Sync from v0.13

vllm/distributed/device_communicators/symm_mem.py

@@ -0,0 +1,156 @@
+# 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.distributed.device_communicators.all_reduce_utils import (
+    SYMM_MEM_ALL_REDUCE_MAX_SIZES,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.batch_invariant import (
+    vllm_is_batch_invariant,
+)
+from vllm.platforms import current_platform
+
+try:
+    import torch.distributed._symmetric_memory as torch_symm_mem
+
+    symm_mem_available = True
+except ImportError:
+    symm_mem_available = False
+
+logger = init_logger(__name__)
+
+
+class SymmMemCommunicator:
+    _WORLD_SIZES_MULTIMEM = {
+        "9.0": [4, 6, 8],
+        "10.0": [6, 8],
+    }
+
+    def __init__(
+        self,
+        group: ProcessGroup,
+        device: int | str | torch.device,
+        # add options for testing
+        force_multimem: bool | None = None,
+        max_size_override: int | None = None,
+    ):
+        self.disabled = True
+
+        if not symm_mem_available:
+            return
+
+        if not current_platform.is_cuda():
+            logger.warning("SymmMemCommunicator: symmetric memory is not available.")
+            return
+        if isinstance(device, int):
+            device = torch.device(f"cuda:{device}")
+        elif isinstance(device, str):
+            device = torch.device(device)
+        torch.cuda.set_device(device)
+        self.dtype = torch.bfloat16
+        self.device = device
+        self.group = group
+        self.world_size = dist.get_world_size(self.group)
+        capability = current_platform.get_device_capability()
+        if capability is None:
+            logger.warning(
+                "SymmMemCommunicator: device capability is unknown, "
+                "communicator is not available."
+            )
+            return
+        self.device_capability = capability.as_version_str()
+        if self.device_capability not in SYMM_MEM_ALL_REDUCE_MAX_SIZES:
+            logger.warning(
+                "SymmMemCommunicator: Device capability %s not supported, "
+                "communicator is not available.",
+                self.device_capability,
+            )
+            return
+        if self.world_size not in SYMM_MEM_ALL_REDUCE_MAX_SIZES[self.device_capability]:
+            logger.warning(
+                "SymmMemCommunicator: World size %d not supported, "
+                "communicator is not available.",
+                self.world_size,
+            )
+            return
+        # Use override max_size if provided, otherwise use default
+        if max_size_override is not None:
+            self.max_size = max_size_override
+            logger.info(
+                "SymmMemCommunicator: Using override max_size: %s bytes",
+                self.max_size,
+            )
+        else:
+            self.max_size = SYMM_MEM_ALL_REDUCE_MAX_SIZES[self.device_capability][
+                self.world_size
+            ]
+        try:
+            self.buffer = torch_symm_mem.empty(
+                self.max_size // self.dtype.itemsize,
+                device=self.device,
+                dtype=self.dtype,
+            )
+            handle = torch_symm_mem.rendezvous(self.buffer, self.group.group_name)
+        except RuntimeError as e:
+            logger.warning_once(
+                "SymmMemCommunicator: symmetric memory initialization failed: %s "
+                "Communicator is not available. To suppress this warning set "
+                "VLLM_ALLREDUCE_USE_SYMM_MEM=0",
+                str(e),
+            )
+            return
+        if handle.multicast_ptr == 0:
+            logger.warning(
+                "SymmMemCommunicator: symmetric memory "
+                "multicast operations are not supported."
+            )
+            return
+        self.force_multimem = force_multimem
+        self.disabled = False
+        if vllm_is_batch_invariant():
+            self.disabled = True
+
+    def should_use_symm_mem(self, inp: torch.Tensor):
+        if self.disabled:
+            return False
+        if inp.dtype != self.dtype:
+            return False
+        inp_size = inp.numel() * inp.element_size()
+        if inp_size % 4 != 0:
+            return False
+        return inp_size < self.max_size
+
+    def all_reduce(
+        self, inp: torch.Tensor, *, out: torch.Tensor | None = None
+    ) -> torch.Tensor | None:
+        if not self.should_use_symm_mem(inp):
+            return None
+        if out is None:
+            out = torch.empty_like(inp)
+        self.buffer[: inp.numel()].copy_(inp.view(-1))
+
+        # Determine which algorithm to use
+        use_multimem = False
+        if self.force_multimem is not None:
+            # Test override: use forced setting
+            use_multimem = self.force_multimem
+        else:
+            # Normal logic: use multimem for supported world sizes
+            use_multimem = (
+                self.world_size in self._WORLD_SIZES_MULTIMEM[self.device_capability]
+            )
+
+        if use_multimem:
+            torch.ops.symm_mem.multimem_all_reduce_(
+                self.buffer[: inp.numel()], "sum", self.group.group_name
+            )
+        else:
+            torch.ops.symm_mem.two_shot_all_reduce_(
+                self.buffer[: inp.numel()], "sum", self.group.group_name
+            )
+        out.copy_(self.buffer[: inp.numel()].view(out.shape))
+        return out