import torch
import torch_mlu
import torch_mlu_ops as tmo
from common import benchmark_forward, save_to_csv
import argparse
from tabulate import tabulate
import os
import random

e2e_time_param_dict_list = [{"num_tokens": 1024, "num_block": 500, "block_size": 6, "head_num_q": 32,
                             "head_num_kv": 32, "head_size": 128, "quantize": True, "input_dtype": [torch.float16, torch.bfloat16]},
                            {"num_tokens": 1024, "num_block": 500, "block_size": 6, "head_num_q": 32,
                             "head_num_kv": 32, "head_size": 128, "quantize": False, "input_dtype": [torch.float16, torch.bfloat16]}
                           ]

def main():
  if 'MLU3' in torch.mlu.get_device_name():
    exit()
  parser = argparse.ArgumentParser()
  parser.add_argument('--repeat_times', type=int, default=10, help='repeat times for testing')
  parser.add_argument('--csv', action='store_true', help='write the report data to csv')
  parser.add_argument('-o', type=str, help='specify the output folder name under --csv mode')

  args = parser.parse_args()
  titles = ["num_tokens", "num_block", "block_size", "head_num_q", "head_num_kv", "head_size", "input_dytpe", "quantize", "hardware_time(us)", "e2e_latency(us)"]
  contents = []
  for params_dict in e2e_time_param_dict_list:
    num_tokens = params_dict["num_tokens"]
    num_blocks = params_dict["num_block"]
    block_size = params_dict["block_size"]
    head_num_q = params_dict["head_num_q"]
    head_num_kv = params_dict["head_num_kv"]
    head_size = params_dict["head_size"]
    quantize = params_dict["quantize"]
    input_dtype_list = params_dict["input_dtype"]
    for dtype in input_dtype_list:
      qkv = torch.randn(num_tokens, head_num_q + 2 * head_num_kv, head_size, dtype=dtype).mlu()
      key = qkv[:, head_num_q : head_num_q + head_num_kv, :]
      value = qkv[:, head_num_q + head_num_kv : head_num_q + 2 * head_num_kv, :]

      key_cache   = torch.randn(num_blocks, head_num_kv, block_size, head_size, dtype=dtype).mlu()
      value_cache = torch.randn(num_blocks, head_num_kv, block_size, head_size, dtype=dtype).mlu()

      num_slots = num_blocks * block_size
      slot_mapping = random.sample(range(num_slots), num_tokens)
      slot_mapping = torch.tensor(slot_mapping, dtype=torch.int).mlu()
      slot_mapping[-1] = -1
      if not quantize:
        hardware_time, e2e_time = benchmark_forward(tmo.reshape_paged_cache,
                                                    key, value,
                                                    key_cache, value_cache,
                                                    slot_mapping,
                                                    repeats=args.repeat_times)
      else:
        k_cache_quant_scale = torch.randn(num_blocks, head_num_kv, block_size).to('mlu').to(torch.float32)
        v_cache_quant_scale = torch.randn(num_blocks, head_num_kv, block_size).to('mlu').to(torch.float32)
        hardware_time, e2e_time = benchmark_forward(tmo.quant_to_paged_cache,
                                                    key, value,
                                                    key_cache, value_cache,
                                                    k_cache_quant_scale,
                                                    v_cache_quant_scale,
                                                    slot_mapping,
                                                    repeats=args.repeat_times)

      content = [f"{num_tokens}", f"{num_blocks}", f"{block_size}", f"{head_num_q}",  f"{head_num_kv}", f"{head_size}", f"{dtype}", f"{quantize}", f"{hardware_time}", f"{e2e_time}"]
      contents.append(content)
  table = [titles] + contents
  print(tabulate(table, headers="firstrow", tablefmt="grid"))

  if args.csv:
    current_file_path = __file__
    _, file_name = os.path.split(current_file_path)
    save_to_csv(table, args.o, file_name)

if __name__=="__main__":
  main()