from abc import ABC from abc import abstractmethod import torch from apex.multi_tensor_apply import multi_tensor_applier import amp_C from megatron import mpu from megatron import get_args def get_megatron_optimizer(optimizer): args = get_args() grad_scaler = DynamicGradScaler( initial_scale=2**32, min_scale=args.min_scale, growth_factor=2.0, backoff_factor=0.5, growth_interval=args.loss_scale_window, hysteresis=args.hysteresis) megatron_optimizer = FP16OptimizerWithFP16Params( optimizer, grad_scaler, args.clip_grad) return megatron_optimizer class MegatronGradScaler(ABC): def __init__(self, initial_scale): """Initialize scale value with the input initial scale.""" assert initial_scale > 0.0 self._scale = torch.cuda.FloatTensor([initial_scale]) @property def scale(self): return self._scale @property def inv_scale(self): return self._scale.double().reciprocal().float() @abstractmethod def update(self, found_inf): pass ''' @abstractmethod def state_dict(self): pass @abstractmethod def load_state_dict(self, state_dict): pass ''' class ConstantGradScaler(MegatronGradScaler): pass class DynamicGradScaler(MegatronGradScaler): def __init__(self, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis): """"Grad scaler with dynamic scale that gets adjusted during training.""" super(DynamicGradScaler, self).__init__(initial_scale) # Lower bound on the scale. assert min_scale > 0.0 assert min_scale <= initial_scale self.min_scale = torch.cuda.FloatTensor([min_scale]) # Growth and backoff factors for the scale. assert growth_factor > 1.0 self.growth_factor = torch.cuda.FloatTensor([growth_factor]) assert backoff_factor < 1.0 assert backoff_factor > 0.0 self.backoff_factor = torch.cuda.FloatTensor([backoff_factor]) # Interval over which if we don't see any inf/nan, # we will scale the grad scale by the growth factor. assert growth_interval > 0 self.growth_interval = growth_interval # Number of inf/nans we should see before scaling down # the grad scale by the backoff factor. assert hysteresis > 0 self.hysteresis = hysteresis # Trackers. self._growth_tracker = 0 self._hysteresis_tracker = self.hysteresis def update(self, found_inf): # If we have an inf/nan, growth tracker is set to 0 # and hysterisis tracker is reduced by 1. if found_inf: self._growth_tracker = 0 self._hysteresis_tracker -= 1 # Now if we are our of hysteresis count, scale down the loss. if self._hysteresis_tracker <= 0: self._scale = torch.max(self._scale * self.backoff_factor, self.min_scale) else: # If there is no nan/inf, increment the growth tracker. self._growth_tracker += 1 # If we have had enough consequitive intervals with no nan/inf: if self._growth_tracker == self.growth_interval: # Reset the tracker and hysteresis trackers, self._growth_tracker = 0 self._hysteresis_tracker = self.hysteresis # and scale up the loss scale. self._scale = self._scale * self.growth_factor def _zero_grad_group_helper(group, set_to_none): """Zero out the gradient for a group of parameters. Note: copied from torch.optim.optimizer.""" for param in group: if param.grad is not None: if set_to_none: param.grad = None else: if param.grad.grad_fn is not None: param.grad.detach_() else: param.grad.requires_grad_(False) param.grad.zero_() class MegatronOptimizer(ABC): def __init__(self, optimizer): """Input optimizer is the base optimizer for example Adam.""" self.optimizer = optimizer assert self.optimizer, 'no optimizer is provided.' @abstractmethod def zero_grad(self, set_to_none=True): pass @abstractmethod def get_loss_scale(self): pass def scale_loss(self, loss): """Simple scaling.""" return self.get_loss_scale() * loss @abstractmethod def step(self): pass ''' @abstractmethod def state_dict(self): pass @abstractmethod def load_state_dict(self, state_dict): pass ''' # Promote state so it can be retrieved or set via # "optimizer_instance.state" def _get_state(self): return self.optimizer.state def _set_state(self, value): self.optimizer.state = value state = property(_get_state, _set_state) # Promote param_groups so it can be retrieved or set via # "optimizer_instance.param_groups" # (for example, to adjust the learning rate) def _get_param_groups(self): return self.optimizer.param_groups def _set_param_groups(self, value): self.optimizer.param_groups = value param_groups = property(_get_param_groups, _set_param_groups) class FP16OptimizerWithFP16Params(MegatronOptimizer): def __init__(self, optimizer, grad_scaler, clip_grad): super(FP16OptimizerWithFP16Params, self).__init__(optimizer) self.grad_scaler = grad_scaler self.clip_grad = clip_grad # Tensor used to determine if a nan/if has happend. # Any non-zero value indicates inf/nan. self.found_inf = torch.cuda.FloatTensor([0.0]) # Dummy tensor needed for apex multi-apply tensor. self._dummy_overflow_buf = torch.cuda.IntTensor([0]) # ====================== # master parameter stuff # ====================== # Three groups of parameters: # fp16_groups: original fp16 parameters # fp32_from_fp16_groups: fp32 copy of fp16 parameters # fp32_from_fp32_groups: original fp32 parameters self.fp16_groups = [] self.fp32_from_fp16_groups = [] self.fp32_from_fp32_groups = [] # For all the groups in the original optimizer: for param_group in self.optimizer.param_groups: fp16_params_this_group = [] fp32_params_this_group = [] fp32_from_fp16_params_this_group = [] # For all the parameters in this group: for i, param in enumerate(param_group['params']): if param.requires_grad: # fp16 params: if param.type() == 'torch.cuda.HalfTensor': fp16_params_this_group.append(param) # Create a copy master_param = param.detach().clone().float() # Store grads master_param.requires_grad = True # Copy tensor model parallel attributes. master_param.tensor_model_parallel = param.tensor_model_parallel #mpu.copy_tensor_model_parallel_attributes(master_param, # param) # Replace the optimizer params with the new fp32 copy. param_group['params'][i] = master_param fp32_from_fp16_params_this_group.append(master_param) # Reset existing state dict key to the new master param. if param in self.optimizer.state: self.optimizer.state[master_param] \ = self.optimizer.state.pop(param) # fp32 params. elif param.type() == 'torch.cuda.FloatTensor': fp32_params_this_group.append(param) param_group['params'][i] = param else: raise TypeError("Wrapped parameters must be either " "torch.cuda.FloatTensor or " "torch.cuda.HalfTensor. " "Received {}".format(param.type())) self.fp16_groups.append(fp16_params_this_group) self.fp32_from_fp16_groups.append(fp32_from_fp16_params_this_group) self.fp32_from_fp32_groups.append(fp32_params_this_group) # Leverage state_dict() and load_state_dict() to # recast preexisting per-param state tensors self.optimizer.load_state_dict(self.optimizer.state_dict()) def zero_grad(self, set_to_none=True): """We only need to zero the model related parameters, i.e., fp16_groups & fp32_from_fp32_groups.""" for group in self.fp16_groups: _zero_grad_group_helper(group, set_to_none) for group in self.fp32_from_fp32_groups: _zero_grad_group_helper(group, set_to_none) def get_loss_scale(self): return self.grad_scaler.scale @torch.no_grad() def step(self): # ================================================== # Copy gradients from model params to master params. # ================================================== # This only needs to be done for the fp16 group. model_grads = [] master_grads = [] for model_group, master_group in zip(self.fp16_groups, self.fp32_from_fp16_groups): for model_param, master_param in zip(model_group, master_group): if model_param.grad is not None: if master_param.grad is None: master_param.grad = torch.empty_like(master_param) model_grads.append(model_param.grad) master_grads.append(master_param.grad) self._dummy_overflow_buf.fill_(0) # Scaling with factor `1.0` is equivalent to copy. multi_tensor_applier(amp_C.multi_tensor_scale, self._dummy_overflow_buf, [model_grads, master_grads], 1.0) # ============================== # Unscale and check for inf/nan. # ============================== # Append fp32 parameters. for master_group in self.fp32_from_fp32_groups: for master_param in master_group: if master_param.grad is not None: master_grads.append(master_param.grad) # Reset found inf. self.found_inf.fill_(0.0) # Unscale and set found inf/nan torch._amp_foreach_non_finite_check_and_unscale_( master_grads, self.found_inf, self.grad_scaler.inv_scale) # Update across all model parallel instances. torch.distributed.all_reduce(self.found_inf, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group()) # ================================== # We are done with scaling gradients # so we can update the loss scale. # ================================== found_inf_flag = (self.found_inf.item() > 0) self.grad_scaler.update(found_inf_flag) # ===================================== # If we found inf/nan, skip the update. # ===================================== if found_inf_flag: return False # ========================== # Clip the master gradients. # ========================== fp32_params = [] for param_group in self.optimizer.param_groups: for param in param_group['params']: fp32_params.append(param) mpu.clip_grad_norm(fp32_params, self.clip_grad) # =================== # Step the optimizer. # =================== self.optimizer.step() # ================================= # Update params from master params. # ================================= # Only needed for the fp16 params. model_data = [] master_data = [] for model_group, master_group in zip(self.fp16_groups, self.fp32_from_fp16_groups): for model_param, master_param in zip(model_group, master_group): model_data.append(model_param.data) master_data.append(master_param.data) self._dummy_overflow_buf.fill_(0) # Scaling with factor `1.0` is equivalent to copy. multi_tensor_applier(amp_C.multi_tensor_scale, self._dummy_overflow_buf, [master_data, model_data], 1.0) return True