from enum import Enum
from os import stat
from typing import Dict, Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.gemini.tensor_utils import (colo_model_data_tensor_move_inline, colo_tensor_mem_usage)
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_model._utils import cast_tensor_to_fp32
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.nn.parameter import Parameter
from torch.optim import Optimizer
from colossalai.gemini.stateful_tensor import (StatefulTensor, TensorState)
from colossalai.gemini.tensor_placement_policy import AutoTensorPlacementPolicy
class OptimState(Enum):
SCALED = 1
UNSCALED = 2
class ShardedOptimizerV2(ColossalaiOptimizer):
"""A wrapper for optimizer. ``ShardedOptimizerV2`` and ``ShardedModelV2`` implement Zero Redundancy Optimizer (ZeRO).
By default the ZeRO optimizer stage 3 offload Optimizer States on CPU.
We apply the Device-aware Operator Placement technique for OS placement from the following paper.
`PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
GPU margin space is the remaining space after removing peak non-model data from the overall GPU memory,
which is detected by a runtime memory tracer.
We place as many OS chunks in the margin space as possible.
The size of margin space can be controlled by ``gpu_margin_mem_ratio``.
If it is set as ``0.0``, it is the same as classical ZeRO optimizer.
Note:
You must use ``ShardedOptimizerV2`` with ``ShardedModelV2``.
Note:
Make sure you set ``tensor_placement_policy`` in ``ShardedModelV2`` to `"auto"`,
if you set ``gpu_margin_mem_ratio > 0``.
Args:
sharded_model (ShardedModelV2): A sharded model initialized by class ShardedModelV2. The optimizer will use the
shard strategy provided by sharded model to shard param fp32 tensors.
optimizer (Optimizer): An Optimizer 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 `tensor_placement_policy` of `ShardedModelV2` 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.
dp_process_group (Optional[ProcessGroup], optional): data paralle process group. Defaults to None.
mp_process_group (Optional[ProcessGroup], optional): model paralle process group. Defaults to None.
.. _PatrickStar\: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
https://arxiv.org/abs/2108.05818
"""
def __init__(self,
sharded_model: ShardedModelV2,
optimizer: Optimizer,
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,
dp_process_group: Optional[ProcessGroup] = None,
mp_process_group: Optional[ProcessGroup] = None,
verbose: bool = False) -> None:
assert isinstance(sharded_model, ShardedModelV2), 'model must be wrapped with ShardedModel'
assert not isinstance(optimizer, ShardedOptimizerV2), 'Nested ShardedOptimizerV2 is not supported.'
super().__init__(optimizer)
self.shard_strategy = sharded_model.shard_strategy
self.model: ShardedModelV2 = sharded_model
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_shards_h2d: bool = sharded_model.cpu_offload and self.gpu_margin_mem_ratio > 0.0 and getattr(
optimizer, 'num_fp32_shards_per_param', 0) >= 2
self.device = sharded_model._tensor_placement_policy.device or torch.device('cpu')
self.optim_state: OptimState = OptimState.UNSCALED
self.dp_process_group = dp_process_group or gpc.get_group(ParallelMode.DATA)
self.mp_process_group = mp_process_group or gpc.get_group(ParallelMode.MODEL)
# 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: Tensor = torch.IntTensor([0]).to(torch.cuda.current_device())
self._logger = get_dist_logger("ShardedOptimizerV2")
self._verbose = verbose
# Store fp32 param shards
self._register_master_weight()
if self.gpu_margin_mem_ratio != 0.0 and not isinstance(sharded_model._tensor_placement_policy,
AutoTensorPlacementPolicy):
self._logger.warning(f'gpu_margin_mem_ratio is meaningless when tensor_placement_policy is not "auto"',
ranks=[0])
if self._verbose:
self._logger.debug(
f"After init ShardedOptimizerV2 consumes {self.get_memory_usage()[0] / 1e6} MB CUDA Memory!", ranks=[0])
self._use_memory_tracer = self.model.use_memory_tracer
@property
def loss_scale(self):
return self.grad_scaler.scale.item()
def get_memory_usage(self) -> Tuple[int, int]:
""" Get the memory usage of the optimizer. Including master_params (param fp32),
momentum (``self.state[p]['exp_avg']``) variance (``self.state[p]['exp_avg_sq']``)
Returns:
Tuple[int, int]: cuda/cpu memory usage in Byte.
"""
cuda_use = 0
cpu_use = 0
def update_mem_use(t):
nonlocal cuda_use
nonlocal cpu_use
t_cuda_use, t_cpu_use = colo_tensor_mem_usage(t)
cuda_use += t_cuda_use
cpu_use += t_cpu_use
for _, p_fp32 in self.master_params.items():
update_mem_use(p_fp32)
for group in self.optim.param_groups:
for p in group['params']:
state = self.optim.state[p]
for k, v in state.items():
update_mem_use(v)
return cuda_use, cpu_use
def zero_grad(self, *args, **kwargs):
self._zero_grad()
def backward(self, loss: Tensor) -> None:
loss = self.loss_scale * loss
self.optim_state = OptimState.SCALED
self.model.backward(loss)
def backward_by_grad(self, tensor: Tensor, grad: Tensor) -> None:
# 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.model.backward_by_grad(tensor, grad)
def clip_grad_norm(self, model: nn.Module, max_norm: float):
if self.optim_state == OptimState.SCALED:
self._prepare_grads()
self._unscale_grads()
return super().clip_grad_norm(model, max_norm)
def step(self, *args, **kwargs):
# unscale grads if scaled
if self.optim_state == OptimState.SCALED:
self._prepare_grads()
self._unscale_grads()
self._maybe_move_fp32_shards()
found_inf = self._check_overflow()
self.grad_scaler.update(found_inf)
if found_inf:
self._logger.warning('found inf during ShardedOptimV2 step')
self._zero_grad(recover_data=True)
return
self._point_param_fp16_to_master_param()
if self._verbose:
gpu_mem, cpu_mem = self.get_memory_usage()
self._logger.debug(
f"Before step ShardedOptimizerV2 consumes {gpu_mem / 1e6} MB CUDA Memory, {cpu_mem / 1e6} MB CUDA Memory!",
ranks=[0])
ret = self.optim.step(*args, **kwargs)
if self._verbose:
gpu_mem, cpu_mem = self.get_memory_usage()
self._logger.debug(
f"After step ShardedOptimizerV2 consumes {gpu_mem / 1e6} MB CUDA Memory, {cpu_mem / 1e6} MB CUDA Memory!",
ranks=[0])
self._copy_master_model_to_model_fp16()
return ret
def _check_overflow(self):
# clear previous overflow record
self._found_overflow.fill_(self.model.overflow_counter)
# all-reduce across dp group
dist.all_reduce(self._found_overflow, group=self.dp_process_group)
# all-reduce over model parallel group
dist.all_reduce(self._found_overflow, group=self.mp_process_group)
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
def _zero_grad(self, recover_data: bool = False):
"""zero grad and maybe recover fp16 params
When `reuse_fp16_shard` is enabled,
p.colo_attr.sharded_data_tensor stores grad here.
We have to recover them from fp32 params.
Args:
recover_data (bool, optional): Whether to recover fp16 param from fp32 param. Defaults to False.
"""
# We must set grad to None
# Because grad here is sharded
# But next backward pass will create a full grad first
# Which leads to wrong accumulation
self.optim.zero_grad(set_to_none=True)
for group in self.optim.param_groups:
for p in group['params']:
# p.colo_attr.sharded_data_tensor stores grad now
# we have to recover fp16 param
reuse_fp16_shard = (p.colo_attr.sharded_data_tensor.payload_size == 0)
if recover_data and reuse_fp16_shard:
self._copy_master_param_to_param_fp16(p)
else:
# release saved gradient
p.colo_attr.saved_grad.set_null()
self.model.overflow_counter = 0 # set overflow counter to zero
def sync_grad(self):
pass
def _register_master_weight(self):
self.master_params: Dict[Parameter, StatefulTensor] = {}
for group in self.optim.param_groups:
for p in group['params']:
assert hasattr(p, 'colo_attr'), 'The parameter must be wrapped with ShardedParam'
shard_flag = not p.colo_attr.sharded_data_tensor.is_sharded and p.colo_attr.is_replicated
if shard_flag:
# we always shard replicated paramters
self.shard_strategy.shard([p.colo_attr.sharded_data_tensor], self.dp_process_group)
self.master_params[p] = StatefulTensor(cast_tensor_to_fp32(p.colo_attr.data_payload.to(self.device)))
if shard_flag:
# In this branch, there's no need to shard param
# So we gather here
self.shard_strategy.gather([p.colo_attr.sharded_data_tensor], self.dp_process_group)
def _maybe_move_fp32_shards(self):
if self._should_move_fp32_shards_h2d:
self._should_move_fp32_shards_h2d = False
available_cuda_margin_mem = self.model.cuda_margin_space * self.gpu_margin_mem_ratio
fp32_shards_available_cuda_margin_mem = available_cuda_margin_mem / self.optim.num_fp32_shards_per_param
fp32_shards_used_cuda_margin_mem = 0
for group in self.optim.param_groups:
for p in group['params']:
if p.colo_attr.saved_grad.is_null():
continue
shard_mem = self.master_params[p].payload.numel() * self.master_params[p].payload.element_size()
if fp32_shards_used_cuda_margin_mem + shard_mem < fp32_shards_available_cuda_margin_mem:
colo_model_data_tensor_move_inline(self.master_params[p], torch.cuda.current_device())
colo_model_data_tensor_move_inline(p.colo_attr.saved_grad, torch.cuda.current_device())
p.colo_attr.offload_grad = False
fp32_shards_used_cuda_margin_mem += shard_mem
state = self.optim.state[p]
for k, v in state.items():
if isinstance(v, Tensor):
state[k] = v.cuda()
def _prepare_grads(self):
for group in self.optim.param_groups:
for p in group['params']:
if p.colo_attr.saved_grad.is_null():
continue
p.colo_attr.saved_grad.trans_state(TensorState.COMPUTE)
# If reuse_fp16_shard, grad fp16 which wasn't be offloaded may be evicted to CPU
if not p.colo_attr.offload_grad:
colo_model_data_tensor_move_inline(p.colo_attr.saved_grad, torch.cuda.current_device())
# FIXME(ver217): p.data here is an empty tensor on CUDA and has no useful infomation
# If we change p.grad directly
# it may raise error because of different shape/dtype/device of p.data and p.grad
# We just set p.data = p.colo_attr.saved_grad.payload here
p.data = p.colo_attr.grad_payload
p.grad = p.colo_attr.grad_payload
# Set p.data to empty tensor, in case of memory leaking
p.colo_attr.set_data_none()
def _point_param_fp16_to_master_param(self):
# assign master param pointers to p.data.
# We will not trigger data copy here.
for group in self.optim.param_groups:
for p in group['params']:
self.master_params[p].trans_state(TensorState.COMPUTE)
p.data = self.master_params[p].payload
# Now p.data is sharded
# So optimizer states are sharded naturally
def _copy_master_model_to_model_fp16(self):
# Copy master param data (fp32) to payload of colo_attr (fp16)
# TODO() improve efficiency by gathering tensors into a chunk and transfering
# a chunk.
for group in self.optim.param_groups:
for p in group['params']:
self._copy_master_param_to_param_fp16(p)
def _copy_master_param_to_param_fp16(self, p):
# flush gradient
if p.colo_attr.sharded_data_tensor.payload_size == 0:
# here reuse_fp16_shard is True
# in order to use copy below, we should give sharded data tensor a payload
p.colo_attr.sharded_data_tensor.payload_relay(p.colo_attr.saved_grad)
else:
p.colo_attr.saved_grad.set_null()
p.data = self.master_params[p].payload
# we need to allocate new memory for keep_not_shard paramters
# in order to use copy, otherwise, the sizes of tensor is not compatible
if p.colo_attr.data_payload.numel() != p.data.numel():
p.colo_attr.data_payload_reset(
torch.empty(p.data.shape, dtype=p.colo_attr.data_payload.dtype, device=p.colo_attr.data_payload.device))
# TODO() optimize this line CPU (fp32) -> GPU (fp16)
p.colo_attr.sharded_data_tensor.payload_copy(p.half().detach())
p.colo_attr.set_data_none()
if p.colo_attr.keep_not_shard and p.colo_attr.is_replicated:
# We gather full fp16 param here
p.colo_attr.sharded_data_tensor.is_sharded = True # since only gradient is sharded, we should set to True
self.shard_strategy.gather([p.colo_attr.sharded_data_tensor], self.dp_process_group)
self.master_params[p].trans_state(TensorState.HOLD)
def state_dict(self):
optim_state_dict = super().state_dict()
scaler_state_dict = self.grad_scaler.state_dict()
optim_state_dict['scaler'] = scaler_state_dict
return optim_state_dict
def load_state_dict(self, *args, **kwargs):
if 'scaler' not in args[0]:
self._logger.warning('Missing scaler when loading optimizer state dict', ranks=[0])
else:
scaler_state_dict = args[0].pop('scaler')
self.grad_scaler.load_state_dict(scaler_state_dict)
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, Tensor):
state[k] = v.to(dtype=self.master_params[p].dtype, device=self.master_params[p].device)