# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Megatron optimizer.""" 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 get_timers from megatron import mpu from megatron import print_rank_0 from .clip_grads import clip_grad_norm_fp32 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.' def clip_grad_norm(self, clip_grad): params = [] for param_group in self.optimizer.param_groups: for param in param_group['params']: params.append(param) clip_grad_norm_fp32(params, clip_grad) @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. mpu.copy_tensor_model_parallel_attributes(master_param, param) if hasattr(param, 'shared'): master_param.shared = param.shared # 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 def _copy_model_grads_to_master_grads(self): # 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.data) master_grads.append(master_param.grad.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, [model_grads, master_grads], 1.0) def _unscale_master_grads_and_check_for_nan(self): master_grads = [] # fp32 params fromm fp16 ones. for master_group in self.fp32_from_fp16_groups: for master_param in master_group: if master_param.grad is not None: master_grads.append(master_param.grad.data) # 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.data) # 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()) # Check for nan. found_inf_flag = (self.found_inf.item() > 0) return found_inf_flag def _copy_master_params_to_model_params(self): # 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) @torch.no_grad() def step(self): timers = get_timers() # ================================================== # Copy gradients from model params to master params. # ================================================== timers('optimizer-copy-to-master-grad').start() self._copy_model_grads_to_master_grads() timers('optimizer-copy-to-master-grad').stop() # ============================== # Unscale and check for inf/nan. # ============================== timers('optimizer-unscale-and-check-inf').start() found_inf_flag = self._unscale_master_grads_and_check_for_nan() timers('optimizer-unscale-and-check-inf').stop() # ================================== # We are done with scaling gradients # so we can update the loss scale. # ================================== 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. # ========================== timers('optimizer-clip-master-grad').start() self.clip_grad_norm(self.clip_grad) timers('optimizer-clip-master-grad').stop() # =================== # Step the optimizer. # =================== self.optimizer.step() # ================================= # Update params from master params. # ================================= timers('optimizer-copy-master-to-model-params').start() self._copy_master_params_to_model_params() timers('optimizer-copy-master-to-model-params').stop() # ================== # Successful update. # ================== return True def state_dict(self): state_dict = {} state_dict['optimizer'] = self.optimizer.state_dict() state_dict['grad_scaler'] = self.grad_scaler.state_dict() state_dict['fp32_from_fp16_params'] = self.fp32_from_fp16_groups return state_dict def load_state_dict(self, state_dict): # Optimizer. optimizer_key = 'optimizer' if optimizer_key not in state_dict: optimizer_key = 'optimizer_state_dict' print_rank_0('***WARNING*** loading optimizer from ' 'an old checkpoint ...') self.optimizer.load_state_dict(state_dict[optimizer_key]) # Grad scaler. if 'grad_scaler' not in state_dict: print_rank_0('***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...') else: self.grad_scaler.load_state_dict(state_dict['grad_scaler']) # Copy data for the master params. fp32_from_fp16_params_key = 'fp32_from_fp16_params' if fp32_from_fp16_params_key not in state_dict: fp32_from_fp16_params_key = 'fp32_from_fp16' for current_group, saved_group in zip( self.fp32_from_fp16_groups, state_dict[fp32_from_fp16_params_key]): for current_param, saved_param in zip(current_group, saved_group): current_param.data.copy_(saved_param.data) class FP32Optimizer(MegatronOptimizer): def __init__(self, optimizer, clip_grad): super(FP32Optimizer, self).__init__(optimizer) self.clip_grad = clip_grad self._scale = torch.cuda.FloatTensor([1.0]) def zero_grad(self, set_to_none=True): """Copied from torch.optim.optimizer""" for group in self.optimizer.param_groups: _zero_grad_group_helper(group['params'], set_to_none) def get_loss_scale(self): """FP32 optimizer does not do any scaling.""" return self._scale @torch.no_grad() def step(self): """Clip gradients (if needed) and step the base optimizer. Always return successful since there is no overflow.""" # Clip gradients. if self.clip_grad > 0.0: self.clip_grad_norm(self.clip_grad) # Update parameters. self.optimizer.step() # No overflow for FP32 optimizer. return True def state_dict(self): return self.optimizer.state_dict() def load_state_dict(self, state_dict): self.optimizer.load_state_dict(state_dict)