# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

# SPDX-License-Identifier: Apache-2.0
# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/platforms/cuda.py
"""Code inside this file can safely assume cuda platform, e.g. importing
pynvml. However, it should not initialize cuda context.
"""

import os
from collections.abc import Callable
from functools import lru_cache, wraps
from typing import Any, TypeVar

import psutil
import torch
from typing_extensions import ParamSpec

from sglang.multimodal_gen import envs
from sglang.multimodal_gen.runtime.platforms.interface import (
    AttentionBackendEnum,
    DeviceCapability,
    Platform,
    PlatformEnum,
)
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.utils import import_pynvml

logger = init_logger(__name__)

_P = ParamSpec("_P")
_R = TypeVar("_R")

pynvml = import_pynvml()  # type: ignore[no-untyped-call]

# pytorch 2.5 uses cudnn sdpa by default, which will cause crash on some models
# see https://github.com/huggingface/diffusers/issues/9704 for details
torch.backends.cuda.enable_cudnn_sdp(False)


def device_id_to_physical_device_id(device_id: int) -> int:
    if "CUDA_VISIBLE_DEVICES" in os.environ:
        device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
        if device_ids == [""]:
            msg = (
                "CUDA_VISIBLE_DEVICES is set to empty string, which means"
                " GPU support is disabled. If you are using ray, please unset"
                " the environment variable `CUDA_VISIBLE_DEVICES` inside the"
                " worker/actor. "
                "Check https://github.com/vllm-project/vllm/issues/8402 for"
                " more information."
            )
            raise RuntimeError(msg)
        physical_device_id = device_ids[device_id]
        return int(physical_device_id)
    else:
        return device_id


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()

    return wrapper


class CudaPlatformBase(Platform):
    _enum = PlatformEnum.CUDA
    device_name: str = "cuda"
    device_type: str = "cuda"
    dispatch_key: str = "CUDA"
    device_control_env_var: str = "CUDA_VISIBLE_DEVICES"

    @classmethod
    def get_local_torch_device(cls) -> torch.device:
        return torch.device(f"cuda:{envs.LOCAL_RANK}")

    @classmethod
    def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
        raise NotImplementedError

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        raise NotImplementedError

    @classmethod
    @lru_cache(maxsize=1)
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        raise NotImplementedError

    @classmethod
    def is_async_output_supported(cls, enforce_eager: bool | None) -> bool:
        if enforce_eager:
            logger.warning(
                "To see benefits of async output processing, enable CUDA "
                "graph. Since, enforce-eager is enabled, async output "
                "processor cannot be used"
            )
            return False
        return True

    @classmethod
    def is_full_nvlink(cls, device_ids: list[int]) -> bool:
        raise NotImplementedError

    @classmethod
    def log_warnings(cls) -> None:
        pass

    @classmethod
    def get_current_memory_usage(
        cls, device: torch.types.Device | None = None
    ) -> float:
        torch.cuda.reset_peak_memory_stats(device)
        return float(torch.cuda.max_memory_allocated(device))

    @classmethod
    def get_available_gpu_memory(
        cls,
        device_id: int = 0,
        distributed: bool = False,
        empty_cache: bool = True,
        cpu_group: Any = None,
    ) -> float:
        if empty_cache:
            torch.cuda.empty_cache()

        if torch.distributed.is_initialized():
            device_id = torch.distributed.get_rank()

        device_props = torch.cuda.get_device_properties(device_id)
        if device_props.is_integrated:
            free_gpu_memory = psutil.virtual_memory().available
        else:
            free_gpu_memory, _ = torch.cuda.mem_get_info(device_id)

        if distributed:
            import torch.distributed as dist

            tensor = torch.tensor(free_gpu_memory, dtype=torch.float32, device="cuda")
            dist.all_reduce(tensor, op=dist.ReduceOp.MIN, group=cpu_group)
            free_gpu_memory = float(tensor.item())

        return free_gpu_memory / (1 << 30)

    @classmethod
    def get_attn_backend_cls_str(
        cls,
        selected_backend: AttentionBackendEnum | None,
        head_size: int,
        dtype: torch.dtype,
    ) -> str:
        target_backend: AttentionBackendEnum | None = None
        # TODO(will): maybe come up with a more general interface for local attention
        # if distributed is False, we always try to use Flash attn
        if selected_backend == AttentionBackendEnum.SLIDING_TILE_ATTN:
            try:
                from st_attn import sliding_tile_attention  # noqa: F401

                from sglang.multimodal_gen.runtime.layers.attention.backends.sliding_tile_attn import (  # noqa: F401
                    SlidingTileAttentionBackend,
                )

                logger.info("Using Sliding Tile Attention backend")

                return "sglang.multimodal_gen.runtime.layers.attention.backends.sliding_tile_attn.SlidingTileAttentionBackend"
            except ImportError as e:
                logger.error(
                    "Failed to import Sliding Tile Attention backend: %s", str(e)
                )
                raise ImportError(
                    "Sliding Tile Attention backend is not installed. "
                ) from e
        elif selected_backend == AttentionBackendEnum.SAGE_ATTN:
            try:
                from sageattention import sageattn  # noqa: F401

                from sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn import (  # noqa: F401
                    SageAttentionBackend,
                )

                logger.info("Using Sage Attention backend")

                return "sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn.SageAttentionBackend"
            except ImportError as e:
                logger.info(e)
                logger.info(
                    "Sage Attention backend is not installed (To install it, run `pip install sageattention==2.2.0 --no-build-isolation`). Falling back to Flash Attention."
                )
                target_backend = AttentionBackendEnum.FA
        elif selected_backend == AttentionBackendEnum.SAGE_ATTN_3:
            try:
                from sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn3 import (  # noqa: F401
                    SageAttention3Backend,
                )

                logger.info("Using Sage Attention 3 backend")
                return "sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn3.SageAttention3Backend"
            except ImportError as e:
                logger.info(e)
                logger.info(
                    "Sage Attention 3 backend is not installed (To install it, see https://github.com/thu-ml/SageAttention/tree/main/sageattention3_blackwell#installation). Falling back to Torch SDPA."
                )
                target_backend = AttentionBackendEnum.TORCH_SDPA
        elif selected_backend == AttentionBackendEnum.VIDEO_SPARSE_ATTN:
            try:
                from vsa import block_sparse_attn  # noqa: F401

                from sglang.multimodal_gen.runtime.layers.attention.backends.video_sparse_attn import (  # noqa: F401
                    VideoSparseAttentionBackend,
                )

                logger.info("Using Video Sparse Attention backend")

                return "sglang.multimodal_gen.runtime.layers.attention.backends.video_sparse_attn.VideoSparseAttentionBackend"
            except ImportError as e:
                logger.error(
                    "Failed to import Video Sparse Attention backend: %s", str(e)
                )
                raise ImportError(
                    "Video Sparse Attention backend is not installed."
                ) from e
        elif selected_backend == AttentionBackendEnum.SPARSE_VIDEO_GEN_2_ATTN:
            try:
                from svg.kernels.triton.permute import (  # noqa: F401
                    apply_inverse_permutation_triton,
                    permute_tensor_by_labels_triton,
                )
                from svg.kmeans_utils import (  # noqa: F401
                    batch_kmeans_Euclid,
                    density_calculation,
                    dynamic_block_sparse_fwd_flashinfer,
                    identify_dynamic_map,
                )

                from sglang.multimodal_gen.runtime.layers.attention.backends.sparse_video_gen_2_attn import (  # noqa: F401
                    SparseVideoGen2AttentionBackend,
                )

                logger.info("Using Sparse Video Gen 2 (SAP) Attention backend")
                return "sglang.multimodal_gen.runtime.layers.attention.backends.sparse_video_gen_2_attn.SparseVideoGen2AttentionBackend"
            except ImportError as e:
                logger.error(
                    "Failed to import Sparse Video Gen 2 (SAP) Attention backend: %s",
                    str(e),
                )
                raise ImportError(
                    "Sparse Video Gen 2 (SAP) Attention backend is not installed. "
                    "Please install it by following the instructions at "
                    "https://github.com/svg-project/Sparse-VideoGen"
                ) from e
        elif selected_backend == AttentionBackendEnum.VMOBA_ATTN:
            try:
                from kernel.attn.vmoba_attn.vmoba import moba_attn_varlen  # noqa: F401

                from sglang.multimodal_gen.runtime.layers.attention.backends.vmoba import (  # noqa: F401
                    VMOBAAttentionBackend,
                )

                logger.info("Using Video MOBA Attention backend")

                return "sglang.multimodal_gen.runtime.layers.attention.backends.vmoba.VMOBAAttentionBackend"
            except ImportError as e:
                logger.error(
                    "Failed to import Video MoBA Attention backend: %s", str(e)
                )
                raise ImportError(
                    "Video MoBA Attention backend is not installed. "
                ) from e
        elif selected_backend == AttentionBackendEnum.AITER:
            logger.info("Using AITer backend")
            return "sglang.multimodal_gen.runtime.layers.attention.backends.aiter.AITerBackend"
        elif selected_backend == AttentionBackendEnum.TORCH_SDPA:
            logger.info("Using Torch SDPA backend")
            return "sglang.multimodal_gen.runtime.layers.attention.backends.sdpa.SDPABackend"
        elif selected_backend == AttentionBackendEnum.SLA_ATTN:
            logger.info("Using Sparse Linear Attention backend")
            return "sglang.multimodal_gen.runtime.layers.attention.backends.sparse_linear_attn.SparseLinearAttentionBackend"
        elif selected_backend == AttentionBackendEnum.SAGE_SLA_ATTN:
            logger.info("Using Sage Sparse Linear Attention backend")
            return "sglang.multimodal_gen.runtime.layers.attention.backends.sparse_linear_attn.SageSparseLinearAttentionBackend"
        elif selected_backend == AttentionBackendEnum.FA2:
            from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn_2 import (  # noqa: F401
                FlashAttention2Backend,
            )

            logger.info("Using FlashAttention2 backend")
            return "sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn_2.FlashAttention2Backend"
        elif selected_backend in [
            AttentionBackendEnum.FA,
        ]:
            if cls.is_sm120():
                logger.info(
                    "FlashAttention is not supported on SM12.x in this build; falling back to Torch SDPA."
                )
                target_backend = AttentionBackendEnum.TORCH_SDPA
            elif cls.is_blackwell():
                from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (
                    set_fa_ver,
                )

                set_fa_ver(4)
                target_backend = AttentionBackendEnum.FA
            else:
                target_backend = AttentionBackendEnum.FA
        elif selected_backend:
            raise ValueError(f"Invalid attention backend for {cls.device_name}")
        else:
            if cls.is_sm120():
                # On SM12.x, the sgl-kernel FlashAttention wheels may not include
                # support yet. Default to Torch SDPA for correctness.
                logger.info("Defaulting to Torch SDPA backend on SM12.x")
                target_backend = AttentionBackendEnum.TORCH_SDPA
            elif cls.is_blackwell():
                from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (
                    set_fa_ver,
                )

                set_fa_ver(4)
                target_backend = AttentionBackendEnum.FA
            else:
                target_backend = AttentionBackendEnum.FA

        # Ensure we have a target backend selected before validation/fallback.
        if target_backend is None:
            target_backend = AttentionBackendEnum.FA

        if target_backend == AttentionBackendEnum.FA and cls.is_blackwell():
            from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (
                set_fa_ver,
            )

            set_fa_ver(4)

        if not cls.has_device_capability(80):
            logger.info("Cannot use FlashAttention backend for Volta and Turing GPUs.")
            target_backend = AttentionBackendEnum.TORCH_SDPA
        elif dtype not in (torch.float16, torch.bfloat16):
            logger.info(
                "Cannot use FlashAttention backend for dtype other than "
                "torch.float16 or torch.bfloat16."
            )
            target_backend = AttentionBackendEnum.TORCH_SDPA
        # FlashAttn is valid for the model, checking if the package is
        # installed.
        if target_backend == AttentionBackendEnum.FA:
            try:
                from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (  # noqa: F401
                    FlashAttentionBackend,
                )

                supported_sizes = FlashAttentionBackend.get_supported_head_sizes()
                if head_size not in supported_sizes:
                    logger.info(
                        "Cannot use FlashAttention backend for head size %d.",
                        head_size,
                    )
                    target_backend = AttentionBackendEnum.TORCH_SDPA
            except ImportError:
                logger.info(
                    "Cannot use FlashAttention backend because the "
                    "flash_attn package is not found. "
                    "Make sure that flash_attn was built and installed "
                    "(on by default)."
                )
                target_backend = AttentionBackendEnum.TORCH_SDPA

        if target_backend == AttentionBackendEnum.TORCH_SDPA:
            logger.info("Using Torch SDPA backend")

            return "sglang.multimodal_gen.runtime.layers.attention.backends.sdpa.SDPABackend"

        logger.info("Using FlashAttention (FA3 for hopper, FA4 for blackwell) backend")

        return "sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn.FlashAttentionBackend"

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        return "sglang.multimodal_gen.runtime.distributed.device_communicators.cuda_communicator.CudaCommunicator"  # noqa


# NVML utils
# Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
# all the related functions work on real physical device ids.
# the major benefit of using NVML is that it will not initialize CUDA
class NvmlCudaPlatform(CudaPlatformBase):
    @classmethod
    @lru_cache(maxsize=8)
    @with_nvml_context
    def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
        try:
            physical_device_id = device_id_to_physical_device_id(device_id)
            handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
            major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
            return DeviceCapability(major=major, minor=minor)
        except RuntimeError:
            return None

    @classmethod
    @lru_cache(maxsize=8)
    @with_nvml_context
    def has_device_capability(
        cls,
        capability: tuple[int, int] | int,
        device_id: int = 0,
    ) -> bool:
        try:
            return bool(super().has_device_capability(capability, device_id))
        except RuntimeError:
            return False

    @classmethod
    @lru_cache(maxsize=8)
    @with_nvml_context
    def get_device_name(cls, device_id: int = 0) -> str:
        physical_device_id = device_id_to_physical_device_id(device_id)
        return cls._get_physical_device_name(physical_device_id)

    @classmethod
    @lru_cache(maxsize=8)
    @with_nvml_context
    def get_device_uuid(cls, device_id: int = 0) -> str:
        physical_device_id = device_id_to_physical_device_id(device_id)
        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
        return str(pynvml.nvmlDeviceGetUUID(handle))

    @classmethod
    @lru_cache(maxsize=8)
    @with_nvml_context
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        physical_device_id = device_id_to_physical_device_id(device_id)
        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
        return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)

    @classmethod
    @with_nvml_context
    def is_full_nvlink(cls, 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:
                            return False
                    except pynvml.NVMLError:
                        logger.exception(
                            "NVLink detection failed. This is normal if"
                            " your machine has no NVLink equipped."
                        )
                        return False
        return True

    @classmethod
    def _get_physical_device_name(cls, device_id: int = 0) -> str:
        handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
        return str(pynvml.nvmlDeviceGetName(handle))

    @classmethod
    @with_nvml_context
    def log_warnings(cls) -> None:
        device_ids: int = pynvml.nvmlDeviceGetCount()
        if device_ids > 1:
            device_names = [cls._get_physical_device_name(i) for i in range(device_ids)]
            if (
                len(set(device_names)) > 1
                and os.environ.get("CUDA_DEVICE_ORDER") != "PCI_BUS_ID"
            ):
                logger.warning(
                    "Detected different devices in the system: %s. Please"
                    " make sure to set `CUDA_DEVICE_ORDER=PCI_BUS_ID` to "
                    "avoid unexpected behavior.",
                    ", ".join(device_names),
                )


class NonNvmlCudaPlatform(CudaPlatformBase):
    @classmethod
    def get_device_capability(cls, device_id: int = 0) -> DeviceCapability:
        major, minor = torch.cuda.get_device_capability(device_id)
        return DeviceCapability(major=major, minor=minor)

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        return str(torch.cuda.get_device_name(device_id))

    @classmethod
    @lru_cache(maxsize=1)
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        device_props = torch.cuda.get_device_properties(device_id)
        return int(device_props.total_memory)

    @classmethod
    def is_full_nvlink(cls, physical_device_ids: list[int]) -> bool:
        logger.exception(
            "NVLink detection not possible, as context support was"
            " not found. Assuming no NVLink available."
        )
        return False


# Autodetect either NVML-enabled or non-NVML platform
# based on whether NVML is available.
nvml_available = False
try:
    try:
        pynvml.nvmlInit()
        nvml_available = True
    except Exception:
        # On Jetson, NVML is not supported.
        nvml_available = False
finally:
    if nvml_available:
        pynvml.nvmlShutdown()

CudaPlatform = NvmlCudaPlatform if nvml_available else NonNvmlCudaPlatform

try:
    from sphinx.ext.autodoc.mock import _MockModule

    if not isinstance(pynvml, _MockModule):
        CudaPlatform.log_warnings()
except ModuleNotFoundError:
    CudaPlatform.log_warnings()