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
from colossalai.utils.common import _compute_grad_lp, compute_grad_norm, _clip_grad_norm
from collections import defaultdict, abc as container_abcs
from copy import deepcopy
from itertools import chain
from torch._six import inf
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.zero_grad()
self._update_fp16_params()
return ret
def compute_grad_norm(self, norm_type: float = 2.0) -> float:
norm_type = float(norm_type)
if not self.chunk_manager.enable_distributed_storage:
return compute_grad_norm(self.module.parameters(), norm_type)
non_distributed_params = []
distributed_params = []
for p in self.module.parameters():
if getattr(p, '_ddp_to_ignore', False):
non_distributed_params.append(p)
else:
distributed_params.append(p)
non_distributed_norm = _compute_grad_lp(non_distributed_params, norm_type)
distributed_norm_tensor = torch.tensor([_compute_grad_lp(distributed_params, norm_type)],
device=get_current_device())
if norm_type == inf:
dist.all_reduce(distributed_norm_tensor,
op=dist.ReduceOp.MAX,
group=self.chunk_manager.process_group.dp_process_group())
total_norm = max(non_distributed_norm, distributed_norm_tensor.item())
else:
dist.all_reduce(distributed_norm_tensor, group=self.chunk_manager.process_group.dp_process_group())
total_norm = non_distributed_norm + distributed_norm_tensor.item()
total_norm = total_norm**(1 / norm_type)
return total_norm
def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: float = 2.0):
if self.optim_state == OptimState.SCALED:
self._unscale_grads()
total_norm = self.compute_grad_norm(norm_type)
_clip_grad_norm(self.module.parameters(), max_norm, total_norm)
return total_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):
# 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 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 state_dict(self, only_rank_0: bool = True):
r"""Returns the state of the optimizer as a :class:`dict`. If only_rank_0 is True, for DP rank != 0, this function returns None.
This saves memory usage.
It contains two entries:
* state - a dict holding current optimization state. Its content
differs between optimizer classes.
* param_groups - a list containing all parameter groups where each
parameter group is a dict
"""
is_rank_0 = self.chunk_manager.process_group.dp_local_rank() == 0
if not self.chunk_manager.enable_distributed_storage and only_rank_0 and not is_rank_0:
return
optim_state_dict = super().state_dict()
scaler_state_dict = self.grad_scaler.state_dict()
optim_state_dict['scaler'] = scaler_state_dict
if not self.chunk_manager.enable_distributed_storage:
return optim_state_dict
local_state = {k: convert_state_dict_to_cpu(v) for k, v in optim_state_dict['state'].items() if len(v) > 0}
if not self.chunk_manager.process_group.has_cpu_groups:
self.chunk_manager.process_group.set_cpu_groups()
output = [None for _ in range(self.chunk_manager.process_group.dp_world_size())]
if only_rank_0:
dst_rank = self.chunk_manager.process_group.dp_rank_list()[0]
dist.gather_object(local_state,
output if self.chunk_manager.process_group.dp_local_rank() == 0 else None,
dst=dst_rank,
group=self.chunk_manager.process_group.cpu_dp_process_group())
if not is_rank_0:
return
else:
dist.all_gather_object(output, local_state, group=self.chunk_manager.process_group.cpu_dp_process_group())
for state in output:
optim_state_dict['state'].update(state)
return optim_state_dict
def load_state_dict(self, state_dict):
r"""Loads the optimizer state.
Args:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
"""
if 'scaler' not in state_dict:
self._logger.warning('Missing scaler when loading optimizer state dict', ranks=[0])
else:
self.grad_scaler.load_state_dict(deepcopy(state_dict['scaler']))
# Validate the state_dict
groups = self.param_groups
saved_groups = deepcopy(state_dict['param_groups'])
if len(groups) != len(saved_groups):
raise ValueError("loaded state dict has a different number of "
"parameter groups")
param_lens = (len(g['params']) for g in groups)
saved_lens = (len(g['params']) for g in saved_groups)
if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
raise ValueError("loaded state dict contains a parameter group "
"that doesn't match the size of optimizer's group")
# Update the state
id_map = {
old_id: p for old_id, p in zip(chain.from_iterable((g['params'] for g in saved_groups
)), chain.from_iterable((g['params'] for g in groups)))
}
def cast(param, value):
r"""Make a deep copy of value, casting all tensors to device of param."""
if isinstance(value, torch.Tensor):
# Floating-point types are a bit special here. They are the only ones
# that are assumed to always match the type of params.
if param.is_floating_point():
value = value.to(param.dtype)
value = value.to(param.device)
return value
elif isinstance(value, dict):
return {k: cast(param, v) for k, v in value.items()}
elif isinstance(value, container_abcs.Iterable):
return type(value)(cast(param, v) for v in value)
else:
return value
# Copy state assigned to params (and cast tensors to appropriate types).
# State that is not assigned to params is copied as is (needed for
# backward compatibility).
state = defaultdict(dict)
for k, v in state_dict['state'].items():
if k in id_map:
param = self.fp16_param_to_fp32_param[id_map[k]]
if param.storage().size() > 0:
state[param] = cast(param, deepcopy(v))
else:
state[k] = deepcopy(v)
# Update parameter groups, setting their 'params' value
def update_group(group, new_group):
new_group['params'] = group['params']
return new_group
param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)]
self.__setstate__({'state': state, 'param_groups': param_groups})
def convert_state_dict_to_cpu(state: Dict[str, torch.Tensor]):
return {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in state.items()}