import random
import time

import torch

from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser


@torch.inference_mode()
def main(num_tokens: int,
         hidden_size: int,
         add_residual: bool,
         dtype: torch.dtype,
         seed: int = 0,
         do_profile: bool = False,
         num_warmup_iters: int = 5,
         num_iters: int = 100) -> None:
    random.seed(seed)
    torch.random.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
    torch.set_default_device("cuda")

    layer = RMSNorm(hidden_size).to(dtype=dtype)
    layer.weight.data.normal_(mean=1.0, std=0.1)
    scale = 1 / (2 * hidden_size)
    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
    x *= scale
    residual = torch.randn_like(x) * scale if add_residual else None

    def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
        torch.cuda.synchronize()
        if profile:
            torch.cuda.cudart().cudaProfilerStart()
        start_time = time.perf_counter()

        for _ in range(num_iters):
            layer(x, residual)
        torch.cuda.synchronize()

        end_time = time.perf_counter()
        if profile:
            torch.cuda.cudart().cudaProfilerStart()
        return (end_time - start_time) / num_iters

    # Warmup.
    print("Warming up...")
    run_benchmark = run_cuda_benchmark
    run_benchmark(num_iters=num_warmup_iters, profile=False)

    # Benchmark.
    if do_profile:
        latency = run_benchmark(num_iters=1, profile=True)
    else:
        latency = run_benchmark(num_iters=num_iters, profile=False)
    print(f"Kernel running time: {latency * 1000000:.3f} us")


if __name__ == '__main__':
    parser = FlexibleArgumentParser(
        description="Benchmark the layernorm kernel.")
    parser.add_argument("--num-tokens", type=int, default=4096)
    parser.add_argument("--hidden-size", type=int, default=8192)
    parser.add_argument("--add-residual", action="store_true")
    parser.add_argument("--dtype",
                        type=str,
                        choices=["half", "bfloat16", "float"],
                        default="half")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--profile", action="store_true")
    parser.add_argument("--num-warmup-iters", type=int, default=5)
    parser.add_argument("--num-iters",
                        type=int,
                        default=100,
                        help="Number of benchmark iterations. "
                        "If --profile is set, this number is ignored")

    args = parser.parse_args()
    print(args)

    main(num_tokens=args.num_tokens,
         hidden_size=args.hidden_size,
         add_residual=args.add_residual,
         dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
         seed=args.seed,
         do_profile=args.profile,
         num_warmup_iters=args.num_warmup_iters,
         num_iters=args.num_iters)
