mirror of https://github.com/hpcaitech/ColossalAI
247 lines
11 KiB
Python
247 lines
11 KiB
Python
from enum import Enum
|
|
from typing import Any, Dict, Set, Tuple
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.nn import Parameter
|
|
from torch.optim import Optimizer
|
|
|
|
from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
|
|
from colossalai.gemini.chunk import Chunk, ChunkManager
|
|
from colossalai.logging import get_dist_logger
|
|
from colossalai.nn.optimizer import ColossalaiOptimizer
|
|
from colossalai.nn.parallel.data_parallel import ZeroDDP
|
|
from colossalai.utils import disposable, get_current_device
|
|
|
|
|
|
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,
|
|
**defaults: Any):
|
|
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]
|
|
if local_chunk.keep_gathered:
|
|
return param_info.offset, param_info.end
|
|
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
|