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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from collections import defaultdict
from itertools import chain
from torch.nn.utils import clip_grad
from annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
from ..dist_utils import allreduce_grads
from ..fp16_utils import LossScaler, wrap_fp16_model
from .hook import HOOKS, Hook
try:
# If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported
# and used; otherwise, auto fp16 will adopt mmcv's implementation.
from torch.cuda.amp import GradScaler
except ImportError:
pass
@HOOKS.register_module()
class OptimizerHook(Hook):
def __init__(self, grad_clip=None):
self.grad_clip = grad_clip
def clip_grads(self, params):
params = list(
filter(lambda p: p.requires_grad and p.grad is not None, params))
if len(params) > 0:
return clip_grad.clip_grad_norm_(params, **self.grad_clip)
def after_train_iter(self, runner):
runner.optimizer.zero_grad()
runner.outputs['loss'].backward()
if self.grad_clip is not None:
grad_norm = self.clip_grads(runner.model.parameters())
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update({'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
runner.optimizer.step()
@HOOKS.register_module()
class GradientCumulativeOptimizerHook(OptimizerHook):
"""Optimizer Hook implements multi-iters gradient cumulating.
Args:
cumulative_iters (int, optional): Num of gradient cumulative iters.
The optimizer will step every `cumulative_iters` iters.
Defaults to 1.
Examples:
>>> # Use cumulative_iters to simulate a large batch size
>>> # It is helpful when the hardware cannot handle a large batch size.
>>> loader = DataLoader(data, batch_size=64)
>>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4)
>>> # almost equals to
>>> loader = DataLoader(data, batch_size=256)
>>> optim_hook = OptimizerHook()
"""
def __init__(self, cumulative_iters=1, **kwargs):
super(GradientCumulativeOptimizerHook, self).__init__(**kwargs)
assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \
f'cumulative_iters only accepts positive int, but got ' \
f'{type(cumulative_iters)} instead.'
self.cumulative_iters = cumulative_iters
self.divisible_iters = 0
self.remainder_iters = 0
self.initialized = False
def has_batch_norm(self, module):
if isinstance(module, _BatchNorm):
return True
for m in module.children():
if self.has_batch_norm(m):
return True
return False
def _init(self, runner):
if runner.iter % self.cumulative_iters != 0:
runner.logger.warning(
'Resume iter number is not divisible by cumulative_iters in '
'GradientCumulativeOptimizerHook, which means the gradient of '
'some iters is lost and the result may be influenced slightly.'
)
if self.has_batch_norm(runner.model) and self.cumulative_iters > 1:
runner.logger.warning(
'GradientCumulativeOptimizerHook may slightly decrease '
'performance if the model has BatchNorm layers.')
residual_iters = runner.max_iters - runner.iter
self.divisible_iters = (
residual_iters // self.cumulative_iters * self.cumulative_iters)
self.remainder_iters = residual_iters - self.divisible_iters
self.initialized = True
def after_train_iter(self, runner):
if not self.initialized:
self._init(runner)
if runner.iter < self.divisible_iters:
loss_factor = self.cumulative_iters
else:
loss_factor = self.remainder_iters
loss = runner.outputs['loss']
loss = loss / loss_factor
loss.backward()
if (self.every_n_iters(runner, self.cumulative_iters)
or self.is_last_iter(runner)):
if self.grad_clip is not None:
grad_norm = self.clip_grads(runner.model.parameters())
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update({'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
runner.optimizer.step()
runner.optimizer.zero_grad()
if (TORCH_VERSION != 'parrots'
and digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
@HOOKS.register_module()
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer hook (using PyTorch's implementation).
If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
to take care of the optimization procedure.
Args:
loss_scale (float | str | dict): Scale factor configuration.
If loss_scale is a float, static loss scaling will be used with
the specified scale. If loss_scale is a string, it must be
'dynamic', then dynamic loss scaling will be used.
It can also be a dict containing arguments of GradScalar.
Defaults to 512. For Pytorch >= 1.6, mmcv uses official
implementation of GradScaler. If you use a dict version of
loss_scale to create GradScaler, please refer to:
https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler
for the parameters.
Examples:
>>> loss_scale = dict(
... init_scale=65536.0,
... growth_factor=2.0,
... backoff_factor=0.5,
... growth_interval=2000
... )
>>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale)
"""
def __init__(self,
grad_clip=None,
coalesce=True,
bucket_size_mb=-1,
loss_scale=512.,
distributed=True):
self.grad_clip = grad_clip
self.coalesce = coalesce
self.bucket_size_mb = bucket_size_mb
self.distributed = distributed
self._scale_update_param = None
if loss_scale == 'dynamic':
self.loss_scaler = GradScaler()
elif isinstance(loss_scale, float):
self._scale_update_param = loss_scale
self.loss_scaler = GradScaler(init_scale=loss_scale)
elif isinstance(loss_scale, dict):
self.loss_scaler = GradScaler(**loss_scale)
else:
raise ValueError('loss_scale must be of type float, dict, or '
f'"dynamic", got {loss_scale}')
def before_run(self, runner):
"""Preparing steps before Mixed Precision Training."""
# wrap model mode to fp16
wrap_fp16_model(runner.model)
# resume from state dict
if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
scaler_state_dict = runner.meta['fp16']['loss_scaler']
self.loss_scaler.load_state_dict(scaler_state_dict)
def copy_grads_to_fp32(self, fp16_net, fp32_weights):
"""Copy gradients from fp16 model to fp32 weight copy."""
for fp32_param, fp16_param in zip(fp32_weights,
fp16_net.parameters()):
if fp16_param.grad is not None:
if fp32_param.grad is None:
fp32_param.grad = fp32_param.data.new(
fp32_param.size())
fp32_param.grad.copy_(fp16_param.grad)
def copy_params_to_fp16(self, fp16_net, fp32_weights):
"""Copy updated params from fp32 weight copy to fp16 model."""
for fp16_param, fp32_param in zip(fp16_net.parameters(),
fp32_weights):
fp16_param.data.copy_(fp32_param.data)
def after_train_iter(self, runner):
"""Backward optimization steps for Mixed Precision Training. For
dynamic loss scaling, please refer to
https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.
1. Scale the loss by a scale factor.
2. Backward the loss to obtain the gradients.
3. Unscale the optimizer’s gradient tensors.
4. Call optimizer.step() and update scale factor.
5. Save loss_scaler state_dict for resume purpose.
"""
# clear grads of last iteration
runner.model.zero_grad()
runner.optimizer.zero_grad()
self.loss_scaler.scale(runner.outputs['loss']).backward()
self.loss_scaler.unscale_(runner.optimizer)
# grad clip
if self.grad_clip is not None:
grad_norm = self.clip_grads(runner.model.parameters())
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update({'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
# backward and update scaler
self.loss_scaler.step(runner.optimizer)
self.loss_scaler.update(self._scale_update_param)
# save state_dict of loss_scaler
runner.meta.setdefault(
'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
@HOOKS.register_module()
class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
Fp16OptimizerHook):
"""Fp16 optimizer Hook (using PyTorch's implementation) implements
multi-iters gradient cumulating.
If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
to take care of the optimization procedure.
"""
def __init__(self, *args, **kwargs):
super(GradientCumulativeFp16OptimizerHook,
self).__init__(*args, **kwargs)
def after_train_iter(self, runner):
if not self.initialized:
self._init(runner)
if runner.iter < self.divisible_iters:
loss_factor = self.cumulative_iters
else:
loss_factor = self.remainder_iters
loss = runner.outputs['loss']
loss = loss / loss_factor
self.loss_scaler.scale(loss).backward()
if (self.every_n_iters(runner, self.cumulative_iters)
or self.is_last_iter(runner)):
# copy fp16 grads in the model to fp32 params in the optimizer
self.loss_scaler.unscale_(runner.optimizer)
if self.grad_clip is not None:
grad_norm = self.clip_grads(runner.model.parameters())
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update(
{'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
# backward and update scaler
self.loss_scaler.step(runner.optimizer)
self.loss_scaler.update(self._scale_update_param)
# save state_dict of loss_scaler
runner.meta.setdefault(
'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
# clear grads
runner.model.zero_grad()
runner.optimizer.zero_grad()
else:
@HOOKS.register_module()
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer hook (mmcv's implementation).
The steps of fp16 optimizer is as follows.
1. Scale the loss value.
2. BP in the fp16 model.
2. Copy gradients from fp16 model to fp32 weights.
3. Update fp32 weights.
4. Copy updated parameters from fp32 weights to fp16 model.
Refer to https://arxiv.org/abs/1710.03740 for more details.
Args:
loss_scale (float | str | dict): Scale factor configuration.
If loss_scale is a float, static loss scaling will be used with
the specified scale. If loss_scale is a string, it must be
'dynamic', then dynamic loss scaling will be used.
It can also be a dict containing arguments of LossScaler.
Defaults to 512.
"""
def __init__(self,
grad_clip=None,
coalesce=True,
bucket_size_mb=-1,
loss_scale=512.,
distributed=True):
self.grad_clip = grad_clip
self.coalesce = coalesce
self.bucket_size_mb = bucket_size_mb
self.distributed = distributed
if loss_scale == 'dynamic':
self.loss_scaler = LossScaler(mode='dynamic')
elif isinstance(loss_scale, float):
self.loss_scaler = LossScaler(
init_scale=loss_scale, mode='static')
elif isinstance(loss_scale, dict):
self.loss_scaler = LossScaler(**loss_scale)
else:
raise ValueError('loss_scale must be of type float, dict, or '
f'"dynamic", got {loss_scale}')
def before_run(self, runner):
"""Preparing steps before Mixed Precision Training.
1. Make a master copy of fp32 weights for optimization.
2. Convert the main model from fp32 to fp16.
"""
# keep a copy of fp32 weights
old_groups = runner.optimizer.param_groups
runner.optimizer.param_groups = copy.deepcopy(
runner.optimizer.param_groups)
state = defaultdict(dict)
p_map = {
old_p: p
for old_p, p in zip(
chain(*(g['params'] for g in old_groups)),
chain(*(g['params']
for g in runner.optimizer.param_groups)))
}
for k, v in runner.optimizer.state.items():
state[p_map[k]] = v
runner.optimizer.state = state
# convert model to fp16
wrap_fp16_model(runner.model)
# resume from state dict
if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
scaler_state_dict = runner.meta['fp16']['loss_scaler']
self.loss_scaler.load_state_dict(scaler_state_dict)
def copy_grads_to_fp32(self, fp16_net, fp32_weights):
"""Copy gradients from fp16 model to fp32 weight copy."""
for fp32_param, fp16_param in zip(fp32_weights,
fp16_net.parameters()):
if fp16_param.grad is not None:
if fp32_param.grad is None:
fp32_param.grad = fp32_param.data.new(
fp32_param.size())
fp32_param.grad.copy_(fp16_param.grad)
def copy_params_to_fp16(self, fp16_net, fp32_weights):
"""Copy updated params from fp32 weight copy to fp16 model."""
for fp16_param, fp32_param in zip(fp16_net.parameters(),
fp32_weights):
fp16_param.data.copy_(fp32_param.data)
def after_train_iter(self, runner):
"""Backward optimization steps for Mixed Precision Training. For
dynamic loss scaling, please refer `loss_scalar.py`
1. Scale the loss by a scale factor.
2. Backward the loss to obtain the gradients (fp16).
3. Copy gradients from the model to the fp32 weight copy.
4. Scale the gradients back and update the fp32 weight copy.
5. Copy back the params from fp32 weight copy to the fp16 model.
6. Save loss_scaler state_dict for resume purpose.
"""
# clear grads of last iteration
runner.model.zero_grad()
runner.optimizer.zero_grad()
# scale the loss value
scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale
scaled_loss.backward()
# copy fp16 grads in the model to fp32 params in the optimizer
fp32_weights = []
for param_group in runner.optimizer.param_groups:
fp32_weights += param_group['params']
self.copy_grads_to_fp32(runner.model, fp32_weights)
# allreduce grads
if self.distributed:
allreduce_grads(fp32_weights, self.coalesce,
self.bucket_size_mb)
has_overflow = self.loss_scaler.has_overflow(fp32_weights)
# if has overflow, skip this iteration
if not has_overflow:
# scale the gradients back
for param in fp32_weights:
if param.grad is not None:
param.grad.div_(self.loss_scaler.loss_scale)
if self.grad_clip is not None:
grad_norm = self.clip_grads(fp32_weights)
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update(
{'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
# update fp32 params
runner.optimizer.step()
# copy fp32 params to the fp16 model
self.copy_params_to_fp16(runner.model, fp32_weights)
self.loss_scaler.update_scale(has_overflow)
if has_overflow:
runner.logger.warning('Check overflow, downscale loss scale '
f'to {self.loss_scaler.cur_scale}')
# save state_dict of loss_scaler
runner.meta.setdefault(
'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
@HOOKS.register_module()
class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
Fp16OptimizerHook):
"""Fp16 optimizer Hook (using mmcv implementation) implements multi-
iters gradient cumulating."""
def __init__(self, *args, **kwargs):
super(GradientCumulativeFp16OptimizerHook,
self).__init__(*args, **kwargs)
def after_train_iter(self, runner):
if not self.initialized:
self._init(runner)
if runner.iter < self.divisible_iters:
loss_factor = self.cumulative_iters
else:
loss_factor = self.remainder_iters
loss = runner.outputs['loss']
loss = loss / loss_factor
# scale the loss value
scaled_loss = loss * self.loss_scaler.loss_scale
scaled_loss.backward()
if (self.every_n_iters(runner, self.cumulative_iters)
or self.is_last_iter(runner)):
# copy fp16 grads in the model to fp32 params in the optimizer
fp32_weights = []
for param_group in runner.optimizer.param_groups:
fp32_weights += param_group['params']
self.copy_grads_to_fp32(runner.model, fp32_weights)
# allreduce grads
if self.distributed:
allreduce_grads(fp32_weights, self.coalesce,
self.bucket_size_mb)
has_overflow = self.loss_scaler.has_overflow(fp32_weights)
# if has overflow, skip this iteration
if not has_overflow:
# scale the gradients back
for param in fp32_weights:
if param.grad is not None:
param.grad.div_(self.loss_scaler.loss_scale)
if self.grad_clip is not None:
grad_norm = self.clip_grads(fp32_weights)
if grad_norm is not None:
# Add grad norm to the logger
runner.log_buffer.update(
{'grad_norm': float(grad_norm)},
runner.outputs['num_samples'])
# update fp32 params
runner.optimizer.step()
# copy fp32 params to the fp16 model
self.copy_params_to_fp16(runner.model, fp32_weights)
else:
runner.logger.warning(
'Check overflow, downscale loss scale '
f'to {self.loss_scaler.cur_scale}')
self.loss_scaler.update_scale(has_overflow)
# save state_dict of loss_scaler
runner.meta.setdefault(
'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
# clear grads
runner.model.zero_grad()
runner.optimizer.zero_grad()
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