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

e2e_time_param_dict_list = [{"batch": 2, "m": 1024, "k": 1600, "n": 6400, "has_c": True, "input_dtype": [torch.float16, torch.bfloat16]},
                            {"batch": 2, "m": 1024, "k": 2048, "n": 8192, "has_c": True, "input_dtype": [torch.float16, torch.bfloat16]},
                            {"batch": 2, "m": 1024, "k": 4096, "n": 11008, "has_c": True, "input_dtype": [torch.float16, torch.bfloat16]},
                            {"batch": 2, "m": 1024, "k": 5120, "n": 16384, "has_c": True, "input_dtype": [torch.float16, torch.bfloat16]},
                            {"batch": 2, "m": 1024, "k": 6144, "n": 24576, "has_c": True, "input_dtype": [torch.float16, torch.bfloat16]}]

def main():
  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()
  device = 'mlu'

  titles = ["batch", "m", "k", "n", "has_c", "input_dtype", "hardware_time(us)", "e2e_latency(us)"]
  contents = []
  for params_dict in e2e_time_param_dict_list:
    batch = params_dict["batch"]
    m = params_dict["m"]
    k = params_dict["k"]
    n = params_dict["n"]
    has_c = params_dict["has_c"]
    input_dtype_list = params_dict["input_dtype"]
    for dtype in input_dtype_list:
      if dtype == torch.bfloat16 and not torch_mlu.mlu.is_bf16_supported():
        continue
      a = torch.randn(batch, m, k).to(device).to(dtype)
      b = torch.randn(batch, n, k).to(device).to(dtype)
      c = None
      if has_c:
        c = torch.randn(batch, m, n).to(device).to(dtype)

      hardware_time, e2e_time = benchmark_forward(tmo.batch_matmul,
                                                  a,
                                                  b,
                                                  c,
                                                  1.0,
                                                  1.0,
                                                  repeats=args.repeat_times)
      content = [f"{batch}", f"{m}", f"{k}", f"{n}", f"{has_c}", f"{dtype}", 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()