"""
Benchmark the latency of a given model. It accepts arguments similar to those of launch_server.py.

# Usage (latency test) with dummy weights:
python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --load-format dummy

# Usage (correctness test):
python -m sglang.bench_latency --model-path TinyLlama/TinyLlama-1.1B-Chat-v0.4 --correct

### Reference output (of the correctness test above, can be gpu dependent):
prefill logits (first half) tensor([[-10.0312,  -9.5000,   0.8936,  ...,  -4.9414,  -3.2402,  -3.3633],
        [-10.0312,  -9.5000,   0.8936,  ...,  -4.9414,  -3.2402,  -3.3633],
        [ -9.1875, -10.2500,   2.7109,  ...,  -4.3359,  -4.0664,  -4.1328]],
       device='cuda:0', dtype=torch.float16)
prefill logits (final) tensor([[-8.3203, -7.1211,  3.3379,  ..., -4.9570, -4.1328, -3.4141],
        [-8.9062, -9.0156,  4.1445,  ..., -4.9922, -4.4961, -4.0742],
        [-9.6328, -9.0547,  4.0117,  ..., -5.3047, -4.7148, -4.4609]],
       device='cuda:0', dtype=torch.float16)
<s> The capital of France is.
The capital of the United States is Washington, D.C.

<s> The capital of the United Kindom is.
The capital of the United Kingdom is London.
The capital of the
<s> Today is a sunny day and I like go for a walk in the park.
I'm going to the park
"""

import argparse
import dataclasses
import logging
import multiprocessing
import time
from typing import Tuple

import jsonlines
import numpy as np
import torch
import torch.distributed as dist

from sglang.srt.hf_transformers_utils import get_tokenizer
from sglang.srt.managers.schedule_batch import Batch, ForwardMode, Req
from sglang.srt.model_config import ModelConfig
from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.sampling_params import SamplingParams
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import suppress_other_loggers


@dataclasses.dataclass
class BenchArgs:
    batch_size: Tuple[int] = (1,)
    input_len: int = 1024
    output_len: int = 4
    result_filename: str = ""
    correctness_test: bool = False
    # This is only used for correctness test
    cut_len: int = 4

    @staticmethod
    def add_cli_args(parser: argparse.ArgumentParser):
        parser.add_argument(
            "--batch-size", type=int, nargs="+", default=BenchArgs.batch_size
        )
        parser.add_argument("--input-len", type=int, default=BenchArgs.input_len)
        parser.add_argument("--output-len", type=int, default=BenchArgs.output_len)
        parser.add_argument(
            "--result-filename", type=str, default=BenchArgs.result_filename
        )
        parser.add_argument("--correctness-test", action="store_true")
        parser.add_argument("--cut-len", type=int, default=BenchArgs.cut_len)

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace):
        # use the default value's type to case the args into correct types.
        attrs = [(attr.name, type(attr.default)) for attr in dataclasses.fields(cls)]
        return cls(
            **{attr: attr_type(getattr(args, attr)) for attr, attr_type in attrs}
        )


def load_model(server_args, tp_rank):
    suppress_other_loggers()
    rank_print = print if tp_rank == 0 else lambda *args, **kwargs: None

    model_config = ModelConfig(path=server_args.model_path)
    model_runner = ModelRunner(
        model_config=model_config,
        mem_fraction_static=server_args.mem_fraction_static,
        gpu_id=tp_rank,
        tp_rank=tp_rank,
        tp_size=server_args.tp_size,
        nccl_port=28888,
        server_args=server_args,
    )
    rank_print(f"max_total_num_tokens={model_runner.max_total_num_tokens}")
    tokenizer = get_tokenizer(
        server_args.tokenizer_path,
        tokenizer_mode=server_args.tokenizer_mode,
        trust_remote_code=server_args.trust_remote_code,
    )
    if server_args.tp_size > 1:
        dist.barrier()
    return model_runner, tokenizer


def prepare_inputs_for_correctness_test(bench_args, tokenizer):
    prompts = [
        "The capital of France is",
        "The capital of the United Kindom is",
        "Today is a sunny day and I like",
    ]
    input_ids = [tokenizer.encode(p) for p in prompts]
    sampling_params = SamplingParams(
        temperature=0,
        max_new_tokens=BenchArgs.output_len,
    )

    reqs = []
    for i in range(len(prompts)):
        assert len(input_ids[i]) > bench_args.cut_len

        tmp_input_ids = input_ids[i][: bench_args.cut_len]
        req = Req(rid=i, origin_input_text=prompts[i], origin_input_ids=tmp_input_ids)
        req.prefix_indices = []
        req.sampling_params = sampling_params
        req.input_ids = req.origin_input_ids
        reqs.append(req)

    return input_ids, reqs


def prepare_extend_inputs_for_correctness_test(
    bench_args, input_ids, reqs, model_runner
):
    for i in range(len(reqs)):
        req = reqs[i]
        req.input_ids += input_ids[i][bench_args.cut_len :]
        req.prefix_indices = model_runner.req_to_token_pool.req_to_token[
            i, : bench_args.cut_len
        ]
    return reqs


def prepare_synthetic_inputs_for_latency_test(batch_size, input_len):
    input_ids = np.ones((batch_size, input_len), dtype=np.int32)
    sampling_params = SamplingParams(
        temperature=0,
        max_new_tokens=BenchArgs.output_len,
    )

    reqs = []
    for i in range(len(input_ids)):
        req = Req(rid=i, origin_input_text="", origin_input_ids=list(input_ids[i]))
        req.prefix_indices = []
        req.sampling_params = sampling_params
        req.input_ids = req.origin_input_ids
        reqs.append(req)

    return reqs


def extend(reqs, model_runner):
    batch = Batch.init_new(
        reqs=reqs,
        req_to_token_pool=model_runner.req_to_token_pool,
        token_to_kv_pool=model_runner.token_to_kv_pool,
        tree_cache=None,
    )
    batch.prepare_for_extend(model_runner.model_config.vocab_size, None)
    output = model_runner.forward(batch, ForwardMode.EXTEND)
    next_token_ids = batch.sample(output.next_token_logits)
    return next_token_ids, output.next_token_logits, batch


def decode(input_token_ids, batch, model_runner):
    batch.prepare_for_decode(input_token_ids.cpu().numpy())
    output = model_runner.forward(batch, ForwardMode.DECODE)
    next_token_ids = batch.sample(output.next_token_logits)
    return next_token_ids, output.next_token_logits


@torch.inference_mode()
def correctness_test(
    server_args,
    bench_args,
    tp_rank,
):
    rank_print = print if tp_rank == 0 else lambda *args, **kwargs: None

    # Load the model
    model_runner, tokenizer = load_model(server_args, tp_rank)

    # Prepare inputs
    input_ids, reqs = prepare_inputs_for_correctness_test(bench_args, tokenizer)

    if bench_args.cut_len > 0:
        # Prefill
        next_token_ids, next_token_logits, batch = extend(reqs, model_runner)
        rank_print("prefill logits (first half)", next_token_logits)

    # Prepare extend inputs
    reqs = prepare_extend_inputs_for_correctness_test(
        bench_args, input_ids, reqs, model_runner
    )

    # Extend
    next_token_ids, next_token_logits, batch = extend(reqs, model_runner)
    rank_print("prefill logits (final)", next_token_logits)

    # Decode
    output_ids = [input_ids[i] + [next_token_ids[i]] for i in range(len(input_ids))]
    for _ in range(bench_args.output_len):
        next_token_ids, _ = decode(next_token_ids, batch, model_runner)
        for i in range(len(reqs)):
            output_ids[i].append(next_token_ids[i])

    # Print
    for i in range(len(reqs)):
        rank_print(tokenizer.decode(output_ids[i]))


@torch.inference_mode()
def latency_test_run_once(
    model_runner, rank_print, reqs, batch_size, input_len, output_len
):

    # Clear the pools.
    model_runner.req_to_token_pool.clear()
    model_runner.token_to_kv_pool.clear()

    measurement_results = {
        "run_name": "before",
        "batch_size": batch_size,
        "input_len": input_len,
        "output_len": output_len,
    }

    tot_latency = 0

    # Prefill
    torch.cuda.synchronize()
    tic = time.time()
    next_token_ids, _, batch = extend(reqs, model_runner)
    torch.cuda.synchronize()
    prefill_latency = time.time() - tic
    tot_latency += prefill_latency
    throughput = input_len * batch_size / prefill_latency
    rank_print(
        f"Prefill. latency: {prefill_latency:6.5f} s, throughput: {throughput:9.2f} token/s"
    )
    measurement_results["prefill_latency"] = prefill_latency
    measurement_results["prefill_throughput"] = throughput

    # Decode
    for i in range(output_len):
        torch.cuda.synchronize()
        tic = time.time()
        next_token_ids, _ = decode(next_token_ids, batch, model_runner)
        torch.cuda.synchronize()
        latency = time.time() - tic
        tot_latency += latency
        throughput = batch_size / latency
        if i < 5:
            rank_print(
                f"Decode.  latency: {latency:6.5f} s, throughput: {throughput:9.2f} token/s"
            )
    avg_decode_latency = (tot_latency - prefill_latency) / output_len
    avg_decode_throughput = batch_size / avg_decode_latency
    rank_print(
        f"Decode.  avg latency: {avg_decode_latency:6.5f} s, avg throughput: {avg_decode_throughput:9.2f} token/s"
    )
    measurement_results["avg_decode_latency"] = avg_decode_latency
    measurement_results["avg_decode_throughput"] = avg_decode_throughput

    throughput = (input_len + output_len) * batch_size / tot_latency
    rank_print(
        f"Total. latency: {tot_latency:6.3f} s, throughput: {throughput:9.2f} token/s"
    )
    measurement_results["total_latency"] = tot_latency
    measurement_results["total_throughput"] = throughput
    return measurement_results


def latency_test(
    server_args,
    bench_args,
    tp_rank,
):
    rank_print = print if tp_rank == 0 else lambda *args, **kwargs: None

    # Load the model
    model_runner, tokenizer = load_model(server_args, tp_rank)
    rank_print(
        f"max_batch_size={model_runner.max_total_num_tokens // (bench_args.input_len + bench_args.output_len)}"
    )

    # To make this PR easier to review, for now, only do the first element in batch_size tuple.
    bench_args.batch_size = bench_args.batch_size[0]

    # Prepare inputs
    reqs = prepare_synthetic_inputs_for_latency_test(
        bench_args.batch_size, bench_args.input_len
    )

    # Warm up
    latency_test_run_once(
        model_runner, rank_print, reqs, bench_args.batch_size, bench_args.input_len, 4
    )

    # Run again
    result_list = []
    result_list.append(
        latency_test_run_once(
            model_runner,
            rank_print,
            reqs,
            bench_args.batch_size,
            bench_args.input_len,
            bench_args.output_len,
        )
    )

    # Write results in jsonlines format.
    if bench_args.result_filename:
        with jsonlines.open(bench_args.result_filename, "a") as f:
            f.write_all(result_list)


def main(server_args, bench_args):
    print(bench_args)

    if bench_args.correctness_test:
        work_func = correctness_test
    else:
        work_func = latency_test

    if server_args.tp_size == 1:
        work_func(server_args, bench_args, 0)
    else:
        workers = []
        for tp_rank in range(server_args.tp_size):
            proc = multiprocessing.Process(
                target=work_func,
                args=(
                    server_args,
                    bench_args,
                    tp_rank,
                ),
            )
            proc.start()
            workers.append(proc)

        for proc in workers:
            proc.join()

        proc.terminate()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    ServerArgs.add_cli_args(parser)
    BenchArgs.add_cli_args(parser)
    args = parser.parse_args()

    server_args = ServerArgs.from_cli_args(args)
    bench_args = BenchArgs.from_cli_args(args)

    logging.basicConfig(
        level=getattr(logging, server_args.log_level.upper()),
        format="%(message)s",
    )

    main(server_args, bench_args)
