hunk.code_window:       env:
           LD_PRELOAD: /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4
         run: |
           export PYTHONDONTWRITEBYTECODE=1
           pytest -vv --durations=0 ./tests
  
hunk.labels: keep keep keep keep replace
hunk.after_edit:         pytest -vv --forked --durations=0 ./tests
hunk.file_path: .github/workflows/tests.yml
hunk.type: replace
file: """ Adafactor Optimizer
  
  Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
  
  Original header/copyright below.
  
  """
  # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  import torch
  import math
  
  
  class Adafactor(torch.optim.Optimizer):
      """Implements Adafactor algorithm.
      This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`
      (see https://arxiv.org/abs/1804.04235)
  
      Note that this optimizer internally adjusts the learning rate depending on the
      *scale_parameter*, *relative_step* and *warmup_init* options.
  
      To use a manual (external) learning rate schedule you should set `scale_parameter=False` and
      `relative_step=False`.
  
      Arguments:
          params (iterable): iterable of parameters to optimize or dicts defining parameter groups
          lr (float, optional): external learning rate (default: None)
          eps (tuple[float, float]): regularization constants for square gradient
              and parameter scale respectively (default: (1e-30, 1e-3))
          clip_threshold (float): threshold of root mean square of final gradient update (default: 1.0)
          decay_rate (float): coefficient used to compute running averages of square gradient (default: -0.8)
          beta1 (float): coefficient used for computing running averages of gradient (default: None)
          weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
          scale_parameter (bool): if True, learning rate is scaled by root mean square of parameter (default: True)
          warmup_init (bool): time-dependent learning rate computation depends on
              whether warm-up initialization is being used (default: False)
      """
  
      def __init__(self, params, lr=None, eps=1e-30, eps_scale=1e-3, clip_threshold=1.0,
                   decay_rate=-0.8, betas=None, weight_decay=0.0, scale_parameter=True, warmup_init=False):
          relative_step = not lr
          if warmup_init and not relative_step:
              raise ValueError('warmup_init requires relative_step=True')
  
          beta1 = None if betas is None else betas[0]   # make it compat with standard betas arg
          defaults = dict(lr=lr, eps=eps, eps_scale=eps_scale, clip_threshold=clip_threshold, decay_rate=decay_rate,
                          beta1=beta1, weight_decay=weight_decay, scale_parameter=scale_parameter,
                          relative_step=relative_step, warmup_init=warmup_init)
          super(Adafactor, self).__init__(params, defaults)
  
      @staticmethod
      def _get_lr(param_group, param_state):
          if param_group['relative_step']:
              min_step = 1e-6 * param_state['step'] if param_group['warmup_init'] else 1e-2
              lr_t = min(min_step, 1.0 / math.sqrt(param_state['step']))
              param_scale = 1.0
              if param_group['scale_parameter']:
                  param_scale = max(param_group['eps_scale'], param_state['RMS'])
              param_group['lr'] = lr_t * param_scale
          return param_group['lr']
  
      @staticmethod
      def _get_options(param_group, param_shape):
          factored = len(param_shape) >= 2
          use_first_moment = param_group['beta1'] is not None
          return factored, use_first_moment
  
      @staticmethod
      def _rms(tensor):
          return tensor.norm(2) / (tensor.numel() ** 0.5)
  
      def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col):
          r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1)
          c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
          return torch.mul(r_factor, c_factor)
  
      @torch.no_grad()
      def step(self, closure=None):
          """Performs a single optimization step.
          Arguments:
              closure (callable, optional): A closure that reevaluates the model and returns the loss.
          """
          loss = None
          if closure is not None:
              with torch.enable_grad():
                  loss = closure()
  
          for group in self.param_groups:
              for p in group['params']:
                  if p.grad is None:
                      continue
                  grad = p.grad
                  if grad.dtype in {torch.float16, torch.bfloat16}:
                      grad = grad.float()
                  if grad.is_sparse:
                      raise RuntimeError('Adafactor does not support sparse gradients.')
  
                  state = self.state[p]
  
                  factored, use_first_moment = self._get_options(group, grad.shape)
                  # State Initialization
                  if len(state) == 0:
                      state['step'] = 0
  
                      if use_first_moment:
                          # Exponential moving average of gradient values
                          state['exp_avg'] = torch.zeros_like(grad)
                      if factored:
                          state['exp_avg_sq_row'] = torch.zeros(grad.shape[:-1]).to(grad)
                          state['exp_avg_sq_col'] = torch.zeros(grad.shape[:-2] + grad.shape[-1:]).to(grad)
                      else:
                          state['exp_avg_sq'] = torch.zeros_like(grad)
  
                      state['RMS'] = 0
                  else:
                      if use_first_moment:
                          state['exp_avg'] = state['exp_avg'].to(grad)
                      if factored:
                          state['exp_avg_sq_row'] = state['exp_avg_sq_row'].to(grad)
                          state['exp_avg_sq_col'] = state['exp_avg_sq_col'].to(grad)
                      else:
                          state['exp_avg_sq'] = state['exp_avg_sq'].to(grad)
  
                  p_fp32 = p
                  if p.dtype in {torch.float16, torch.bfloat16}:
                      p_fp32 = p_fp32.float()
  
                  state['step'] += 1
                  state['RMS'] = self._rms(p_fp32)
                  lr_t = self._get_lr(group, state)
  
                  beta2t = 1.0 - math.pow(state['step'], group['decay_rate'])
                  update = grad ** 2 + group['eps']
                  if factored:
                      exp_avg_sq_row = state['exp_avg_sq_row']
                      exp_avg_sq_col = state['exp_avg_sq_col']
  
                      exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t)
                      exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t)
  
                      # Approximation of exponential moving average of square of gradient
                      update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
                      update.mul_(grad)
                  else:
                      exp_avg_sq = state['exp_avg_sq']
  
                      exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t)
                      update = exp_avg_sq.rsqrt().mul_(grad)
  
                  update.div_((self._rms(update) / group['clip_threshold']).clamp_(min=1.0))
                  update.mul_(lr_t)
  
                  if use_first_moment:
                      exp_avg = state['exp_avg']
                      exp_avg.mul_(group['beta1']).add_(update, alpha=1 - group['beta1'])
                      update = exp_avg
  
                  if group['weight_decay'] != 0:
                      p_fp32.add_(p_fp32, alpha=-group['weight_decay'] * lr_t)
  
                  p_fp32.add_(-update)
                  if p.dtype in {torch.float16, torch.bfloat16}:
                      p.copy_(p_fp32)
  
          return loss
  