content: # https://github.com/fpgaminer/GPTQ-triton
  """
  Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
  """
  
  import builtins
  import math
  import time
  from typing import Dict
  
  import triton
  
  
  class Autotuner(triton.KernelInterface):
      def __init__(
          self,
          fn,
          arg_names,
          configs,
          key,
          reset_to_zero,
          prune_configs_by: Dict = None,
          nearest_power_of_two: bool = False,
      ):
          """
          :param prune_configs_by: a dict of functions that are used to prune configs, fields:
                  'perf_model': performance model used to predicate running time with different configs, returns running time
                  'top_k': number of configs to bench
                  'prune_num_stages_by'(optional): a function used to prune num_stages. It take configs:List[Config] as its input, and returns pruned configs.
                  'nearest_power_of_two'(optional): whether to round key arguments to the nearest power of two when caching tuning results
          """
          if not configs:
              self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
          else:
              self.configs = configs
          self.key_idx = [arg_names.index(k) for k in key]
          self.nearest_power_of_two = nearest_power_of_two
          self.cache = {}
          # hook to reset all required tensor to zeros before relaunching a kernel
          self.hook = lambda args: 0
          if reset_to_zero is not None:
              self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
  
              def _hook(args):
                  for i in self.reset_idx:
                      args[i].zero_()
  
              self.hook = _hook
          self.arg_names = arg_names
          # prune configs
          if prune_configs_by:
              perf_model, top_k = (
                  prune_configs_by["perf_model"],
                  prune_configs_by["top_k"],
              )
              if "early_config_prune" in prune_configs_by:
                  early_config_prune = prune_configs_by["early_config_prune"]
          else:
              perf_model, top_k, early_config_prune = None, None, None
          self.perf_model, self.configs_top_k = perf_model, top_k
          self.early_config_prune = early_config_prune
          self.fn = fn
  
      def _bench(self, *args, config, **meta):
          # check for conflicts, i.e. meta-parameters both provided
          # as kwargs and by the autotuner
          conflicts = meta.keys() & config.kwargs.keys()
          if conflicts:
              raise ValueError(
                  f"Conflicting meta-parameters: {', '.join(conflicts)}."
                  " Make sure that you don't re-define auto-tuned symbols."
              )
          # augment meta-parameters with tunable ones
          current = dict(meta, **config.kwargs)
  
          def kernel_call():
              if config.pre_hook:
                  config.pre_hook(self.nargs)
              self.hook(args)
              self.fn.run(
                  *args,
                  num_warps=config.num_warps,
                  num_stages=config.num_stages,
                  **current,
              )
  
          try:
              # In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
              # PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
              return triton.testing.do_bench(kernel_call, quantiles=(0.5, 0.2, 0.8), rep=40)
          except triton.OutOfResources:
              return (float("inf"), float("inf"), float("inf"))
  
      def run(self, *args, **kwargs):
          self.nargs = dict(zip(self.arg_names, args))
          if len(self.configs) > 1:
              key = tuple(args[i] for i in self.key_idx)
  
              # This reduces the amount of autotuning by rounding the keys to the nearest power of two
              # In my testing this gives decent results, and greatly reduces the amount of tuning required
              if self.nearest_power_of_two:
                  key = tuple([2 ** int(math.log2(x) + 0.5) for x in key])
  
              if key not in self.cache:
                  # prune configs
                  pruned_configs = self.prune_configs(kwargs)
                  bench_start = time.time()
                  timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
                  bench_end = time.time()
                  self.bench_time = bench_end - bench_start
                  self.cache[key] = builtins.min(timings, key=timings.get)
                  self.hook(args)
                  self.configs_timings = timings
              config = self.cache[key]
          else:
              config = self.configs[0]
          self.best_config = config
          if config.pre_hook is not None:
              config.pre_hook(self.nargs)
          return self.fn.run(
              *args,
              num_warps=config.num_warps,
              num_stages=config.num_stages,
              **kwargs,
              **config.kwargs,
          )
  
      def prune_configs(self, kwargs):
          pruned_configs = self.configs
          if self.early_config_prune:
              pruned_configs = self.early_config_prune(self.configs, self.nargs)
          if self.perf_model:
              top_k = self.configs_top_k
              if isinstance(top_k, float) and top_k <= 1.0:
                  top_k = int(len(self.configs) * top_k)
              if len(pruned_configs) > top_k:
                  est_timing = {
                      config: self.perf_model(
                          **self.nargs,
                          **kwargs,
                          **config.kwargs,
                          num_stages=config.num_stages,
                          num_warps=config.num_warps,
                      )
                      for config in pruned_configs
                  }
                  pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
          return pruned_configs
  
      def warmup(self, *args, **kwargs):
          self.nargs = dict(zip(self.arg_names, args))
          for config in self.prune_configs(kwargs):
              self.fn.warmup(
                  *args,
                  num_warps=config.num_warps,
                  num_stages=config.num_stages,
                  **kwargs,
                  **config.kwargs,
              )
          self.nargs = None
  
  
  def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False):
      """
      Decorator for auto-tuning a :code:`triton.jit`'d function.
      .. highlight:: python
      .. code-block:: python
              @triton.autotune(configs=[
                      triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
                      triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
                      ],
                      key=['x_size'] # the two above configs will be evaluated anytime
                                                      # the value of x_size changes
              )
              @triton.jit
              def kernel(x_ptr, x_size, **META):
                      BLOCK_SIZE = META['BLOCK_SIZE']
      :note: When all the configurations are evaluated, the kernel will run multiple time.
                      This means that whatever value the kernel updates will be updated multiple times.
                      To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
                      reset the value of the provided tensor to `zero` before running any configuration.
      :param configs: a list of :code:`triton.Config` objects
      :type configs: list[triton.Config]
      :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
      :type key: list[str]
      :param prune_configs_by: a dict of functions that are used to prune configs, fields:
              'perf_model': performance model used to predicate running time with different configs, returns running time
              'top_k': number of configs to bench
              'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
      :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
      :type reset_to_zero: list[str]
      """
  
      def decorator(fn):
          return Autotuner(
              fn,
              fn.arg_names,
              configs,
              key,
              reset_to_zero,
              prune_configs_by,
              nearest_power_of_two,
          )
  
      return decorator
  
  
  def matmul248_kernel_config_pruner(configs, nargs):
      """
      The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
      """
      m = max(2 ** int(math.ceil(math.log2(nargs["M"]))), 16)
      n = max(2 ** int(math.ceil(math.log2(nargs["N"]))), 16)
      k = max(2 ** int(math.ceil(math.log2(nargs["K"]))), 16)
  
      used = set()
      for config in configs:
          block_size_m = min(m, config.kwargs["BLOCK_SIZE_M"])
          block_size_n = min(n, config.kwargs["BLOCK_SIZE_N"])
          block_size_k = min(k, config.kwargs["BLOCK_SIZE_K"])
          group_size_m = config.kwargs["GROUP_SIZE_M"]
  
          if (
              block_size_m,
              block_size_n,
              block_size_k,
              group_size_m,
              config.num_stages,
              config.num_warps,
          ) in used:
              continue
  
          used.add(
              (
                  block_size_m,
                  block_size_n,
                  block_size_k,
                  group_size_m,
                  config.num_stages,
                  config.num_warps,
              )
          )
          yield triton.Config(
              {
                  "BLOCK_SIZE_M": block_size_m,
                  "BLOCK_SIZE_N": block_size_n,
                  "BLOCK_SIZE_K": block_size_k,
                  "GROUP_SIZE_M": group_size_m,
              },
              num_stages=config.num_stages,
              num_warps=config.num_warps,
          )
  