# Adapted from https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py
import argparse
import json
import time
from datetime import datetime
from typing import Any, Dict, List, Tuple, TypedDict

import ray
import torch
import triton
from ray.experimental.tqdm_ray import tqdm
from transformers import AutoConfig

from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
    fused_moe,
    get_config_dtype_str,
    get_config_file_name,
    get_default_config,
    get_moe_configs,
)


class BenchmarkConfig(TypedDict):
    BLOCK_SIZE_M: int
    BLOCK_SIZE_N: int
    BLOCK_SIZE_K: int
    GROUP_SIZE_M: int
    num_warps: int
    num_stages: int


def benchmark_config(
    config: BenchmarkConfig,
    num_tokens: int,
    num_experts: int,
    shard_intermediate_size: int,
    hidden_size: int,
    topk: int,
    dtype: torch.dtype,
    use_fp8_w8a8: bool,
    use_int8_w8a16: bool,
    num_iters: int = 100,
) -> float:
    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
    if use_int8_w8a16:
        w1 = torch.randint(
            -127,
            127,
            (
                num_experts,
                shard_intermediate_size,
                hidden_size,
            ),
            dtype=torch.int8,
        )
        w2 = torch.randint(
            -127,
            127,
            (
                num_experts,
                hidden_size,
                shard_intermediate_size // 2,
            ),
            dtype=torch.int8,
        )
    else:
        w1 = torch.randn(
            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
        )
        w2 = torch.randn(
            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
        )
    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)

    w1_scale = None
    w2_scale = None
    a1_scale = None
    a2_scale = None
    if use_int8_w8a16:
        w1_scale = torch.randn(
            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
        )
        w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
    if use_fp8_w8a8:
        w1_scale = torch.randn(num_experts, dtype=torch.float32)
        w2_scale = torch.randn(num_experts, dtype=torch.float32)
        a1_scale = torch.randn(1, dtype=torch.float32)
        a2_scale = torch.randn(1, dtype=torch.float32)

        w1 = w1.to(torch.float8_e4m3fn)
        w2 = w2.to(torch.float8_e4m3fn)

    input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32)

    def prepare(i: int):
        input_gating.copy_(gating_output[i])

    def run():
        from sglang.srt.layers.moe.fused_moe_triton import override_config

        with override_config(config):
            fused_moe(
                x,
                w1,
                w2,
                input_gating,
                topk,
                renormalize=True,
                inplace=True,
                use_fp8_w8a8=use_fp8_w8a8,
                use_int8_w8a16=use_int8_w8a16,
                w1_scale=w1_scale,
                w2_scale=w2_scale,
                a1_scale=a1_scale,
                a2_scale=a2_scale,
            )

    # JIT compilation & warmup
    run()
    torch.cuda.synchronize()

    # Capture 10 invocations with CUDA graph
    graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(graph):
        for _ in range(10):
            run()
    torch.cuda.synchronize()

    # Warmup
    for _ in range(5):
        graph.replay()
    torch.cuda.synchronize()

    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)

    latencies: List[float] = []
    for i in range(num_iters):
        prepare(i)
        torch.cuda.synchronize()

        start_event.record()
        graph.replay()
        end_event.record()
        end_event.synchronize()
        latencies.append(start_event.elapsed_time(end_event))
    avg = sum(latencies) / (num_iters * 10) * 1000  # us
    graph.reset()
    return avg


def get_configs_compute_bound() -> List[Dict[str, int]]:
    # Reduced search space for faster tuning.
    # TODO(woosuk): Increase the search space and use a performance model to
    # prune the search space.
    configs: List[BenchmarkConfig] = []
    for num_stages in [2, 3, 4, 5]:
        for block_m in [16, 32, 64, 128, 256]:
            for block_k in [64, 128, 256]:
                for block_n in [32, 64, 128, 256]:
                    for num_warps in [4, 8]:
                        for group_size in [1, 16, 32, 64]:
                            configs.append(
                                {
                                    "BLOCK_SIZE_M": block_m,
                                    "BLOCK_SIZE_N": block_n,
                                    "BLOCK_SIZE_K": block_k,
                                    "GROUP_SIZE_M": group_size,
                                    "num_warps": num_warps,
                                    "num_stages": num_stages,
                                }
                            )
    return configs


@ray.remote(num_gpus=1)
class BenchmarkWorker:

    def __init__(self, seed: int) -> None:
        torch.set_default_device("cuda")
        torch.cuda.manual_seed_all(0)
        self.seed = seed

    def benchmark(
        self,
        num_tokens: int,
        num_experts: int,
        shard_intermediate_size: int,
        hidden_size: int,
        topk: int,
        dtype: torch.dtype,
        use_fp8_w8a8: bool,
        use_int8_w8a16: bool,
    ) -> Tuple[Dict[str, int], float]:
        torch.cuda.manual_seed_all(0)
        dtype_str = get_config_dtype_str(
            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
        )
        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
        # is the intermediate size after silu_and_mul.
        op_config = get_moe_configs(
            num_experts, shard_intermediate_size // 2, dtype_str
        )
        if op_config is None:
            config = get_default_config(
                num_tokens,
                num_experts,
                shard_intermediate_size,
                hidden_size,
                topk,
                dtype_str,
            )
        else:
            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
        kernel_time = benchmark_config(
            config,
            num_tokens,
            num_experts,
            shard_intermediate_size,
            hidden_size,
            topk,
            dtype,
            use_fp8_w8a8,
            use_int8_w8a16,
        )
        return config, kernel_time

    def tune(
        self,
        num_tokens: int,
        num_experts: int,
        shard_intermediate_size: int,
        hidden_size: int,
        topk: int,
        dtype: torch.dtype,
        use_fp8_w8a8: bool,
        use_int8_w8a16: bool,
        search_space: List[Dict[str, int]],
    ) -> Dict[str, int]:
        best_config = None
        best_time = float("inf")
        for config in tqdm(search_space):
            try:
                kernel_time = benchmark_config(
                    config,
                    num_tokens,
                    num_experts,
                    shard_intermediate_size,
                    hidden_size,
                    topk,
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
                    num_iters=10,
                )
            except triton.runtime.autotuner.OutOfResources:
                # Some configurations may be invalid and fail to compile.
                continue

            if kernel_time < best_time:
                best_time = kernel_time
                best_config = config
        now = datetime.now()
        print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
        assert best_config is not None
        return best_config


def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
    return {
        "BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
        "BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
        "BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
        "GROUP_SIZE_M": config["GROUP_SIZE_M"],
        "num_warps": config["num_warps"],
        "num_stages": config["num_stages"],
    }


def save_configs(
    configs: Dict[int, BenchmarkConfig],
    num_experts: int,
    shard_intermediate_size: int,
    hidden_size: int,
    topk: int,
    dtype: torch.dtype,
    use_fp8_w8a8: bool,
    use_int8_w8a16: bool,
) -> None:
    dtype_str = get_config_dtype_str(
        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
    )

    # NOTE(woosuk): The current naming convention uses w2.shape[2], which
    # is the intermediate size after silu_and_mul.
    filename = get_config_file_name(
        num_experts, shard_intermediate_size // 2, dtype_str
    )

    print(f"Writing best config to {filename}...")
    with open(filename, "w") as f:
        json.dump(configs, f, indent=4)
        f.write("\n")


def main(args: argparse.Namespace):
    print(args)

    config = AutoConfig.from_pretrained(args.model, trust_remote_code=True)
    if config.architectures[0] == "DbrxForCausalLM":
        E = config.ffn_config.moe_num_experts
        topk = config.ffn_config.moe_top_k
        intermediate_size = config.ffn_config.ffn_hidden_size
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] == "JambaForCausalLM":
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] == "Qwen2MoeForCausalLM":
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] == "DeepseekV2ForCausalLM":
        E = config.n_routed_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    else:
        # Default: Mixtral
        E = config.num_local_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
        shard_intermediate_size = 2 * intermediate_size // args.tp_size

    hidden_size = config.hidden_size
    dtype = config.torch_dtype
    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
    use_int8_w8a16 = args.dtype == "int8_w8a16"

    if args.batch_size is None:
        batch_sizes = [
            1,
            2,
            4,
            8,
            16,
            24,
            32,
            48,
            64,
            96,
            128,
            256,
            512,
            1024,
            1536,
            2048,
            3072,
            4096,
        ]
    else:
        batch_sizes = [args.batch_size]

    ray.init()
    num_gpus = int(ray.available_resources()["GPU"])
    workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]

    def _distribute(method: str, inputs: List[Any]) -> List[Any]:
        outputs = []
        worker_idx = 0
        for input_args in inputs:
            worker = workers[worker_idx]
            worker_method = getattr(worker, method)
            output = worker_method.remote(*input_args)
            outputs.append(output)
            worker_idx = (worker_idx + 1) % num_gpus
        return ray.get(outputs)

    if args.tune:
        search_space = get_configs_compute_bound()
        print(f"Start tuning over {len(search_space)} configurations...")

        start = time.time()
        configs = _distribute(
            "tune",
            [
                (
                    batch_size,
                    E,
                    shard_intermediate_size,
                    hidden_size,
                    topk,
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
                    search_space,
                )
                for batch_size in batch_sizes
            ],
        )
        best_configs = {
            M: sort_config(config) for M, config in zip(batch_sizes, configs)
        }
        save_configs(
            best_configs,
            E,
            shard_intermediate_size,
            hidden_size,
            topk,
            dtype,
            use_fp8_w8a8,
            use_int8_w8a16,
        )
        end = time.time()
        print(f"Tuning took {end - start:.2f} seconds")
    else:
        outputs = _distribute(
            "benchmark",
            [
                (
                    batch_size,
                    E,
                    shard_intermediate_size,
                    hidden_size,
                    topk,
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
                )
                for batch_size in batch_sizes
            ],
        )

        for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
            print(f"Batch size: {batch_size}, config: {config}")
            print(f"Kernel time: {kernel_time:.2f} us")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
    )
    parser.add_argument("--tp-size", "-tp", type=int, default=2)
    parser.add_argument(
        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--batch-size", type=int, required=False)
    parser.add_argument("--tune", action="store_true")
    args = parser.parse_args()

    main(args)
