# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import os
from typing import Optional

from vllm.model_executor.layers.quantization.kernels.scaled_mm.aiter import (
    AiterScaledMMLinearKernel,
)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.cpu import (
    CPUScaledMMLinearKernel,
)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
    CutlassScaledMMLinearKernel,
)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
    ScaledMMLinearKernel,
    ScaledMMLinearLayerConfig,
)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
    TritonScaledMMLinearKernel,
)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.xla import (
    XLAScaledMMLinearKernel,
)
from vllm.platforms import PlatformEnum, current_platform

# in priority/performance order (when available)
_POSSIBLE_KERNELS: dict[PlatformEnum, list[type[ScaledMMLinearKernel]]] = {
    PlatformEnum.CPU: [CPUScaledMMLinearKernel],
    PlatformEnum.CUDA: [CutlassScaledMMLinearKernel],
    PlatformEnum.ROCM: [AiterScaledMMLinearKernel, TritonScaledMMLinearKernel],
    PlatformEnum.TPU: [XLAScaledMMLinearKernel],
}


def choose_scaled_mm_linear_kernel(
    config: ScaledMMLinearLayerConfig, compute_capability: Optional[int] = None
) -> type[ScaledMMLinearKernel]:
    """
    Choose an ScaledMMLinearKernel that can implement the given config for the
    given compute capability. Attempts to choose the best kernel in terms of
    performance.

    Args:
        config (ScaledMMLinearLayerConfig): Description of the linear layer
            to be implemented.
        compute_capability (Optional[int], optional): The compute capability of
            the target device, if None uses `current_platform` to get the
            compute capability. Defaults to None.

    Raises:
        ValueError: If no kernel can implement the given config.

    Returns:
        type[ScaledMMLinearKernel]: Chosen kernel.
    """

    if compute_capability is None:
        _cc = current_platform.get_device_capability()
        if _cc is not None:
            compute_capability = _cc[0] * 10 + _cc[1]

    failure_reasons = []
    for kernel in _POSSIBLE_KERNELS[current_platform._enum]:
        if kernel.__name__ in os.environ.get("VLLM_DISABLED_KERNELS", "").split(","):
            failure_reasons.append(
                f" {kernel.__name__} disabled by environment variable"
            )
            continue

        # If the current platform uses compute_capability,
        # make sure the kernel supports the compute cability.
        if compute_capability is not None:
            kernel_min_capability = kernel.get_min_capability()
            if (
                kernel_min_capability is not None
                and kernel_min_capability > compute_capability
            ):
                failure_reasons.append(
                    f"{kernel.__name__} requires capability "
                    f"{kernel_min_capability}, current compute capability "
                    f"is {compute_capability}"
                )
                continue

        can_implement, failure_reason = kernel.can_implement(config)
        if can_implement:
            return kernel
        else:
            failure_reasons.append(
                f" {kernel.__name__} cannot implement due to: {failure_reason}"
            )

    raise ValueError(
        "Failed to find a kernel that can implement the "
        "ScaledMM linear layer. Reasons: \n" + "\n".join(failure_reasons)
    )
