Enable custom AR for AMD GPUs and maintain it in sgl-kernel (#3406)

python/sglang/srt/_custom_ops.py

@@ -9,13 +9,13 @@ from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 import torch
 import torch.library
 
-from sglang.srt.utils import is_hpu
+from sglang.srt.utils import is_hip, is_hpu
 
 logger = logging.getLogger(__name__)
 use_vllm_custom_allreduce = os.environ.get("USE_VLLM_CUSTOM_ALLREDUCE", default=True)
-
 if not is_hpu():
-    if use_vllm_custom_allreduce:
+    # Remove vllm dependency for custom allreduce on ROCm
+    if use_vllm_custom_allreduce and not is_hip():
         try:
             import vllm._C
         except ImportError as e:
@@ -56,7 +56,7 @@ def hint_on_error(fn):
     return wrapper
 
 
-if use_vllm_custom_allreduce:
+if use_vllm_custom_allreduce and not is_hip():
     # custom ar
     def init_custom_ar(
         ipc_tensors: List[torch.Tensor],
@@ -95,39 +95,87 @@ if use_vllm_custom_allreduce:
         torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)
 
 else:
-    # custom ar
-    def init_custom_ar(
-        rank_id: int,
-        world_size: int,
-        rank_data_base: torch.Tensor,
-        buffers: List[int],
-        tmp_result_buffers: List[int],
-        barrier_in: List[int],
-        barrier_out: List[int],
-    ) -> int:
-        return sgl_kernel.ops.init_custom_reduce(
-            rank_id,
-            world_size,
-            rank_data_base,
-            buffers,
-            tmp_result_buffers,
-            barrier_in,
-            barrier_out,
-        )
+    if is_hip():
+
+        def init_custom_ar(
+            meta: torch.Tensor,
+            rank_data: torch.Tensor,
+            handles: List[str],
+            offsets: List[int],
+            rank: int,
+            full_nvlink: bool,
+        ) -> int:
+            return sgl_kernel.ops.init_custom_ar(
+                meta, rank_data, handles, offsets, rank, full_nvlink
+            )
 
-    def all_reduce(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
-        sgl_kernel.ops.custom_reduce(fa, inp, out)
+        def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
+            sgl_kernel.ops.all_reduce_reg(fa, inp, out)
 
-    def dispose(fa: int) -> None:
-        sgl_kernel.ops.custom_dispose(fa)
+        def all_reduce_unreg(
+            fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor, out: torch.Tensor
+        ) -> None:
+            sgl_kernel.ops.all_reduce_unreg(fa, inp, reg_buffer, out)
 
-    def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[int], List[int]]:
-        return sgl_kernel.ops.get_graph_buffer_ipc_meta(fa)
+        def dispose(fa: int) -> None:
+            sgl_kernel.ops.dispose(fa)
 
-    def register_graph_buffers(
-        fa: int, handles: List[List[int]], offsets: List[List[int]]
-    ) -> None:
-        sgl_kernel.ops.register_graph_buffers(fa, handles, offsets)
+        def meta_size() -> int:
+            return sgl_kernel.ops.meta_size()
+
+        def register_buffer(
+            fa: int, t: torch.Tensor, handles: List[str], offsets: List[int]
+        ) -> None:
+            return sgl_kernel.ops.register_buffer(fa, t, handles, offsets)
+
+        def get_graph_buffer_ipc_meta(fa: int) -> Tuple[torch.Tensor, List[int]]:
+            return sgl_kernel.ops.get_graph_buffer_ipc_meta(fa)
+
+        def register_graph_buffers(
+            fa: int, handles: List[str], offsets: List[List[int]]
+        ) -> None:
+            sgl_kernel.ops.register_graph_buffers(fa, handles, offsets)
+
+        def allocate_meta_buffer(size: int) -> torch.Tensor:
+            return sgl_kernel.ops.allocate_meta_buffer(size)
+
+        def get_meta_buffer_ipc_handle(inp: torch.Tensor) -> torch.Tensor:
+            return sgl_kernel.ops.get_meta_buffer_ipc_handle(inp)
+
+    else:
+        # custom ar
+        def init_custom_ar(
+            rank_id: int,
+            world_size: int,
+            rank_data_base: torch.Tensor,
+            buffers: List[int],
+            tmp_result_buffers: List[int],
+            barrier_in: List[int],
+            barrier_out: List[int],
+        ) -> int:
+            return sgl_kernel.ops.init_custom_reduce(
+                rank_id,
+                world_size,
+                rank_data_base,
+                buffers,
+                tmp_result_buffers,
+                barrier_in,
+                barrier_out,
+            )
+
+        def all_reduce(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
+            sgl_kernel.ops.custom_reduce(fa, inp, out)
+
+        def dispose(fa: int) -> None:
+            sgl_kernel.ops.custom_dispose(fa)
+
+        def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[int], List[int]]:
+            return sgl_kernel.ops.get_graph_buffer_ipc_meta(fa)
+
+        def register_graph_buffers(
+            fa: int, handles: List[List[int]], offsets: List[List[int]]
+        ) -> None:
+            sgl_kernel.ops.register_graph_buffers(fa, handles, offsets)
 
 
 # temporary fix for https://github.com/vllm-project/vllm/issues/5456

python/sglang/srt/distributed/device_communicators/custom_all_reduce.py

@@ -18,7 +18,7 @@ from sglang.srt.distributed.device_communicators.custom_all_reduce_utils import
     gpu_p2p_access_check,
 )
 from sglang.srt.distributed.parallel_state import in_the_same_node_as
-from sglang.srt.utils import cuda_device_count_stateless, is_cuda
+from sglang.srt.utils import cuda_device_count_stateless, is_cuda, is_hip
 
 logger = logging.getLogger(__name__)
 
@@ -28,14 +28,27 @@ if is_cuda():
     except ImportError as e:
         logger.warning("Failed to import pynvml with %r", e)
 
+if is_hip():
+    try:
+        from amdsmi import (
+            AmdSmiException,
+            amdsmi_get_gpu_board_info,
+            amdsmi_get_processor_handles,
+            amdsmi_init,
+            amdsmi_shut_down,
+            amdsmi_topo_get_link_type,
+        )
+    except ImportError as e:
+        logger.warning("Failed to import amdsmi with %r", e)
+
 try:
-    if ops.use_vllm_custom_allreduce:
+    if ops.use_vllm_custom_allreduce and not is_hip():
         ops.meta_size()
     else:
         import sgl_kernel
     custom_ar = True
 except Exception:
-    # For AMD GPUs and CPUs
+    # For CPUs
     custom_ar = False
 
 logger = logging.getLogger(__name__)
@@ -47,37 +60,62 @@ _R = TypeVar("_R")
 def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
     @wraps(fn)
     def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
-        pynvml.nvmlInit()
-        try:
-            return fn(*args, **kwargs)
-        finally:
-            pynvml.nvmlShutdown()
+        if torch.version.hip:
+            try:
+                amdsmi_init()
+                return fn(*args, **kwargs)
+            finally:
+                amdsmi_shut_down()
+        else:
+            pynvml.nvmlInit()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                pynvml.nvmlShutdown()
 
     return wrapper
 
 
 @with_nvml_context
-def is_full_nvlink(physical_device_ids: List[int]) -> bool:
-    """
-    query if the set of gpus are fully connected by nvlink (1 hop)
-    """
-    handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in physical_device_ids]
-    for i, handle in enumerate(handles):
-        for j, peer_handle in enumerate(handles):
-            if i < j:
-                try:
-                    p2p_status = pynvml.nvmlDeviceGetP2PStatus(
-                        handle, peer_handle, pynvml.NVML_P2P_CAPS_INDEX_NVLINK
-                    )
-                    if p2p_status != pynvml.NVML_P2P_STATUS_OK:
+def is_full_nvlink(physical_device_ids: List[int], world_size: int) -> bool:
+    if is_hip():
+        """
+        query if the set of gpus are fully connected by xgmi (1 hop)
+        """
+        handles = [amdsmi_get_processor_handles()[i] for i in physical_device_ids]
+        for i, handle in enumerate(handles):
+            for j, peer_handle in enumerate(handles):
+                if i < j:
+                    try:
+                        link_type = amdsmi_topo_get_link_type(handle, peer_handle)
+                        # type is 2 for XGMI
+                        if link_type["hops"] != 1 or link_type["type"] != 2:
+                            return False
+                    except AmdSmiException as error:
+                        logger.error("AMD 1 hop XGMI detection failed.", exc_info=error)
                         return False
-                except pynvml.NVMLError:
-                    logger.exception(
-                        "NVLink detection failed. This is normal if your"
-                        " machine has no NVLink equipped."
-                    )
-                    return False
-    return True
+        return True
+    else:
+        """
+        query if the set of gpus are fully connected by nvlink (1 hop)
+        """
+        handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in physical_device_ids]
+        for i, handle in enumerate(handles):
+            for j, peer_handle in enumerate(handles):
+                if i < j:
+                    try:
+                        p2p_status = pynvml.nvmlDeviceGetP2PStatus(
+                            handle, peer_handle, pynvml.NVML_P2P_CAPS_INDEX_NVLINK
+                        )
+                        if p2p_status != pynvml.NVML_P2P_STATUS_OK:
+                            return False
+                    except pynvml.NVMLError:
+                        logger.exception(
+                            "NVLink detection failed. This is normal if your"
+                            " machine has no NVLink equipped."
+                        )
+                        return False
+        return True
 
 
 def _can_p2p(rank: int, world_size: int) -> bool:
@@ -102,15 +140,18 @@ def is_weak_contiguous(inp: torch.Tensor):
 
 
 class CustomAllreduce:
-
     _SUPPORTED_WORLD_SIZES = [2, 4, 6, 8]
+    _MAX_CAR_SIZE = 8192 * 1024
+    if is_hip():
+        # crossover is at 16MB buffer size for ROCm
+        _MAX_CAR_SIZE = 2 * 8192 * 1024
 
     # max_size: max supported allreduce size
     def __init__(
         self,
         group: ProcessGroup,
         device: Union[int, str, torch.device],
-        max_size=8192 * 1024,
+        max_size=_MAX_CAR_SIZE,
     ) -> None:
         """
         Args:
@@ -185,12 +226,9 @@ class CustomAllreduce:
         # test nvlink first, this will filter out most of the cases
         # where custom allreduce is not supported
         # this checks hardware and driver support for NVLink
-        if is_cuda():
-            assert is_cuda()
+        if is_cuda() or is_hip():
+            full_nvlink = is_full_nvlink(physical_device_ids, world_size)
 
-            full_nvlink = is_full_nvlink(physical_device_ids)
-        else:
-            full_nvlink = False
         if world_size > 2 and not full_nvlink:
             logger.warning(
                 "Custom allreduce is disabled because it's not supported on"
@@ -201,7 +239,8 @@ class CustomAllreduce:
         # test P2P capability, this checks software/cudaruntime support
         # this is expensive to compute at the first time
         # then we cache the result
-        if not _can_p2p(rank, world_size):
+        # On AMD GPU, p2p is always enabled between XGMI connected GPUs
+        if not is_hip() and not _can_p2p(rank, world_size):
             logger.warning(
                 "Custom allreduce is disabled because your platform lacks "
                 "GPU P2P capability or P2P test failed. To silence this "
@@ -214,7 +253,7 @@ class CustomAllreduce:
         self.world_size = world_size
         self.full_nvlink = full_nvlink
 
-        if ops.use_vllm_custom_allreduce:
+        if ops.use_vllm_custom_allreduce and not is_hip():
             # Buffers memory are owned by this Python class and passed to C++.
             # Meta data composes of two parts: meta data for synchronization and a
             # temporary buffer for storing intermediate allreduce results.
@@ -237,35 +276,56 @@ class CustomAllreduce:
             )
             ops.register_buffer(self._ptr, self.buffer_ptrs)
         else:
-            # From TensorRT-LLM getMaxRequiredWorkspaceSize
-            self.max_required_workspace_size = [16 * 1024 * 1024, 8 * 1024 * 1024]
+            if is_hip():
+                # meta data buffers need to be "uncached" for signal on MI200
+                self.meta = ops.allocate_meta_buffer(ops.meta_size() + max_size)
+                self.buffer = torch.empty(
+                    max_size, dtype=torch.uint8, device=self.device
+                )
+                handle = ops.get_meta_buffer_ipc_handle(self.meta)
+                shard_data = (
+                    bytes(handle),  # ipc handle to base ptr
+                    0,  # offset of base ptr
+                )
+                handles, offsets = self._gather_ipc_meta(shard_data)
+                self.rank_data = torch.empty(
+                    8 * 1024 * 1024, dtype=torch.uint8, device=self.device
+                )
+                self._ptr = ops.init_custom_ar(
+                    self.meta, self.rank_data, handles, offsets, rank, self.full_nvlink
+                )
+                self.register_buffer(self.buffer)
+                self.MSCCL = os.getenv("RCCL_MSCCL_ENABLE", "1") == "1"
+            else:
+                # From TensorRT-LLM getMaxRequiredWorkspaceSize
+                self.max_required_workspace_size = [16 * 1024 * 1024, 8 * 1024 * 1024]
 
-            # sizeof(uint32_t) * (MAX_ALL_REDUCE_BLOCKS + 2) * MAX_RANKS_PER_NODE;
-            self.barrier_max_size = 8 * (36 + 2) * 8
+                # sizeof(uint32_t) * (MAX_ALL_REDUCE_BLOCKS + 2) * MAX_RANKS_PER_NODE;
+                self.barrier_max_size = 8 * (36 + 2) * 8
 
-            self.buffer_ptrs = self.create_shared_buffer(max_size, group=group)
-            self.tmp_result_buffer_ptrs = self.create_shared_buffer(
-                max_size, group=group
-            )
-            self.rank_data_base = torch.empty(
-                8 * 1024 * 1024, dtype=torch.uint8, device=self.device
-            )
-            self.barrier_in_ptrs = self.create_shared_buffer(
-                self.barrier_max_size, group=group
-            )
-            self.barrier_out_ptrs = self.create_shared_buffer(
-                self.barrier_max_size, group=group
-            )
+                self.buffer_ptrs = self.create_shared_buffer(max_size, group=group)
+                self.tmp_result_buffer_ptrs = self.create_shared_buffer(
+                    max_size, group=group
+                )
+                self.rank_data_base = torch.empty(
+                    8 * 1024 * 1024, dtype=torch.uint8, device=self.device
+                )
+                self.barrier_in_ptrs = self.create_shared_buffer(
+                    self.barrier_max_size, group=group
+                )
+                self.barrier_out_ptrs = self.create_shared_buffer(
+                    self.barrier_max_size, group=group
+                )
 
-            self._ptr = ops.init_custom_ar(
-                rank,
-                world_size,
-                self.rank_data_base,
-                self.buffer_ptrs,
-                self.tmp_result_buffer_ptrs,
-                self.barrier_in_ptrs,
-                self.barrier_out_ptrs,
-            )
+                self._ptr = ops.init_custom_ar(
+                    rank,
+                    world_size,
+                    self.rank_data_base,
+                    self.buffer_ptrs,
+                    self.tmp_result_buffer_ptrs,
+                    self.barrier_in_ptrs,
+                    self.barrier_out_ptrs,
+                )
         self.disabled = False
 
     @staticmethod
@@ -316,23 +376,69 @@ class CustomAllreduce:
             if not self.disabled:
                 self.register_graph_buffers()
 
-    def register_graph_buffers(self):
-        handle, offset = ops.get_graph_buffer_ipc_meta(self._ptr)
-        logger.info("Registering %d cuda graph addresses", len(offset))
-        # We cannot directly use `dist.all_gather_object` here
-        # because it is incompatible with `gloo` backend under inference mode.
-        # see https://github.com/pytorch/pytorch/issues/126032 for details.
-        all_data = [[None, None] for _ in range(dist.get_world_size(group=self.group))]
-        all_data[self.rank] = [handle, offset]
-        ranks = sorted(dist.get_process_group_ranks(group=self.group))
+    def _get_ipc_meta(self, inp: torch.Tensor):
+        # _share_cuda_() doesn't accept meta buffer not allocated from
+        # PyTorch cache allocator, use direct HIP call to get IPC handle
+        handle = ops.get_meta_buffer_ipc_handle(inp)
+        shard_data = (
+            bytes(handle),  # ipc handle to base ptr
+            0,  # offset of base ptr
+        )
+        return self._gather_ipc_meta(shard_data)
+
+    def _gather_ipc_meta(self, shard_data):
+        # Note: don't use `[[None]] * self.world_size` here
+        # because it will create a list of the same reference
+        all_data: List[Optional[Any]] = [[None] for i in range(self.world_size)]
+        all_data[self.rank][0] = shard_data
+
+        ranks = dist.get_process_group_ranks(group=self.group)
+        ranks.sort()
         for i, rank in enumerate(ranks):
             dist.broadcast_object_list(
                 all_data[i], src=rank, group=self.group, device="cpu"
             )
-        # Unpack list of tuples to tuple of lists.
-        handles = [d[0] for d in all_data]  # type: ignore
-        offsets = [d[1] for d in all_data]  # type: ignore
-        ops.register_graph_buffers(self._ptr, handles, offsets)
+
+        # we cannot directly use `dist.all_gather_object` here
+        # because it is incompatible with `gloo` backend under inference mode.
+        # see https://github.com/pytorch/pytorch/issues/126032 for details.
+
+        handles = []
+        offsets = []
+        for i in range(len(all_data)):
+            handles.append(all_data[i][0][0])  # type: ignore
+            offsets.append(all_data[i][0][1])  # type: ignore
+        return handles, offsets
+
+    def register_buffer(self, inp: torch.Tensor):
+        handles, offsets = self._get_ipc_meta(inp)
+        ops.register_buffer(self._ptr, inp, handles, offsets)
+
+    def register_graph_buffers(self):
+        if is_hip():
+            handle, offset = ops.get_graph_buffer_ipc_meta(self._ptr)
+            handles, offsets = self._gather_ipc_meta((bytes(handle), offset))
+            logger.info("Registering %d cuda graph addresses", len(offset))
+            ops.register_graph_buffers(self._ptr, handles, offsets)
+        else:
+            handle, offset = ops.get_graph_buffer_ipc_meta(self._ptr)
+            logger.info("Registering %d cuda graph addresses", len(offset))
+            # We cannot directly use `dist.all_gather_object` here
+            # because it is incompatible with `gloo` backend under inference mode.
+            # see https://github.com/pytorch/pytorch/issues/126032 for details.
+            all_data = [
+                [None, None] for _ in range(dist.get_world_size(group=self.group))
+            ]
+            all_data[self.rank] = [handle, offset]
+            ranks = sorted(dist.get_process_group_ranks(group=self.group))
+            for i, rank in enumerate(ranks):
+                dist.broadcast_object_list(
+                    all_data[i], src=rank, group=self.group, device="cpu"
+                )
+            # Unpack list of tuples to tuple of lists.
+            handles = [d[0] for d in all_data]  # type: ignore
+            offsets = [d[1] for d in all_data]  # type: ignore
+            ops.register_graph_buffers(self._ptr, handles, offsets)
 
     def should_custom_ar(self, inp: torch.Tensor):
         if self.disabled:
@@ -345,11 +451,22 @@ class CustomAllreduce:
             return False
         # for 4 or more non NVLink-capable GPUs, custom allreduce provides
         # little performance improvement over NCCL.
-        if ops.use_vllm_custom_allreduce:
+        if ops.use_vllm_custom_allreduce and not is_hip():
             if self.world_size == 2 or self.full_nvlink:
                 return inp_size < self.max_size
             return False
 
+        if is_hip():
+            if self.full_nvlink:
+                if self.world_size == 8:
+                    if self.MSCCL:
+                        return False
+                    else:
+                        return inp_size < self.max_size
+                else:
+                    return inp_size < self.max_size
+            return False
+
         if self.world_size == 2:
             return (
                 inp_size < self.max_size
@@ -364,6 +481,21 @@ class CustomAllreduce:
 
         return False
 
+    # all reduce, assuming inp tensor is IPC registered with register_buffer,
+    # or, in the context of cuda graphs, register_graph_buffers
+    def all_reduce_reg(self, inp: torch.Tensor, out: torch.Tensor = None):
+        if out is None:
+            out = torch.empty_like(inp)
+        ops.all_reduce_reg(self._ptr, inp, out)
+        return out
+
+    # all reduce, assuming inp tensor is NOT IPC registered
+    def all_reduce_unreg(self, inp: torch.Tensor, out: torch.Tensor = None):
+        if out is None:
+            out = torch.empty_like(inp)
+        ops.all_reduce_unreg(self._ptr, inp, self.buffer, out)
+        return out
+
     def all_reduce(
         self,
         inp: torch.Tensor,
@@ -397,13 +529,23 @@ class CustomAllreduce:
             return None
         if self._IS_CAPTURING:
             if torch.cuda.is_current_stream_capturing():
-                return self.all_reduce(input, registered=True)
+                if is_hip():
+                    return self.all_reduce_reg(input)
+                else:
+                    return self.all_reduce(input, registered=True)
             else:
                 # If warm up, mimic the allocation pattern since custom
                 # allreduce is out-of-place.
                 return torch.empty_like(input)
         else:
-            return self.all_reduce(input, registered=False)
+            if is_hip():
+                # note: outside of cuda graph context,
+                # custom allreduce incurs a cost of cudaMemcpy, which should
+                # be small(<=1% of overall latency) compared to the performance
+                # gains of using custom kernels
+                return self.all_reduce_unreg(input)
+            else:
+                return self.all_reduce(input, registered=False)
 
     def close(self):
         if not self.disabled and self._ptr:
@@ -411,7 +553,7 @@ class CustomAllreduce:
             if ops.use_vllm_custom_allreduce:
                 self.free_shared_buffer(self.meta_ptrs)
                 self.free_shared_buffer(self.buffer_ptrs)
-            else:
+            elif is_cuda():
                 self.free_shared_buffer(self.buffer_ptrs)
                 self.free_shared_buffer(self.tmp_result_buffer_ptrs)
                 self.free_shared_buffer(self.barrier_in_ptrs)

sgl-kernel/setup_rocm.py

@@ -44,6 +44,7 @@ include_dirs = [
 sources = [
     "src/sgl-kernel/torch_extension_rocm.cc",
     "src/sgl-kernel/csrc/moe_align_kernel.cu",
+    "src/sgl-kernel/csrc/custom_all_reduce.hip",
 ]
 
 cxx_flags = ["-O3"]

sgl-kernel/src/sgl-kernel/__init__.py

 import ctypes
 import os
 
+import torch
+
 if os.path.exists("/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12"):
     ctypes.CDLL(
         "/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12",
         mode=ctypes.RTLD_GLOBAL,
     )
+from .version import __version__
 
-from sgl_kernel.ops import (
-    apply_rope_with_cos_sin_cache_inplace,
-    bmm_fp8,
-    build_tree_kernel,
-    build_tree_kernel_efficient,
-    cublas_grouped_gemm,
-    custom_dispose,
-    custom_reduce,
-    fp8_blockwise_scaled_mm,
-    fp8_scaled_mm,
-    fused_add_rmsnorm,
-    gelu_and_mul,
-    gelu_tanh_and_mul,
-    gemma_fused_add_rmsnorm,
-    gemma_rmsnorm,
-    get_graph_buffer_ipc_meta,
-    init_custom_reduce,
-    int8_scaled_mm,
-    lightning_attention_decode,
-    min_p_sampling_from_probs,
-    moe_align_block_size,
-    register_graph_buffers,
-    rmsnorm,
-    sampling_scaling_penalties,
-    sgl_per_token_group_quant_fp8,
-    silu_and_mul,
-    top_k_renorm_prob,
-    top_k_top_p_sampling_from_probs,
-    top_p_renorm_prob,
-    tree_speculative_sampling_target_only,
-)
+if torch.version.hip is not None:
+    from sgl_kernel.ops import (
+        all_reduce_reg,
+        all_reduce_unreg,
+        allocate_meta_buffer,
+        apply_rope_with_cos_sin_cache_inplace,
+        bmm_fp8,
+        dispose,
+        fp8_scaled_mm,
+        fused_add_rmsnorm,
+        gelu_and_mul,
+        gelu_tanh_and_mul,
+        gemma_fused_add_rmsnorm,
+        gemma_rmsnorm,
+        get_graph_buffer_ipc_meta,
+        get_meta_buffer_ipc_handle,
+        init_custom_ar,
+        int8_scaled_mm,
+        lightning_attention_decode,
+        meta_size,
+        min_p_sampling_from_probs,
+        moe_align_block_size,
+        register_buffer,
+        register_graph_buffers,
+        rmsnorm,
+        sampling_scaling_penalties,
+        silu_and_mul,
+        top_k_renorm_prob,
+        top_k_top_p_sampling_from_probs,
+        top_p_renorm_prob,
+    )
 
-from .version import __version__
+    __all__ = [
+        "all_reduce_reg",
+        "all_reduce_unreg",
+        "allocate_meta_buffer",
+        "apply_rope_with_cos_sin_cache_inplace",
+        "bmm_fp8",
+        "dispose",
+        "fp8_scaled_mm",
+        "fused_add_rmsnorm",
+        "gelu_and_mul",
+        "gelu_tanh_and_mul",
+        "gemma_fused_add_rmsnorm",
+        "gemma_rmsnorm",
+        "get_graph_buffer_ipc_meta",
+        "get_meta_buffer_ipc_handle",
+        "init_custom_ar",
+        "int8_scaled_mm",
+        "lightning_attention_decode",
+        "meta_size",
+        "min_p_sampling_from_probs",
+        "moe_align_block_size",
+        "register_buffer",
+        "register_graph_buffers",
+        "rmsnorm",
+        "sampling_scaling_penalties",
+        "silu_and_mul",
+        "top_k_renorm_prob",
+        "top_k_top_p_sampling_from_probs",
+        "top_p_renorm_prob",
+    ]
+else:
+    from sgl_kernel.ops import (
+        apply_rope_with_cos_sin_cache_inplace,
+        bmm_fp8,
+        build_tree_kernel,
+        build_tree_kernel_efficient,
+        cublas_grouped_gemm,
+        custom_dispose,
+        custom_reduce,
+        fp8_blockwise_scaled_mm,
+        fp8_scaled_mm,
+        fused_add_rmsnorm,
+        gelu_and_mul,
+        gelu_tanh_and_mul,
+        gemma_fused_add_rmsnorm,
+        gemma_rmsnorm,
+        get_graph_buffer_ipc_meta,
+        init_custom_reduce,
+        int8_scaled_mm,
+        lightning_attention_decode,
+        min_p_sampling_from_probs,
+        moe_align_block_size,
+        register_graph_buffers,
+        rmsnorm,
+        sampling_scaling_penalties,
+        sgl_per_token_group_quant_fp8,
+        silu_and_mul,
+        top_k_renorm_prob,
+        top_k_top_p_sampling_from_probs,
+        top_p_renorm_prob,
+        tree_speculative_sampling_target_only,
+    )
 
-__all__ = [
-    "apply_rope_with_cos_sin_cache_inplace",
-    "bmm_fp8",
-    "cublas_grouped_gemm",
-    "custom_dispose",
-    "custom_reduce",
-    "fp8_blockwise_scaled_mm",
-    "fp8_scaled_mm",
-    "fused_add_rmsnorm",
-    "gelu_and_mul",
-    "gelu_tanh_and_mul",
-    "gemma_fused_add_rmsnorm",
-    "gemma_rmsnorm",
-    "get_graph_buffer_ipc_meta",
-    "init_custom_reduce",
-    "int8_scaled_mm",
-    "lightning_attention_decode",
-    "min_p_sampling_from_probs",
-    "moe_align_block_size",
-    "register_graph_buffers",
-    "rmsnorm",
-    "sampling_scaling_penalties",
-    "silu_and_mul",
-    "top_k_renorm_prob",
-    "top_k_top_p_sampling_from_probs",
-    "top_p_renorm_prob",
-    "tree_speculative_sampling_target_only",
-    "build_tree_kernel_efficient",
-    "build_tree_kernel",
-    "sgl_per_token_group_quant_fp8",
-]
+    __all__ = [
+        "apply_rope_with_cos_sin_cache_inplace",
+        "bmm_fp8",
+        "cublas_grouped_gemm",
+        "custom_dispose",
+        "custom_reduce",
+        "fp8_blockwise_scaled_mm",
+        "fp8_scaled_mm",
+        "fused_add_rmsnorm",
+        "gelu_and_mul",
+        "gelu_tanh_and_mul",
+        "gemma_fused_add_rmsnorm",
+        "gemma_rmsnorm",
+        "get_graph_buffer_ipc_meta",
+        "init_custom_reduce",
+        "int8_scaled_mm",
+        "lightning_attention_decode",
+        "min_p_sampling_from_probs",
+        "moe_align_block_size",
+        "register_graph_buffers",
+        "rmsnorm",
+        "sampling_scaling_penalties",
+        "silu_and_mul",
+        "top_k_renorm_prob",
+        "top_k_top_p_sampling_from_probs",
+        "top_p_renorm_prob",
+        "tree_speculative_sampling_target_only",
+        "build_tree_kernel_efficient",
+        "build_tree_kernel",
+        "sgl_per_token_group_quant_fp8",
+    ]

sgl-kernel/src/sgl-kernel/csrc/custom_all_reduce.hip

+// !!! This is a file automatically generated by hipify!!!
+#include <ATen/hip/Exceptions.h>
+#include <ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h>
+#include <ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h>
+#include <torch/all.h>
+
+#include "custom_all_reduce_hip.cuh"
+
+// fake pointer type, must match fptr_t type in ops.h
+using fptr_t = int64_t;
+static_assert(sizeof(void*) == sizeof(fptr_t));
+
+fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
+                      const std::vector<std::string>& handles,
+                      const std::vector<int64_t>& offsets, int64_t rank,
+                      bool full_nvlink) {
+  int world_size = offsets.size();
+  if (world_size > 8)
+    throw std::invalid_argument("world size > 8 is not supported");
+  if (world_size % 2 != 0)
+    throw std::invalid_argument("Odd num gpus is not supported for now");
+  if (world_size != handles.size())
+    throw std::invalid_argument(
+        "handles length should equal to offsets length");
+  if (rank < 0 || rank >= world_size)
+    throw std::invalid_argument("invalid rank passed in");
+
+  hipIpcMemHandle_t ipc_handles[8];
+  for (int i = 0; i < world_size; i++) {
+    std::memcpy(&ipc_handles[i], handles[i].data(), sizeof(hipIpcMemHandle_t));
+  }
+  return (fptr_t) new vllm::CustomAllreduce(
+      reinterpret_cast<vllm::Signal*>(meta.data_ptr()), rank_data.data_ptr(),
+      rank_data.numel(), ipc_handles, offsets, rank, full_nvlink);
+}
+
+/**
+ * Make sure tensor t's data lies completely within ((char)t.data_ptr()) +
+ * t.numel() * t.element_size(). This is slightly weaker than t.is_contiguous()
+ * because it allows transpose of contiguous slice (i.e. slicing the first
+ * dimension). Currently, we require this because stride information is not
+ * passed into the kernels and we treat input tensors as flat.
+ *
+ * Examples
+ * A = torch.zeros(3, 3, 3)
+ * 1. A: OK
+ * 2. A[1:]: OK
+ * 3. A.permute(2, 0, 1): OK
+ * 4. A[1:].permute(2, 0, 1): OK
+ * 5. A[None].expand(2, -1, -1, -1): Not OK
+ * 6. A[:, 1:, 1:]: Not OK
+ */
+bool _is_weak_contiguous(torch::Tensor& t) {
+  return t.is_contiguous() ||
+         (t.storage().nbytes() - t.storage_offset() * t.element_size() ==
+          t.numel() * t.element_size());
+}
+
+void _all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
+                 hipStream_t stream) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  TORCH_CHECK(_is_weak_contiguous(out));
+  switch (out.scalar_type()) {
+    case at::ScalarType::Float: {
+      fa->allreduce<float>(stream, reinterpret_cast<float*>(inp.data_ptr()),
+                           reinterpret_cast<float*>(out.data_ptr()),
+                           out.numel());
+      break;
+    }
+    case at::ScalarType::Half: {
+      fa->allreduce<half>(stream, reinterpret_cast<half*>(inp.data_ptr()),
+                          reinterpret_cast<half*>(out.data_ptr()), out.numel());
+      break;
+    }
+#if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
+    case at::ScalarType::BFloat16: {
+      fa->allreduce<nv_bfloat16>(
+          stream, reinterpret_cast<nv_bfloat16*>(inp.data_ptr()),
+          reinterpret_cast<nv_bfloat16*>(out.data_ptr()), out.numel());
+      break;
+    }
+#endif
+    default:
+      throw std::runtime_error(
+          "custom allreduce only supports float32, float16 and bfloat16");
+  }
+}
+
+void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out) {
+  const at::hip::OptionalHIPGuardMasqueradingAsCUDA device_guard(device_of(inp));
+  auto stream = c10::hip::getCurrentHIPStreamMasqueradingAsCUDA().stream();
+  TORCH_CHECK_EQ(inp.scalar_type(), out.scalar_type());
+  TORCH_CHECK_EQ(inp.numel(), out.numel());
+  _all_reduce(_fa, inp, out, stream);
+}
+
+void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer,
+                      torch::Tensor& out) {
+  const at::hip::OptionalHIPGuardMasqueradingAsCUDA device_guard(device_of(inp));
+  auto stream = c10::hip::getCurrentHIPStreamMasqueradingAsCUDA().stream();
+
+  auto input_size = inp.numel() * inp.element_size();
+  TORCH_CHECK_EQ(inp.scalar_type(), out.scalar_type());
+  TORCH_CHECK_EQ(inp.numel(), out.numel());
+  TORCH_CHECK(input_size <= reg_buffer.numel() * reg_buffer.element_size(),
+              "registered buffer is too small to contain the input");
+  AT_CUDA_CHECK(hipMemcpyAsync(reg_buffer.data_ptr(), inp.data_ptr(),
+                                input_size, hipMemcpyDeviceToDevice, stream));
+  _all_reduce(_fa, reg_buffer, out, stream);
+}
+
+void dispose(fptr_t _fa) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  delete fa;
+}
+
+int64_t meta_size() { return sizeof(vllm::Signal); }
+
+void register_buffer(fptr_t _fa, torch::Tensor& t,
+                     const std::vector<std::string>& handles,
+                     const std::vector<int64_t>& offsets) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  fa->register_buffer(handles, offsets, t.data_ptr());
+}
+
+std::tuple<torch::Tensor, std::vector<int64_t>> get_graph_buffer_ipc_meta(
+    fptr_t _fa) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  auto [handle_bytes, offsets] = fa->get_graph_buffer_ipc_meta();
+  auto options =
+      torch::TensorOptions().dtype(torch::kUInt8).device(torch::kCPU);
+  auto handles =
+      torch::empty({static_cast<int64_t>(handle_bytes.size())}, options);
+  std::memcpy(handles.data_ptr(), handle_bytes.data(), handle_bytes.size());
+  return {handles, std::move(offsets)};
+}
+
+void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
+                            const std::vector<std::vector<int64_t>>& offsets) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  fa->register_graph_buffers(handles, offsets);
+}
+
+void free_meta_buffer(void* buffer) { CUDACHECK(hipFree(buffer)); }
+
+torch::Tensor get_meta_buffer_ipc_handle(torch::Tensor& inp) {
+  auto options =
+      torch::TensorOptions().dtype(torch::kUInt8).device(torch::kCPU);
+  auto data_handle =
+      torch::empty({static_cast<int64_t>(sizeof(hipIpcMemHandle_t))}, options);
+  CUDACHECK(hipIpcGetMemHandle((hipIpcMemHandle_t*)data_handle.data_ptr(),
+                                inp.data_ptr()));
+  return data_handle;
+}
+
+torch::Tensor allocate_meta_buffer(int64_t size) {
+  auto device_index = c10::hip::current_device();
+  at::DeviceGuard device_guard(at::Device(at::DeviceType::CUDA, device_index));
+  void* buffer;
+  hipStreamCaptureMode mode = hipStreamCaptureModeRelaxed;
+  auto stream = c10::hip::getCurrentHIPStreamMasqueradingAsCUDA().stream();
+  AT_CUDA_CHECK(hipThreadExchangeStreamCaptureMode(&mode));
+  AT_CUDA_CHECK(
+      hipExtMallocWithFlags((void**)&buffer, size, hipDeviceMallocUncached));
+  AT_CUDA_CHECK(hipMemsetAsync(buffer, 0, size, stream));
+  AT_CUDA_CHECK(hipStreamSynchronize(stream));
+  AT_CUDA_CHECK(hipThreadExchangeStreamCaptureMode(&mode));
+  auto options = torch::TensorOptions()
+                     .dtype(torch::kI8)
+                     .device(torch::kCUDA, device_index);
+  return torch::from_blob(buffer, {size}, free_meta_buffer, options);
+}
+
+std::vector<uint8_t> get_device_bdf(int dev) {
+  char busIdStr[] = "0000:00:00.0";
+  std::vector<uint8_t> bdf(sizeof(busIdStr), 0);
+  CUDACHECK(hipDeviceGetPCIBusId((char*)bdf.data(), sizeof(busIdStr), dev));
+  bdf.resize(bdf.size() - 1);  // remove trailing NULL
+  return bdf;
+}

sgl-kernel/src/sgl-kernel/include/sgl_kernels_ops.h

@@ -34,8 +34,23 @@ limitations under the License.
     return PyModule_Create(&module);                                                                  \
   }
 
-// trt_reduce
 using fptr_t = int64_t;
+#ifdef USE_ROCM
+fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data, const std::vector<std::string>& handles,
+                      const std::vector<int64_t>& offsets, int64_t rank, bool full_nvlink);
+void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
+void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer, torch::Tensor& out);
+void dispose(fptr_t _fa);
+int64_t meta_size();
+void register_buffer(fptr_t _fa, torch::Tensor& t, const std::vector<std::string>& handles,
+                     const std::vector<int64_t>& offsets);
+std::tuple<torch::Tensor, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
+void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
+                            const std::vector<std::vector<int64_t>>& offsets);
+torch::Tensor allocate_meta_buffer(int64_t size);
+torch::Tensor get_meta_buffer_ipc_handle(torch::Tensor& inp);
+#else
+// trt_reduce
 fptr_t init_custom_ar(int64_t rank_id, int64_t world_size, torch::Tensor& rank_data, const std::vector<fptr_t>& buffers,
                       const std::vector<fptr_t>& tmp_result_buffers, const std::vector<fptr_t>& barrier_in,
                       const std::vector<fptr_t>& barrier_out);
@@ -44,6 +59,7 @@ void all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
 std::tuple<std::vector<int64_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
 void register_graph_buffers(fptr_t _fa, const std::vector<std::vector<int64_t>>& handles,
                             const std::vector<std::vector<int64_t>>& offsets);
+#endif
 
 // moe_align_block_size
 void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, int64_t block_size,

sgl-kernel/src/sgl-kernel/ops/__init__.py

@@ -64,28 +64,79 @@ def apply_rope_with_cos_sin_cache_inplace(
         )
 
 
-def init_custom_reduce(
-    rank_id, num_devices, rank_data, buffers, tmp_buffers, barrier_in, barrier_out
-):
-    return torch.ops.sgl_kernels.init_custom_ar(
-        rank_id, num_devices, rank_data, buffers, tmp_buffers, barrier_in, barrier_out
-    )
+if torch.version.hip is not None:
+
+    def init_custom_ar(
+        meta: torch.Tensor,
+        rank_data: torch.Tensor,
+        handles: List[str],
+        offsets: List[int],
+        rank: int,
+        full_nvlink: bool,
+    ) -> int:
+        return torch.ops.sgl_kernels.init_custom_ar(
+            meta, rank_data, handles, offsets, rank, full_nvlink
+        )
+
+    def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
+        torch.ops.sgl_kernels.all_reduce_reg(fa, inp, out)
+
+    def all_reduce_unreg(
+        fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor, out: torch.Tensor
+    ) -> None:
+        torch.ops.sgl_kernels.all_reduce_unreg(fa, inp, reg_buffer, out)
 
+    def dispose(fa: int) -> None:
+        torch.ops.sgl_kernels.dispose(fa)
 
-def custom_dispose(fa):
-    torch.ops.sgl_kernels.dispose(fa)
+    def meta_size() -> int:
+        return torch.ops.sgl_kernels.meta_size()
 
+    def register_buffer(
+        fa: int, t: torch.Tensor, handles: List[str], offsets: List[int]
+    ) -> None:
+        return torch.ops.sgl_kernels.register_buffer(fa, t, handles, offsets)
 
-def custom_reduce(fa, inp, out):
-    torch.ops.sgl_kernels.all_reduce(fa, inp, out)
+    def get_graph_buffer_ipc_meta(fa: int) -> Tuple[torch.Tensor, List[int]]:
+        return torch.ops.sgl_kernels.get_graph_buffer_ipc_meta(fa)
+
+    def register_graph_buffers(
+        fa: int, handles: List[str], offsets: List[List[int]]
+    ) -> None:
+        torch.ops.sgl_kernels.register_graph_buffers(fa, handles, offsets)
+
+    def allocate_meta_buffer(size: int) -> torch.Tensor:
+        return torch.ops.sgl_kernels.allocate_meta_buffer(size)
+
+    def get_meta_buffer_ipc_handle(inp: torch.Tensor) -> torch.Tensor:
+        return torch.ops.sgl_kernels.get_meta_buffer_ipc_handle(inp)
+
+else:
+    # trt_reduce
+    def init_custom_reduce(
+        rank_id, num_devices, rank_data, buffers, tmp_buffers, barrier_in, barrier_out
+    ):
+        return torch.ops.sgl_kernels.init_custom_ar(
+            rank_id,
+            num_devices,
+            rank_data,
+            buffers,
+            tmp_buffers,
+            barrier_in,
+            barrier_out,
+        )
 
+    def custom_dispose(fa):
+        torch.ops.sgl_kernels.dispose(fa)
 
-def get_graph_buffer_ipc_meta(fa):
-    return torch.ops.sgl_kernels.get_graph_buffer_ipc_meta(fa)
+    def custom_reduce(fa, inp, out):
+        torch.ops.sgl_kernels.all_reduce(fa, inp, out)
 
+    def get_graph_buffer_ipc_meta(fa):
+        return torch.ops.sgl_kernels.get_graph_buffer_ipc_meta(fa)
 
-def register_graph_buffers(fa, handles, offsets):
-    torch.ops.sgl_kernels.register_graph_buffers(fa, handles, offsets)
+    def register_graph_buffers(fa, handles, offsets):
+        torch.ops.sgl_kernels.register_graph_buffers(fa, handles, offsets)
 
 
 def moe_align_block_size(

sgl-kernel/src/sgl-kernel/torch_extension_rocm.cc

@@ -19,6 +19,37 @@ limitations under the License.
 #include "sgl_kernels_ops.h"
 
 TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
+  // Custom all-reduce kernels
+  m.def(
+      "init_custom_ar(Tensor meta, Tensor rank_data, "
+      "str[] handles, int[] offsets, int rank, "
+      "bool full_nvlink) -> int");
+  m.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);
+
+  m.def("all_reduce_reg(int fa, Tensor inp, Tensor! out) -> ()");
+  m.impl("all_reduce_reg", torch::kCUDA, &all_reduce_reg);
+
+  m.def(
+      "all_reduce_unreg(int fa, Tensor inp, Tensor reg_buffer, Tensor! out) -> "
+      "()");
+  m.impl("all_reduce_unreg", torch::kCUDA, &all_reduce_unreg);
+
+  m.def("dispose", &dispose);
+
+  m.def("meta_size", &meta_size);
+
+  m.def(
+      "register_buffer(int fa, Tensor t, str[] handles, "
+      "int[] offsets) -> ()");
+  m.impl("register_buffer", torch::kCUDA, &register_buffer);
+
+  m.def("get_graph_buffer_ipc_meta", &get_graph_buffer_ipc_meta);
+  m.def("register_graph_buffers", &register_graph_buffers);
+  m.def("allocate_meta_buffer", &allocate_meta_buffer);
+  m.impl("allocate_meta_buffer", torch::kCUDA, &allocate_meta_buffer);
+  m.def("get_meta_buffer_ipc_handle", &get_meta_buffer_ipc_handle);
+  m.impl("get_meta_buffer_ipc_handle", torch::kCPU, &get_meta_buffer_ipc_handle);
+
   // moe_align_block_size
   m.def(
       "moe_align_block_size(Tensor topk_ids, int num_experts, int block_size, Tensor! sorted_token_ids, Tensor! "