import logging
import os
from contextlib import contextmanager
from dataclasses import dataclass
from enum import IntEnum, auto
from typing import Callable, Dict, List, Optional, Tuple

import torch
from tqdm.contrib.concurrent import thread_map

from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import get_bool_env_var, get_device_sm, get_int_env_var, is_cuda

logger = logging.getLogger(__name__)
_ENABLE_JIT_DEEPGEMM = False

# try:
#     import deep_gemm
#     from deep_gemm import get_num_sms
#     from deep_gemm.jit.compiler import get_nvcc_compiler
#     from deep_gemm.jit_kernels.gemm import get_best_configs
#     from deep_gemm.jit_kernels.runtime import FP8GemmRuntime, GemmType
#     from deep_gemm.jit_kernels.tuner import jit_tuner

#     sm_version = get_device_sm()
#     if sm_version == 90:
#         if get_bool_env_var("SGL_ENABLE_JIT_DEEPGEMM", default="true"):
#             _ENABLE_JIT_DEEPGEMM = True
# except ImportError:
#     logger.warning("Failed to import deepgemm, disable _ENABLE_JIT_DEEPGEMM.")


def get_enable_jit_deepgemm():
    return _ENABLE_JIT_DEEPGEMM


_BUILTIN_M_LIST = list(range(1, 1024 * 16 + 1))
_ENABLE_JIT_DEEPGEMM_PRECOMPILE = get_bool_env_var(
    "SGL_JIT_DEEPGEMM_PRECOMPILE", "true"
)
_DO_COMPILE_ALL = True
_IS_FIRST_RANK_ON_NODE = get_bool_env_var("SGL_IS_FIRST_RANK_ON_NODE", "true")
_COMPILE_WORKERS = get_int_env_var("SGL_JIT_DEEPGEMM_COMPILE_WORKERS", 4)
_IN_PRECOMPILE_STAGE = get_bool_env_var("SGL_IN_DEEPGEMM_PRECOMPILE_STAGE", "false")

# Force redirect deep_gemm cache_dir
os.environ["DG_JIT_CACHE_DIR"] = os.getenv(
    "SGL_DG_CACHE_DIR", os.path.join(os.path.expanduser("~"), ".cache", "deep_gemm")
)

# Refer to https://github.com/deepseek-ai/DeepGEMM/commit/d75b218b7b8f4a5dd5406ac87905039ead3ae42f
# NVRTC may have performance loss with some cases.
# And NVCC JIT speed is also 9x faster in the ref commit
_USE_NVRTC_DEFAULT = "0"
if _ENABLE_JIT_DEEPGEMM:
    try:
        get_nvcc_compiler()
    except:
        logger.warning(
            "NVCC Compiler not found, use NVRTC for DeepGEMM JIT "
            "and may have performance loss with some cases."
        )
        _USE_NVRTC_DEFAULT = "1"
os.environ["DG_JIT_USE_NVRTC"] = os.getenv("SGL_DG_USE_NVRTC", _USE_NVRTC_DEFAULT)


def update_deep_gemm_config(gpu_id: int, server_args: ServerArgs):
    global _BUILTIN_M_LIST
    global _DO_COMPILE_ALL
    global _IS_FIRST_RANK_ON_NODE

    # Generate m_max
    m_max = 1024 * 16
    if server_args.chunked_prefill_size < 1:
        m_max = 1024 * 64
    elif server_args.chunked_prefill_size > 8192:
        m_max = server_args.chunked_prefill_size * 2
    m_max = min(1024 * 128, m_max)
    _BUILTIN_M_LIST = list(range(1, m_max + 1))

    _IS_FIRST_RANK_ON_NODE = ServerArgs.base_gpu_id == gpu_id

    # Check if is the first rank on node.
    # Default each rank will try compile all Ms to
    # load all symbols at the launch stages.
    # Avoid loading symbols at the serving stages.
    _DO_COMPILE_ALL = _IS_FIRST_RANK_ON_NODE or not _IN_PRECOMPILE_STAGE


class DeepGemmKernelType(IntEnum):
    GROUPED_GEMM_NT_F8F8BF16_MASKED = auto()
    GROUPED_GEMM_NT_F8F8BF16_CONTIG = auto()
    GEMM_NT_F8F8BF16 = auto()


@dataclass
class DeepGemmKernelHelper:
    name: str
    compile_func: Callable[
        [
            int,
            int,
            int,
            Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]],
        ],
        None,
    ]
    configure_func: Callable[
        [int, int, int, int, int],
        Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]],
    ]


_INITIALIZATION_DICT: Dict[Tuple[DeepGemmKernelType, int, int, int], bool] = dict()


def _compile_warning_1():
    if not _IN_PRECOMPILE_STAGE and _IS_FIRST_RANK_ON_NODE:
        logger.warning(
            "Entering DeepGEMM JIT Pre-Compile session. "
            "And it may takes a long time(Typically 10-20 mins) "
            "if you have not run `sglang.compile_deep_gemm`. "
            "It is recommended to run `sglang.compile_deep_gemm` with same args as `sglang.launch_server`"
            " for pre-compilation to reduce the overhead if you have not run it before. "
            "For example: "
            "`python3 -m sglang.compile_deep_gemm --model deepseek-ai/DeepSeek-V3 --tp 8 --trust-remote-code`"
        )


def _compile_warning_2():
    logger.warning(
        "Entering DeepGEMM JIT Single Kernel Compile session. "
        "And it will makes inference throughput becomes flaky. "
        "Please run `sglang.compile_deep_gemm` with same args as `sglang.launch_server`"
        " for pre-compilation to solve this issue. "
        "For example: "
        "`python3 -m sglang.compile_deep_gemm --model deepseek-ai/DeepSeek-V3 --tp 8 --trust-remote-code`"
    )


def _compile_grouped_gemm_nt_f8f8bf16_masked_one(
    n: int,
    k: int,
    num_groups: int,
    config: Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]],
) -> None:
    num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = config
    block_k = 128
    num_tma_threads = 128
    num_math_threads_per_group = 128
    kwargs = {
        "NUM_TMA_THREADS": num_tma_threads,
        "NUM_MATH_THREADS_PER_GROUP": num_math_threads_per_group,
        "BLOCK_K": block_k,
        "NUM_SMS": num_sms,
        "SMEM_SIZE": smem_config[0],
    }
    _, _ = jit_tuner.compile_and_tune(
        name="m_grouped_gemm_fp8_fp8_bf16_nt",
        keys={
            "N": n,
            "K": k,
            "BLOCK_M": block_m,
            "BLOCK_N": block_n,
            "SWIZZLE_D_MODE": smem_config[1],
            "BLOCK_N_PADDING": smem_config[2],
            "NUM_GROUPS": num_groups,
            "NUM_STAGES": num_stages,
            "NUM_TMA_MULTICAST": tma_multicast_config[0],
            "IS_TMA_MULTICAST_ON_A": tma_multicast_config[1],
            "GEMM_TYPE": GemmType.GroupedMasked,
        },
        space=(),
        kwargs=kwargs,
        runtime_cls=FP8GemmRuntime,
    )


def _compile_grouped_gemm_nt_f8f8bf16_contig_one(
    n: int,
    k: int,
    num_groups: int,
    config: Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]],
) -> None:
    num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = config
    block_k = 128
    num_tma_threads = 128
    num_math_threads_per_group = 128
    kwargs = {
        "NUM_TMA_THREADS": num_tma_threads,
        "NUM_MATH_THREADS_PER_GROUP": num_math_threads_per_group,
        "BLOCK_K": block_k,
        "NUM_SMS": num_sms,
        "SMEM_SIZE": smem_config[0],
    }
    _, _ = jit_tuner.compile_and_tune(
        name="m_grouped_gemm_fp8_fp8_bf16_nt",
        keys={
            "N": n,
            "K": k,
            "BLOCK_M": block_m,
            "BLOCK_N": block_n,
            "SWIZZLE_D_MODE": smem_config[1],
            "BLOCK_N_PADDING": smem_config[2],
            "NUM_GROUPS": num_groups,
            "NUM_STAGES": num_stages,
            "NUM_TMA_MULTICAST": tma_multicast_config[0],
            "IS_TMA_MULTICAST_ON_A": tma_multicast_config[1],
            "GEMM_TYPE": GemmType.GroupedContiguous,
        },
        space=(),
        kwargs=kwargs,
        runtime_cls=FP8GemmRuntime,
    )


def _compile_gemm_nt_f8f8bf16_one(
    n: int,
    k: int,
    _: int,  # _ is a dummy parameter to align with other interfaces
    config: Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]],
) -> None:
    num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = config
    block_k = 128
    num_tma_threads = 128
    num_math_threads_per_group = 128
    kwargs = {
        "GEMM_TYPE": GemmType.Normal,
        "NUM_TMA_THREADS": num_tma_threads,
        "NUM_MATH_THREADS_PER_GROUP": num_math_threads_per_group,
        "NUM_GROUPS": 1,
        "BLOCK_K": block_k,
        "NUM_SMS": num_sms,
        "SMEM_SIZE": smem_config[0],
    }
    _, _ = jit_tuner.compile_and_tune(
        name="gemm_fp8_fp8_bf16_nt",
        keys={
            "N": n,
            "K": k,
            "BLOCK_M": block_m,
            "BLOCK_N": block_n,
            "SWIZZLE_D_MODE": smem_config[1],
            "BLOCK_N_PADDING": smem_config[2],
            "NUM_STAGES": num_stages,
            "NUM_TMA_MULTICAST": tma_multicast_config[0],
            "IS_TMA_MULTICAST_ON_A": tma_multicast_config[1],
        },
        space=(),
        kwargs=kwargs,
        runtime_cls=FP8GemmRuntime,
    )


_KERNEL_HELPER_DICT: Dict[DeepGemmKernelType, DeepGemmKernelHelper] = {
    DeepGemmKernelType.GROUPED_GEMM_NT_F8F8BF16_MASKED: DeepGemmKernelHelper(
        name="m_grouped_gemm_fp8_fp8_bf16_nt_masked",
        compile_func=_compile_grouped_gemm_nt_f8f8bf16_masked_one,
        configure_func=lambda m, n, k, num_groups, num_sms: get_best_configs(
            m, n, k, num_groups, num_sms, is_grouped_masked=True
        ),
    ),
    DeepGemmKernelType.GROUPED_GEMM_NT_F8F8BF16_CONTIG: DeepGemmKernelHelper(
        name="m_grouped_gemm_fp8_fp8_bf16_nt_contiguous",
        compile_func=_compile_grouped_gemm_nt_f8f8bf16_contig_one,
        configure_func=lambda m, n, k, _, num_sms: get_best_configs(
            m, n, k, 1, num_sms, is_grouped_contiguous=True
        ),
    ),
    DeepGemmKernelType.GEMM_NT_F8F8BF16: DeepGemmKernelHelper(
        name="gemm_fp8_fp8_bf16_nt",
        compile_func=_compile_gemm_nt_f8f8bf16_one,
        configure_func=lambda m, n, k, _, num_sms: get_best_configs(
            m, n, k, 1, num_sms
        ),
    ),
}


def _maybe_compile_deep_gemm_one_type_all(
    kernel_type: DeepGemmKernelType,
    n: int,
    k: int,
    num_groups: int,
    m_list: Optional[List[int]] = None,
) -> None:

    global _INITIALIZATION_DICT
    global _BUILTIN_M_LIST

    query_key = (kernel_type, n, k, num_groups)
    if (
        _ENABLE_JIT_DEEPGEMM_PRECOMPILE
        and _DO_COMPILE_ALL
        and _INITIALIZATION_DICT.get(query_key) is None
    ):
        _INITIALIZATION_DICT[query_key] = True

        kernel_helper = _KERNEL_HELPER_DICT[kernel_type]
        _compile_warning_1()
        logger.info(
            f"Try DeepGEMM JIT Compiling for "
            f"<{kernel_helper.name}> N={n}, K={k}, num_groups={num_groups} with all Ms."
            f"{' It only takes a little time (typically 1 sec) if you have run `python3 -m sglang.compile_deep_gemm`. ' if not _IN_PRECOMPILE_STAGE else ''}"
        )

        # NOTE(alcanderian): get_num_sms should be change when 2-batch-overlap is introduced
        num_sms = get_num_sms()
        collected_configs = set()
        for m in m_list if m_list is not None else _BUILTIN_M_LIST:
            # Put config into set to get unique configs and reduce cases to be compiled
            collected_configs.add(
                kernel_helper.configure_func(m, n, k, num_groups, num_sms)
            )
        compile_func = lambda config: kernel_helper.compile_func(
            n, k, num_groups, config
        )
        thread_map(compile_func, collected_configs, max_workers=_COMPILE_WORKERS)


def grouped_gemm_nt_f8f8bf16_masked(
    lhs: Tuple[torch.Tensor, torch.Tensor],
    rhs: Tuple[torch.Tensor, torch.Tensor],
    out: torch.Tensor,
    masked_m: torch.Tensor,
    expected_m: int,
):
    num_groups, _, k = lhs[0].shape
    _, n, _ = rhs[0].shape

    kernel_type = DeepGemmKernelType.GROUPED_GEMM_NT_F8F8BF16_MASKED
    _maybe_compile_deep_gemm_one_type_all(kernel_type, n, k, num_groups)

    with _log_jit_build(expected_m, n, k, kernel_type):
        deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
            lhs, rhs, out, masked_m, expected_m
        )


def grouped_gemm_nt_f8f8bf16_contig(
    lhs: Tuple[torch.Tensor, torch.Tensor],
    rhs: Tuple[torch.Tensor, torch.Tensor],
    out: torch.Tensor,
    m_indices: torch.Tensor,
):
    m, k = lhs[0].shape
    num_groups, n, _ = rhs[0].shape

    kernel_type = DeepGemmKernelType.GROUPED_GEMM_NT_F8F8BF16_CONTIG
    _maybe_compile_deep_gemm_one_type_all(kernel_type, n, k, num_groups)

    with _log_jit_build(m, n, k, kernel_type):
        deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(lhs, rhs, out, m_indices)


def gemm_nt_f8f8bf16(
    lhs: Tuple[torch.Tensor, torch.Tensor],
    rhs: Tuple[torch.Tensor, torch.Tensor],
    out: torch.Tensor,
):
    m, k = lhs[0].shape
    n, _ = rhs[0].shape

    kernel_type = DeepGemmKernelType.GEMM_NT_F8F8BF16
    _maybe_compile_deep_gemm_one_type_all(kernel_type, n, k, 1)

    with _log_jit_build(m, n, k, kernel_type):
        deep_gemm.gemm_fp8_fp8_bf16_nt(lhs, rhs, out)


@contextmanager
def _log_jit_build(M: int, N: int, K: int, kernel_type: DeepGemmKernelType):
    if _IN_PRECOMPILE_STAGE:
        yield
        return

    from deep_gemm.jit.runtime import RuntimeCache

    origin_func = RuntimeCache.get

    def __patched_func(self, *args, **kwargs):
        ret = origin_func(self, *args, **kwargs)
        if ret is None:
            kernel_helper = _KERNEL_HELPER_DICT[kernel_type]
            _compile_warning_2()
            logger.warning(
                f"DeepGEMM JIT Compiling for <{kernel_helper.name}> M={M}, N={N}, K={K}. Please wait."
            )
        return ret

    RuntimeCache.get = __patched_func
    yield
    RuntimeCache.get = origin_func
