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):
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)