# 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
            ]

        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)
        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
