import torch import torch.distributed as dist from enum import Enum from torch.optim import Optimizer from colossalai.nn.parallel.data_parallel import ZeroDDP from typing import Dict from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer from colossalai.utils import get_current_device, disposable class OptimState(Enum): SCALED = 0 UNSCALED = 1 class ZeroOptimizer(ColossalaiOptimizer): """A wrapper for optimizer. ``ZeroDDP`` and ``ZeroOptimizer`` implement Zero Redundancy Optimizer (ZeRO state-3). Note: You must use ``ZeroDDP`` with ``ZeroOptimizer``. Note: Make sure you set ``placement_policy`` of ``GeminiManager`` to `"auto"`, if you set ``gpu_margin_mem_ratio > 0``. Args: optim (Optimizer): An Optimizer instance. module (ZeroDDP): A ``ZeroDDP`` instance. gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward) which will be used when using hybrid CPU optimizer. This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto". Defaults to 0.0. initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. """ def __init__(self, optim: Optimizer, module: ZeroDDP, gpu_margin_mem_ratio: float = 0.0, initial_scale: float = 2**32, min_scale: float = 1, growth_factor: float = 2, backoff_factor: float = 0.5, growth_interval: int = 1000, hysteresis: int = 2, max_scale: float = 2**32): super().__init__(optim) assert isinstance(module, ZeroDDP) self.module = module self.gemini_manager = module.gemini_manager self.chunk_manager = self.gemini_manager.chunk_manager self.optim_state = OptimState.UNSCALED self.fp16_param_to_fp32_param: Dict[torch.Tensor, torch.Tensor] = {} for p, fp32_p in zip(module.parameters(), module.fp32_params): self.fp16_param_to_fp32_param[p] = fp32_p # Grad scaler self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale, min_scale=min_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, hysteresis=hysteresis, max_scale=max_scale) self._found_overflow: torch.Tensor = torch.zeros(1, dtype=torch.int64, device=torch.cuda.current_device()) self._logger = get_dist_logger() self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio) assert 0.0 <= self.gpu_margin_mem_ratio <= 1.0, f'gpu_margin_mem_ratio must >=0.0 and <=1.0' # Only move fp32 shards from CPU to GPU when user allows and inner optimizer is valid # Inner optimizer must support optimizing hybrid (CPU and CUDA) tensors, # and it must set `num_fp32_shards_per_param` correctly self._should_move_fp32_params_h2d: bool = self.gemini_manager.is_cuda_margin_mem_avail and self.gpu_margin_mem_ratio > 0.0 and getattr( optim, 'num_fp32_shards_per_param', 0) >= 2 if self.gpu_margin_mem_ratio > 0.0 and not self.gemini_manager.is_cuda_margin_mem_avail: self._logger.warning(f'gpu_margin_mem_ratio is meaningless when placement_policy is not "auto"', ranks=[0]) self._register_states = disposable(self._register_states_) def _update_params_ptr(self): for group in self.optim.param_groups: for p in group['params']: if not self.module.chunk_manager.get_chunk(p).is_empty: p.data = self.fp16_param_to_fp32_param[p] else: assert p.grad is None def _update_fp16_params(self): self.module.chunk_manager.copy_chunk_group('fp16_param', 'fp32_param') def _check_overflow(self): # clear previous overflow record self._found_overflow.fill_(self.module.overflow_counter) # all-reduce across global group dist.all_reduce(self._found_overflow) return self._found_overflow.item() > 0 def _unscale_grads(self): assert self.optim_state == OptimState.SCALED for group in self.optim.param_groups: for p in group['params']: if p.grad is not None: p.grad.data.div_(self.loss_scale) self.optim_state = OptimState.UNSCALED @property def loss_scale(self): return self.grad_scaler.scale.item() def zero_grad(self, *args, **kwargs): self.module.overflow_counter = 0 return self.optim.zero_grad(set_to_none=True) def step(self, *args, **kwargs): self._maybe_move_fp32_params() # unscale grads if scaled if self.optim_state == OptimState.SCALED: self._unscale_grads() found_inf = self._check_overflow() self.grad_scaler.update(found_inf) if found_inf: self._logger.info(f'Found overflow. Skip step') self.zero_grad() self._update_fp16_params() return self._update_params_ptr() ret = self.optim.step(*args, **kwargs) self._register_states() self._update_fp16_params() return ret def clip_grad_norm(self, model: torch.nn.Module, max_norm: float): if self.optim_state == OptimState.SCALED: self._unscale_grads() return super().clip_grad_norm(model, max_norm) def backward(self, loss: torch.Tensor): loss = self.loss_scale * loss self.optim_state = OptimState.SCALED self.module.backward(loss) def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor): self.module.backward_by_grad(tensor, grad) def _maybe_move_fp32_params(self): if self._should_move_fp32_params_h2d: self._should_move_fp32_params_h2d = False available_cuda_margin_mem = self.gemini_manager.cuda_margin_mem * self.gpu_margin_mem_ratio fp32_params_available_cuda_margin_mem = available_cuda_margin_mem / self.optim.num_fp32_shards_per_param fp32_params_used_cuda_margin_mem = 0 for fp16_param_chunk, fp32_param_chunk in zip(self.chunk_manager.chunk_groups['fp16_param'], self.chunk_manager.chunk_groups['fp32_param']): if fp32_param_chunk.is_empty: continue if fp32_params_used_cuda_margin_mem + fp32_param_chunk.mem < fp32_params_available_cuda_margin_mem: self.chunk_manager.move_chunk(fp32_param_chunk, get_current_device()) # stores grad now self.chunk_manager.move_chunk(fp16_param_chunk, get_current_device()) self.module._set_chunk_grad_device(fp16_param_chunk, get_current_device()) fp32_params_used_cuda_margin_mem += fp32_param_chunk.mem for p in fp16_param_chunk.get_tensors(): state = self.optim.state[p] for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.to(get_current_device()) self.module._setup_grads_ptr() def _register_states_(self): for group in self.optim.param_groups: for p in group['params']: state = self.optim.state[p] for val in state.values(): if isinstance(val, torch.Tensor): self.chunk_manager.add_extern_static_tensor(val) def load_state_dict(self, *args, **kwargs): super().load_state_dict(*args, **kwargs) for group in self.optim.param_groups: for p in group['params']: state = self.optim.state[p] for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.to(dtype=self.fp16_param_to_fp32_param[p].dtype, device=self.fp16_param_to_fp32_param[p].device)