feat: use D2D instead of H2H in pp (#7673)

Co-authored-by: alpha-baby <fujianhao1997@qq.com>

python/sglang/srt/distributed/parallel_state.py

@@ -699,18 +699,25 @@ class GroupCoordinator:
         )
 
         # Serialize object to tensor and get the size as well
-        object_tensor = torch.frombuffer(pickle.dumps(obj), dtype=torch.uint8)
+        object_tensor = torch.frombuffer(pickle.dumps(obj), dtype=torch.uint8).cuda(
+            device=torch.cuda.current_device()
+        )
 
         size_tensor = torch.tensor(
-            [object_tensor.numel()], dtype=torch.long, device="cpu"
+            [object_tensor.numel()],
+            dtype=torch.long,
+            device=torch.cuda.current_device(),
         )
 
         # Send object size
-
+        torch.distributed.send(
-        torch.distributed.send(size_tensor, dst=self.ranks[dst], group=self.cpu_group)
+            size_tensor, dst=self.ranks[dst], group=self.device_group
+        )
 
         # Send object
-        torch.distributed.send(object_tensor, dst=self.ranks[dst], group=self.cpu_group)
+        torch.distributed.send(
+            object_tensor, dst=self.ranks[dst], group=self.device_group
+        )
 
         return None
 
@@ -724,29 +731,31 @@ class GroupCoordinator:
             src != self.rank_in_group
         ), "Invalid source rank. Source rank is the same as the current rank."
 
-        size_tensor = torch.empty(1, dtype=torch.long, device="cpu")
+        size_tensor = torch.empty(
+            1, dtype=torch.long, device=torch.cuda.current_device()
+        )
 
         # Receive object size
         rank_size = torch.distributed.recv(
-            size_tensor, src=self.ranks[src], group=self.cpu_group
+            size_tensor, src=self.ranks[src], group=self.device_group
         )
 
         # Tensor to receive serialized objects into.
         object_tensor = torch.empty(  # type: ignore[call-overload]
             size_tensor.item(),  # type: ignore[arg-type]
             dtype=torch.uint8,
-            device="cpu",
+            device=torch.cuda.current_device(),
         )
 
         rank_object = torch.distributed.recv(
-            object_tensor, src=self.ranks[src], group=self.cpu_group
+            object_tensor, src=self.ranks[src], group=self.device_group
         )
 
         assert (
             rank_object == rank_size
         ), "Received object sender rank does not match the size sender rank."
 
-        obj = pickle.loads(object_tensor.numpy().tobytes())
+        obj = pickle.loads(object_tensor.cpu().numpy().tobytes())
 
         return obj
 
@@ -857,14 +866,16 @@ class GroupCoordinator:
             dst = (self.rank_in_group + 1) % self.world_size
         assert dst < self.world_size, f"Invalid dst rank ({dst})"
 
-        metadata_list: List[Tuple[Any, Any]] = []
         assert isinstance(
             tensor_dict, dict
         ), f"Expecting a dictionary, got {type(tensor_dict)}"
         metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
-        # `metadata_list` lives in CPU memory.
+        # Note: While switching to Device-to-Device (D2D) would introduce an extra
-        # `send_object_list` has serialization & deserialization,
+        # Device-to-Host (D2H) memory copy overhead for serialization, our benchmarks
-        # all happening on CPU. Therefore, we can use the CPU group.
+        # show better overall transmission performance with D2D due to:
+        # 1. Superior D2D transfer bandwidth
+        # 2. Ability to overlap send and recv operations
+        # Thus the net performance gain justifies this approach.
         self.send_object(metadata_list, dst=dst)
         for tensor in tensor_list:
             if tensor.numel() == 0:

python/sglang/srt/managers/scheduler.py

@@ -928,7 +928,7 @@ class Scheduler(
                         point_to_point_pyobj(
                             recv_reqs,
                             self.pp_rank * self.tp_size + dp_offset,
-                            self.world_group.cpu_group,
+                            self.world_group.device_group,
                             self.pp_rank * self.tp_size + dp_offset,
                             (self.pp_rank + 1) * self.tp_size + dp_offset,
                         )
@@ -975,7 +975,7 @@ class Scheduler(
                 recv_reqs = point_to_point_pyobj(
                     [],
                     self.pp_rank * self.tp_size + dp_offset,
-                    self.world_group.cpu_group,
+                    self.world_group.device_group,
                     (self.pp_rank - 1) * self.tp_size + dp_offset,
                     self.pp_rank * self.tp_size + dp_offset,
                 )

python/sglang/srt/utils.py

@@ -1000,36 +1000,48 @@ def point_to_point_pyobj(
     src: int = 0,
     dst: int = 1,
 ):
-    """Send data from src to dst in group."""
+    """Send data from src to dst in group using DeviceToDevice communication."""
 
     if rank == src:
         if len(data) == 0:
-            tensor_size = torch.tensor([0], dtype=torch.long)
+            tensor_size = torch.tensor(
+                [0], dtype=torch.long, device=torch.cuda.current_device()
+            )
             dist.send(tensor_size, dst=dst, group=group)
         else:
             serialized_data = pickle.dumps(data)
             size = len(serialized_data)
             tensor_data = torch.ByteTensor(
                 np.frombuffer(serialized_data, dtype=np.uint8)
+            ).cuda(
+                device=torch.cuda.current_device()
+            )  # Move to GPU
+            tensor_size = torch.tensor(
+                [size], dtype=torch.long, device=torch.cuda.current_device()
             )
-            tensor_size = torch.tensor([size], dtype=torch.long)
 
             dist.send(tensor_size, dst=dst, group=group)
             dist.send(tensor_data, dst=dst, group=group)
         return data
 
     elif rank == dst:
-        tensor_size = torch.tensor([0], dtype=torch.long)
+        tensor_size = torch.tensor(
+            [0], dtype=torch.long, device=torch.cuda.current_device()
+        )
         dist.recv(tensor_size, src=src, group=group)
         size = tensor_size.item()
 
         if size == 0:
             return []
 
-        tensor_data = torch.empty(size, dtype=torch.uint8)
+        tensor_data = torch.empty(
+            size, dtype=torch.uint8, device=torch.cuda.current_device()
+        )
         dist.recv(tensor_data, src=src, group=group)
 
-        serialized_data = bytes(tensor_data.cpu().numpy())
+        serialized_data = bytes(
+            tensor_data.cpu().numpy()
+        )  # Move back to host for deserialization
         data = pickle.loads(serialized_data)
         return data