File size: 11,994 Bytes
3ad86c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 |
""" Adafactor (Big Vision variant) for PyTorch
Adapted from the implementation in big vision: https://github.com/google-research/big_vision
Described in 'Scaling Vision Transformers': https://arxiv.org/abs/2106.04560
Adaptation and PyTorch modifications by Ross Wightman
"""
from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch.optim import Optimizer
from ._types import ParamsT
def _get_scalar_dtype():
"""Get the scalar dtype that the optimizer uses for state"""
return torch.float64
def _factored_dims(
shape: Tuple[int, ...],
factored: bool,
min_dim_size_to_factor: int
) -> Optional[tuple[int, int]]:
"""Whether to use a factored second moment estimator.
This function returns a tuple with the two largest axes to reduce over.
If no two dimensions have size >= min_dim_size_to_factor, return None.
Args:
shape: an input shape
factored: whether to use factored second-moment estimator for > 2d vars.
min_dim_size_to_factor: only factor accumulator if two array dimensions have at least this size.
Returns:
None or a tuple of ints
"""
if not factored or len(shape) < 2:
return None
sorted_dims = sorted(((x, i) for i, x in enumerate(shape)))
if shape[sorted_dims[-2][1]] < min_dim_size_to_factor:
return None
return int(sorted_dims[-2][1]), int(sorted_dims[-1][1])
class AdafactorBigVision(Optimizer):
"""
PyTorch implementation of BigVision's Adafactor variant with both single and multi tensor implementations.
Adapted from https://github.com/google-research/big_vision by Ross Wightman
"""
def __init__(
self,
params: ParamsT,
lr: float = 1.0,
min_dim_size_to_factor: int = 16,
decay_rate: float = 0.8,
decay_offset: int = 0,
beta2_cap: float = 0.999,
momentum: Optional[float] = 0.9,
momentum_dtype: Union[str, torch.dtype] = torch.bfloat16,
eps: Optional[float] = None,
weight_decay: float = 0.0,
clipping_threshold: Optional[float] = None,
unscaled_wd: bool = False,
caution: bool = False,
*,
foreach: Optional[bool] = False,
):
if isinstance(momentum_dtype, str):
if momentum_dtype == 'float16':
momentum_dtype = torch.float16
elif momentum_dtype == 'bfloat16':
momentum_dtype = torch.bfloat16
else:
assert momentum_dtype == 'float32', f'{momentum_dtype} dtype not supported'
momentum_dtype = torch.float32
# FIXME try to check if momentum dtype is appropriate for device? Torch API not great for this.
defaults = dict(
lr=lr,
min_dim_size_to_factor=min_dim_size_to_factor,
decay_rate=decay_rate,
decay_offset=decay_offset,
beta2_cap=beta2_cap,
momentum=momentum,
momentum_dtype=momentum_dtype,
eps=eps,
weight_decay=weight_decay,
clipping_threshold=clipping_threshold,
unscaled_wd=unscaled_wd,
caution=caution,
foreach=foreach,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault('caution', False)
group.setdefault('foreach', None)
for p in group['params']:
p_state = self.state.get(p, {})
if len(p_state) != 0 and not torch.is_tensor(p_state['step']):
p_state['step'] = torch.tensor(float(p_state['step']), dtype=_get_scalar_dtype())
if 'exp_avg' in p_state and torch.is_tensor(p_state['exp_avg']):
# FIXME this is a bit of a hack, optimizer.load_state_dict appears to upcast
# the momentum to float32 (it's half precision in the state_dict), need to
# look into this further. Better to override _process_value_according_to_param_policy?
p_state['exp_avg'] = p_state['exp_avg'].to(dtype=self.defaults['momentum_dtype'])
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avg_sq_rs = []
exp_avg_sq_cs = []
exp_avg_sqs = []
state_steps = []
exp_avgs = [] # For momentum
for p in group['params']:
if p.grad is None:
continue
if p.grad.is_sparse:
raise RuntimeError("Sparse gradients not supported")
params_with_grad.append(p)
grads.append(p.grad)
state = self.state[p]
if len(state) == 0:
# NOTE step on CPU, probably need some more though to make capturable
state['step'] = torch.tensor(0.0, dtype=_get_scalar_dtype())
shape = p.grad.shape
factored_dims = _factored_dims(
shape,
factored=True,
min_dim_size_to_factor=self.defaults['min_dim_size_to_factor']
)
if factored_dims is not None:
dc, dr = factored_dims
row_shape = list(p.grad.shape)
row_shape[dr] = 1
col_shape = list(p.grad.shape)
col_shape[dc] = 1
state['exp_avg_sq_r'] = p.grad.new_zeros(row_shape)
state['exp_avg_sq_c'] = p.grad.new_zeros(col_shape)
else:
state['exp_avg_sq'] = torch.zeros_like(p.grad, memory_format=torch.preserve_format)
if self.defaults['momentum'] is not None:
state['exp_avg'] = torch.zeros_like(p.grad, dtype=self.defaults['momentum_dtype'])
state_steps.append(state['step'])
exp_avg_sq_rs.append(state.get('exp_avg_sq_r', None))
exp_avg_sq_cs.append(state.get('exp_avg_sq_c', None))
exp_avg_sqs.append(state.get('exp_avg_sq', None))
exp_avgs.append(state.get('exp_avg', None))
if group['foreach']:
func = _multi_tensor_adafactor
else:
func = _single_tensor_adafactor
func(
params=params_with_grad,
grads=grads,
exp_avg_sq_rs=exp_avg_sq_rs,
exp_avg_sq_cs=exp_avg_sq_cs,
exp_avg_sqs=exp_avg_sqs,
exp_avgs=exp_avgs,
state_steps=state_steps,
beta2_decay=group['decay_rate'],
beta2_cap=group['beta2_cap'],
min_dim_size_to_factor=group['min_dim_size_to_factor'],
eps=group['eps'],
lr=group['lr'],
weight_decay=group['weight_decay'],
momentum=group['momentum'],
momentum_dtype=group['momentum_dtype'],
clipping_threshold=group['clipping_threshold'],
unscaled_wd=group['unscaled_wd'],
caution=group['caution'],
)
return loss
def _single_tensor_adafactor(
params: List[Tensor],
grads: List[Tensor],
exp_avg_sq_rs: List[Optional[Tensor]],
exp_avg_sq_cs: List[Optional[Tensor]],
exp_avg_sqs: List[Optional[Tensor]],
exp_avgs: List[Optional[Tensor]],
state_steps: List[Tensor],
*,
beta2_decay: float,
beta2_cap: float,
min_dim_size_to_factor: int,
eps: float,
lr: float,
weight_decay: float,
momentum: Optional[float],
momentum_dtype: Union[str, torch.dtype],
clipping_threshold: Optional[float],
unscaled_wd: bool,
caution: bool,
):
for i, param in enumerate(params):
grad = grads[i]
exp_avg_sq_r = exp_avg_sq_rs[i]
exp_avg_sq_c = exp_avg_sq_cs[i]
exp_avg_sq = exp_avg_sqs[i]
exp_avg = exp_avgs[i]
step_t = state_steps[i]
if eps is None:
# default eps for avoiding div by zero, diff from float type eps
eps = 1e-7 if grad.dtype == torch.float16 else 1e-30
# Update step
step_t += 1
beta2_t = min(beta2_cap, 1.0 - float(step_t) ** (-beta2_decay))
one_minus_beta2_t = 1 - beta2_t
grad_sqr = torch.square(grad) + eps
# NOTE application of eps (epsilon1) mirrors the optax/big vision/t5x approach
if exp_avg_sq is None:
# factorized second moment
dc, dr = _factored_dims(grad.shape, True, min_dim_size_to_factor=min_dim_size_to_factor)
exp_avg_sq_r.lerp_(grad_sqr.mean(dim=dr, keepdim=True), one_minus_beta2_t)
exp_avg_sq_c.lerp_(grad_sqr.mean(dim=dc, keepdim=True), one_minus_beta2_t)
reduce_dc = dc - 1 if dc > dr else dc
row_col_mean = exp_avg_sq_r.mean(dim=reduce_dc, keepdim=True)
row_factor = (exp_avg_sq_r / row_col_mean).rsqrt()
col_factor = exp_avg_sq_c.rsqrt()
update = grad * row_factor * col_factor
else:
# non-factorized second moment
assert exp_avg_sq_r is None and exp_avg_sq_c is None
exp_avg_sq.lerp_(grad_sqr, one_minus_beta2_t)
update = grad * exp_avg_sq.rsqrt()
# Clip by RMS value
if clipping_threshold is not None:
denom = (update.norm(2) / ((update.numel() ** 0.5) / clipping_threshold)).clamp_(max=1.0)
update.div_(denom)
# Apply momentum (in different dtype)
if momentum is not None and exp_avg is not None:
if momentum_dtype != grad.dtype:
exp_avg.lerp_(update.to(momentum_dtype), 1 - momentum) # ema
update = exp_avg.to(grad.dtype)
else:
exp_avg.lerp_(update, 1 - momentum) # ema
update = exp_avg.clone()
if caution:
# apply caution as per 'Cautious Optimizers': https://arxiv.org/abs/2411.16085
mask = (update * grad > 0).to(grad.dtype)
mask.div_(mask.mean().clamp_(min=1e-3))
update.mul_(mask)
# Scale by learning rate
update.mul_(lr)
# Perform weight decay
if weight_decay != 0:
if unscaled_wd:
# match big vision impl, 'fully decoupled' decay w/o LR scaling
param.mul_(1. - weight_decay)
else:
# match typical pytorch behaviour for decoupled decay, eg adamw where wd is scaled by LR
param.mul_(1. - lr * weight_decay)
# Update parameters
param.add_(update, alpha=-1.0)
def _multi_tensor_adafactor(
params: List[Tensor],
grads: List[Tensor],
exp_avg_sq_rs: List[Optional[Tensor]],
exp_avg_sq_cs: List[Optional[Tensor]],
exp_avg_sqs: List[Optional[Tensor]],
exp_avgs: List[Optional[Tensor]],
state_steps: List[Tensor],
*,
beta2_decay: float,
beta2_cap: float,
min_dim_size_to_factor: int,
eps: float,
lr: float,
weight_decay: float,
momentum: Optional[float],
momentum_dtype: Union[str, torch.dtype],
clipping_threshold: Optional[float],
unscaled_wd: bool,
caution: bool,
):
# FIXME TODO
assert False, 'multi-tensor fn (foreach=True) not implemented yet'
|