# SPDX-License-Identifier: MIT
# Copyright (C) 2025, Advanced Micro Devices, Inc. All rights reserved.

import torch
from aiter.test_common import (
    checkAllclose,
    benchmark,
    run_perftest,
)
import aiter
from aiter import dtypes
from aiter.ops.topk_plain import topk_plain
import pandas as pd

torch.set_default_device("cuda")
torch.set_printoptions(sci_mode=False)


@benchmark()
def test_topk(
    batch_size,
    hiddensize,
    topk,
    largest,
    dtype,
):
    output = torch.randn((batch_size, hiddensize), dtype=dtype)
    device = output.device

    topk_ids = torch.zeros((batch_size, topk), dtype=dtypes.i32, device=device)
    topk_value = torch.zeros((batch_size, topk), dtype=dtype, device=device)

    x = torch.arange(hiddensize, dtype=dtype).repeat(batch_size, 1)
    
    for b in range(batch_size):
        x[b] = x[b, torch.randperm(hiddensize)]

    (ref_value, ref_index), us_ref = run_perftest(
        torch.topk,
        x,
        topk,
        largest=largest,
        num_iters=100,
        num_warmup=20,
    )

    id_ref, _ref = torch.sort(ref_index)

    # Try Triton, but handle resource errors gracefully
    # try:
    #     (res_triton_value, res_triton_index), us_triton = run_perftest(
    #         triton_topk,
    #         x,
    #         topk,
    #         largest=largest,
    #         num_iters=1000,
    #         num_warmup=100,
    #     )

    #     id_triton, _triton = torch.sort(res_triton_index)
    #     checkAllclose(
    #         ref_value.gather(1, _ref),
    #         res_triton_value.gather(1, _triton),
    #         msg="topk_values [golden vs triton]",
    #     )
    #     checkAllclose(
    #         id_ref,
    #         id_triton,
    #         msg=(
    #             f"topk_ids Performance Comparison:\n"
    #             f"  {'Method':<10} {'Time (us)':>12}\n"
    #             f"  {'-'*10} {'-'*12}\n"
    #             f"  {'golden':<10} {us_ref:>12.2f}\n"
    #             f"  {'triton':<10} {us_triton:>12.2f}\n"
    #         ),
    #     )
    # except Exception as e:
    #     print(f"Triton failed: {e}")
    #     print("Setting triton time to 0 and continuing...")
    #     us_triton = 0.0

    # TODO: uncomment this when the triton topk return in a resonalbe execution time
    us_triton = 0.0

    _, us_aiter = run_perftest(
        topk_plain,
        x,
        topk_ids,
        topk_value,
        topk,
        largest,
        torch.tensor(
            [], dtype=torch.int32, device=device
        ),  # rowStarts - empty int32 tensor
        torch.tensor(
            [], dtype=torch.int32, device=device
        ),  # rowEnds - empty int32 tensor
        -1,  # stride0
        1,  # stride1
        num_iters=100,
        num_warmup=20,
    )

    id_aiter, _aiter = torch.sort(topk_ids.to(torch.long))

    # Skip for float16 as it would has duplicates in topk_ids
    if dtype != torch.float16 and dtype != torch.bfloat16:
        # TODO: uncomment this when the aiter topk supports value return
        # err = checkAllclose(
        #     ref_value.gather(1, _ref),
        #     topk_value.gather(1, _aiter),
        #     msg="topk_values [golden vs aiter]",
        # )
        err = checkAllclose(
            id_ref,
            id_aiter,
            msg=(
                f"topk_ids Performance Comparison:\n"
                f"  {'Method':<10} {'Time (us)':>12}\n"
                f"  {'-'*10} {'-'*12}\n"
                f"  {'golden':<10} {us_ref:>12.2f}\n"
                f"  {'triton':<10} {us_triton:>12.2f}\n"
                f"  {'aiter':<10} {us_aiter:>12.2f}\n"
            ),
        )
    else:
        err = checkAllclose(
            ref_value,
            topk_value,
            msg=(
                f"topk_values [golden vs aiter]:\n"
                f"  {'Method':<10} {'Time (us)':>12}\n"
                f"  {'-'*10} {'-'*12}\n"
                f"  {'golden':<10} {us_ref:>12.2f}\n"
                f"  {'triton':<10} {us_triton:>12.2f}\n"
                f"  {'aiter':<10} {us_aiter:>12.2f}\n"
            ),
        )

    return {
        "err": err,
        "us_aiter": us_aiter,
        "us_torch": us_ref,
        "us_triton": us_triton,
    }


# BATCH_SIZES = [100, 1000, 10000, 32679]
# HIDDENSIZES = [10000, 100000]
# topk = 64
# BATCH_SIZES = [3072, 3072, 3072]
# HIDDENSIZES = [3072, 4096, 8192]
BATCH_SIZES = [3072]
HIDDENSIZES = [3072, 4096, 8192, 16384, 32768, 65536, 131072]
# HIDDENSIZES = [32768]
TOPKS = [2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1]
largest = True

df = []
for batch_size in BATCH_SIZES:
    for hiddensize in HIDDENSIZES:
        for topk in TOPKS:
            print(f"\n{'='*60}")
            print(
                f"Testing: batch_size={batch_size}, hiddensize={hiddensize}, topk={topk}"
            )
            print(f"{'='*60}")
            ret = test_topk(
                batch_size,
                hiddensize,
                topk,
                largest,
                dtypes.fp32,
            )
            df.append(
                {
                    "batch_size": batch_size,
                    "hiddensize": hiddensize,
                    "topk": topk,
                    "error": ret["err"],
                    "time_us (aiter)": ret["us_aiter"],
                    "time_us (torch)": ret["us_torch"],
                    "time_us (triton)": ret["us_triton"],
                }
            )

df = pd.DataFrame(df)

# Add speedup columns
df["speedup (aiter vs torch)"] = df["time_us (torch)"] / df["time_us (aiter)"]
df["speedup (aiter vs triton)"] = df["time_us (triton)"] / df["time_us (aiter)"]

df.to_csv("topk_plain.csv",index=False)
df_md = df.to_markdown(index=False)
aiter.logger.info("topk_plain summary (markdown):\n%s", df_md)
