import torch
import torch.distributed as dist
from enum import Enum
from torch.optim import Optimizer
from torch.nn import Parameter
from colossalai.nn.parallel.data_parallel import ZeroDDP
from typing import Dict, Tuple, Set
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
from colossalai.gemini.chunk import Chunk, ChunkManager
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: ChunkManager = self.gemini_manager.chunk_manager
self.optim_state = OptimState.UNSCALED
self.param_to_range: Dict[Parameter, Tuple[int, int]] = dict()
self.param_to_chunk32: Dict[Parameter, Chunk] = dict()
self.chunk16_set: Set[Chunk] = set()
params_list = [p for p in module.parameters() if not getattr(p, '_ddp_to_ignore', False)]
for p, fp32_p in zip(params_list, module.fp32_params):
chunk_16 = self.chunk_manager.get_chunk(p)
if chunk_16 not in self.chunk16_set:
self.chunk16_set.add(chunk_16)
self.__init__optimizer()
# 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=get_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 _set_grad_ptr(self):
for group in self.param_groups:
for fake_param in group['params']:
chunk32 = self.param_to_chunk32[fake_param]
begin, end = self.param_to_range[fake_param]
chunk16 = chunk32.paired_chunk
fake_param.data = chunk16.payload[begin:end]
fake_param.grad = fake_param.data
fake_param.data = chunk32.payload[begin:end]
def _update_fp16_params(self):
none_tensor = torch.empty([0])
for group in self.param_groups:
for fake_param in group['params']:
assert fake_param.grad is None
fake_param.data = none_tensor
for chunk16 in self.chunk16_set:
chunk16.optim_update()
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()
self._set_grad_ptr()
# 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
ret = self.optim.step(*args, **kwargs)
self._register_states()
self.zero_grad()
self._update_fp16_params()
return ret
def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: float = 2.0):
raise NotImplementedError
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):
# This function is called except the last stage of pipeline parallel
# It receives the scaled grad from the previous rank
# No need to scale the grad again
# Need to unscale when optimizing
self.optim_state = OptimState.SCALED
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 group in self.param_groups:
for fake_param in group['params']:
chunk32 = self.param_to_chunk32[fake_param]
chunk16 = chunk32.paired_chunk
if chunk32.device_type == 'cuda':
continue
if fp32_params_used_cuda_margin_mem + chunk32.payload_mem < fp32_params_available_cuda_margin_mem:
self.chunk_manager.move_chunk(chunk32, get_current_device())
# stores grad now
self.chunk_manager.move_chunk(chunk16, get_current_device())
self.module.set_chunk_grad_device(chunk16, get_current_device())
fp32_params_used_cuda_margin_mem += chunk32.payload_mem
for group in self.param_groups:
for fake_param in group['params']:
chunk32 = self.param_to_chunk32[fake_param]
if chunk32.device_type == 'cuda':
state = self.optim.state[fake_param]
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(get_current_device())
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 __init__optimizer(self):
def get_range_pair(local_chunk: Chunk, local_param: Parameter):
param_info = local_chunk.tensors_info[local_param]
begin = max(0, param_info.offset - local_chunk.shard_begin)
end = min(local_chunk.shard_size, param_info.end - local_chunk.shard_begin)
return begin, end
for group in self.optim.param_groups:
fake_params_list = list()
for param in group['params']:
chunk16 = self.chunk_manager.get_chunk(param)
range_pair = get_range_pair(chunk16, param)
if range_pair[0] >= range_pair[1]:
continue
fake_param = torch.nn.Parameter(torch.empty([0]))
self.param_to_chunk32[fake_param] = chunk16.paired_chunk
self.param_to_range[fake_param] = range_pair
fake_params_list.append(fake_param)
group['params'] = fake_params_list