = gather_input.T
+            world_out = input.new_empty(1, out_dim * world_size)
+            local_out = input.new_empty(1, out_dim)
 
             torch.mm(input, self.linear.weight.T, out=local_out)
 
             torch.distributed.all_gather_into_tensor(
-                world_out, gather_input, group=self.process_group
+                world_out, local_out, group=self.process_group
             )
-
-            if input.shape[0] == 1:
-                return world_out
-            return world_out.T
+            return world_out
 
         output = super().forward(input)
-        world_output = [
-            torch.empty_like(output) for _ in range(self.process_group.size())
-        ]
+        if not self.should_gather:
+            return output
+
+        world_output = [torch.empty_like(output) for _ in range(world_size)]
         torch.distributed.all_gather(world_output, output, group=self.process_group)
         world_output = torch.cat(world_output, dim=-1)
         return world_output