add qwen3

vllm-v0.6.2/vllm/distributed/device_communicators/hpu_communicator.py

@@ -0,0 +1,48 @@
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+
+from vllm.platforms import current_platform
+
+if current_platform.is_hpu():
+    import habana_frameworks.torch as htorch  # noqa: F401
+
+
+class HpuCommunicator:
+
+    def __init__(self, group: ProcessGroup):
+        if not current_platform.is_hpu():
+            self.disabled = True
+            return
+        self.disabled = False
+        self.group = group
+        self.world_size = dist.get_world_size(self.group)
+
+    def all_reduce(self, x: torch.Tensor) -> torch.Tensor:
+        # FIXME(kzawora): this is a workaround for a bug in Habana PT bridge
+        # occurring when PT_HPU_ENABLE_LAZY_COLLECTIVES=true env var is used
+        # (which is required for tensor parallel HPUGraph inference)
+        htorch.core.mark_step()
+        dist.all_reduce(x, group=self.group)
+        return x
+
+    def all_gather(self, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
+        world_size = self.world_size
+        if dim < 0:
+            # Convert negative dim to positive.
+            dim += x.dim()
+        input_size = x.size()
+        # Allocate output tensor.
+        output_tensor = torch.empty((world_size, ) + input_size,
+                                    dtype=x.dtype,
+                                    device=x.device)
+        # All-gather.
+        htorch.core.mark_step()
+        dist.all_gather_into_tensor(output_tensor, x, group=self.group)
+        # Reshape
+        output_tensor = output_tensor.movedim(0, dim)
+        output_tensor = output_tensor.reshape(input_size[:dim] +
+                                              (world_size *
+                                               input_size[dim], ) +
+                                              input_size[dim + 1:])
+        return output_tensor