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

import copy
from collections.abc import Callable
from itertools import product
from typing import Any

import torch

from vllm.config import VllmConfig
from vllm.utils.torch_utils import set_random_seed

from .common import Config, RankTensors, WeightTensors, make_modular_kernel
from .parallel_utils import ProcessGroupInfo, parallel_launch_with_config


def do_profile(
    fn: Callable,
    fn_kwargs: dict[Any, Any],
    pgi: ProcessGroupInfo,
    config: Config,
    num_warmups: int = 5,
):
    for _ in range(num_warmups):
        fn(**fn_kwargs)

    with torch.profiler.profile(
        activities=[
            torch.profiler.ProfilerActivity.CPU,
            torch.profiler.ProfilerActivity.CUDA,
        ],
        with_stack=True,
        record_shapes=True,
    ) as tprof:
        fn(**fn_kwargs)
        device = torch.accelerator.current_device_index()
        torch.accelerator.synchronize(device=device)

    # TODO (varun): Add a descriptive trace file name
    tprof.export_chrome_trace(
        f"{config.torch_trace_dir_path}/m{config.M}_{pgi.rank}_trace.json"
    )


def profile_modular_kernel(
    pgi: ProcessGroupInfo,
    vllm_config: VllmConfig,
    config: Config,
    weights: WeightTensors,
    rank_tensors: RankTensors,
) -> None:
    assert isinstance(config.Ms, int)
    assert isinstance(config.topks, int)

    # weights for rank
    rank_weights = weights.slice_weights(pgi.rank, config.num_local_experts)

    # make modular kernel
    mk = make_modular_kernel(config, vllm_config, weights)

    mk_kwargs = {
        "hidden_states": rank_tensors.hidden_states,
        "w1": rank_weights.w1,
        "w2": rank_weights.w2,
        "topk_weights": rank_tensors.topk_weights,
        "topk_ids": rank_tensors.topk_ids,
        "expert_map": rank_tensors.expert_map,
        "w1_scale": rank_weights.w1_scale,
        "w2_scale": rank_weights.w2_scale,
        "a1_scale": rank_tensors.hidden_states_scale,
        "global_num_experts": config.E,
        "apply_router_weight_on_input": config.topk == 1,
    }

    do_profile(mk.apply, mk_kwargs, pgi, config)


def rank_worker(
    pgi: ProcessGroupInfo,
    vllm_config: VllmConfig,
    cpu_group,
    config: Config,
    weights: WeightTensors,
):
    set_random_seed(pgi.rank)

    # get weights to this device
    weights.to_current_device()

    Ms = config.Ms
    assert isinstance(Ms, list)
    TOPKs = config.topks
    assert isinstance(TOPKs, list)

    for m, topk in product(Ms, TOPKs):
        print(f"Running m={m}, topk={topk} ...")
        # override m and topk
        cfgx = copy.deepcopy(config)
        cfgx.Ms = m
        cfgx.topks = topk

        # inputs for rank
        rank_tensors = RankTensors.make(cfgx, pgi)
        profile_modular_kernel(pgi, vllm_config, cfgx, weights, rank_tensors)


def run(config: Config):
    weights: WeightTensors = WeightTensors.make(config)
    vllm_config, env_dict = config.make_env_data()
    parallel_launch_with_config(
        config.world_size, rank_worker, vllm_config, env_dict, config, weights
    )


if __name__ == "__main__":
    from .cli_args import make_config, make_config_arg_parser

    parser = make_config_arg_parser(
        description=(
            "Run single prepare-finalize & fused-experts combination test"
            "Example : python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel "  # noqa: E501
            "--pf-type DeepEPLLPrepareAndFinalize --experts-type BatchedTritonExperts"
        )
    )
    args = parser.parse_args()
    assert args.torch_trace_dir_path is not None, (
        "Please pass in a directory to store torch traces"
    )
    config = make_config(args)

    run(config)
