v1.0

distributed/device_communicators/xpu_communicator.py

@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+
+from vllm.logger import init_logger
+
+from .base_device_communicator import DeviceCommunicatorBase
+
+logger = init_logger(__name__)
+
+
+class XpuCommunicator(DeviceCommunicatorBase):
+    def __init__(
+        self,
+        cpu_group: ProcessGroup,
+        device: torch.device | None = None,
+        device_group: ProcessGroup | None = None,
+        unique_name: str = "",
+    ):
+        super().__init__(cpu_group, device, device_group, unique_name)
+        if self.use_all2all:
+            if self.all2all_backend != "naive":
+                logger.warning(
+                    "`%s` all2all manager is not supported on XPU. "
+                    "Falling back to `naive` all2all manager for XPU.",
+                    self.all2all_backend,
+                )
+                self.all2all_backend = "naive"
+            if self.all2all_backend == "naive":
+                from .all2all import NaiveAll2AllManager
+
+                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
+                logger.info("Using naive all2all manager.")
+
+    def all_reduce(self, input_) -> torch.Tensor:
+        dist.all_reduce(input_, group=self.device_group)
+        return input_
+
+    def gather(
+        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
+    ) -> torch.Tensor | None:
+        assert -input_.dim() <= dim < input_.dim(), (
+            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
+        )
+        if dim < 0:
+            # Convert negative dim to positive.
+            dim += input_.dim()
+        # For xpu path, gather doesn't work properly together with ray
+        # cluster so we use all_gather instead for now.
+        input_size = input_.size()
+        # Allocate output tensor.
+        output_tensor = torch.empty(
+            (self.world_size,) + input_size, dtype=input_.dtype, device=input_.device
+        )
+        # All-gather.
+        dist.all_gather_into_tensor(output_tensor, input_, group=self.device_group)
+        if self.rank_in_group == dst:
+            # Reshape
+            output_tensor = output_tensor.movedim(0, dim)
+            output_tensor = output_tensor.reshape(
+                input_size[:dim]
+                + (self.world_size * input_size[dim],)
+                + input_size[dim + 1 :]
+            )
+        else:
+            output_tensor = None
+        return output_tensor
+
+    def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
+        dist.broadcast(input_, src=src, group=self.device_group)
+
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        is_sequence_parallel: bool = False,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self.all2all_manager is not None
+        hidden_states, router_logits = self.all2all_manager.dispatch(
+            hidden_states, router_logits, is_sequence_parallel
+        )
+        return hidden_states, router_logits
+
+    def combine(
+        self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
+    ) -> torch.Tensor:
+        assert self.all2all_manager is not None
+        hidden_states = self.all2all_manager.combine(
+            hidden_states, is_sequence_parallel
+        )
+        return hidden_states