diffusers/src/diffusers/optimization.py at main · AndrewMohawk/diffusers · GitHub

History

275 lines (222 loc) · 11.1 KB

Raw

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

# coding=utf-8

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

"""PyTorch optimization for diffusion models."""

import math

from enum import Enum

from typing import Optional, Union

from torch.optim import Optimizer

from torch.optim.lr_scheduler import LambdaLR

from .utils import logging

logger = logging.get_logger(__name__)

class SchedulerType(Enum):

LINEAR = "linear"

COSINE = "cosine"

COSINE_WITH_RESTARTS = "cosine_with_restarts"

POLYNOMIAL = "polynomial"

CONSTANT = "constant"

CONSTANT_WITH_WARMUP = "constant_with_warmup"

def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):

"""

Create a schedule with a constant learning rate, using the learning rate set in optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)

def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):

"""

Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate

increases linearly between 0 and the initial lr set in the optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

num_warmup_steps (`int`):

The number of steps for the warmup phase.

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

def lr_lambda(current_step: int):

if current_step < num_warmup_steps:

return float(current_step) / float(max(1.0, num_warmup_steps))

return 1.0

return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)

def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):

"""

Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after

a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

num_warmup_steps (`int`):

The number of steps for the warmup phase.

num_training_steps (`int`):

The total number of training steps.

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

def lr_lambda(current_step: int):

if current_step < num_warmup_steps:

return float(current_step) / float(max(1, num_warmup_steps))

return max(

0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))

)

return LambdaLR(optimizer, lr_lambda, last_epoch)

def get_cosine_schedule_with_warmup(

optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1

"""

Create a schedule with a learning rate that decreases following the values of the cosine function between the

initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the

initial lr set in the optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

num_warmup_steps (`int`):

The number of steps for the warmup phase.

num_training_steps (`int`):

The total number of training steps.

num_cycles (`float`, *optional*, defaults to 0.5):

The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0

following a half-cosine).

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

def lr_lambda(current_step):

if current_step < num_warmup_steps:

return float(current_step) / float(max(1, num_warmup_steps))

progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))

return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))

return LambdaLR(optimizer, lr_lambda, last_epoch)

def get_cosine_with_hard_restarts_schedule_with_warmup(

optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1

"""

Create a schedule with a learning rate that decreases following the values of the cosine function between the

initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases

linearly between 0 and the initial lr set in the optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

num_warmup_steps (`int`):

The number of steps for the warmup phase.

num_training_steps (`int`):

The total number of training steps.

num_cycles (`int`, *optional*, defaults to 1):

The number of hard restarts to use.

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

def lr_lambda(current_step):

if current_step < num_warmup_steps:

return float(current_step) / float(max(1, num_warmup_steps))

progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))

if progress >= 1.0:

return 0.0

return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))

return LambdaLR(optimizer, lr_lambda, last_epoch)

def get_polynomial_decay_schedule_with_warmup(

optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1

"""

Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the

optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the

initial lr set in the optimizer.

Args:

optimizer ([`~torch.optim.Optimizer`]):

The optimizer for which to schedule the learning rate.

num_warmup_steps (`int`):

The number of steps for the warmup phase.

num_training_steps (`int`):

The total number of training steps.

lr_end (`float`, *optional*, defaults to 1e-7):

The end LR.

power (`float`, *optional*, defaults to 1.0):

Power factor.

last_epoch (`int`, *optional*, defaults to -1):

The index of the last epoch when resuming training.

Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT

implementation at

https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37

Return:

`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.

"""

lr_init = optimizer.defaults["lr"]

if not (lr_init > lr_end):

raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")

def lr_lambda(current_step: int):

if current_step < num_warmup_steps:

return float(current_step) / float(max(1, num_warmup_steps))

elif current_step > num_training_steps:

return lr_end / lr_init # as LambdaLR multiplies by lr_init

else:

lr_range = lr_init - lr_end

decay_steps = num_training_steps - num_warmup_steps

pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps

decay = lr_range * pct_remaining**power + lr_end

return decay / lr_init # as LambdaLR multiplies by lr_init

return LambdaLR(optimizer, lr_lambda, last_epoch)

TYPE_TO_SCHEDULER_FUNCTION = {

SchedulerType.LINEAR: get_linear_schedule_with_warmup,

SchedulerType.COSINE: get_cosine_schedule_with_warmup,

SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,

SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,

SchedulerType.CONSTANT: get_constant_schedule,

SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,

}

def get_scheduler(

name: Union[str, SchedulerType],

optimizer: Optimizer,

num_warmup_steps: Optional[int] = None,

num_training_steps: Optional[int] = None,

"""

Unified API to get any scheduler from its name.

Args:

name (`str` or `SchedulerType`):

The name of the scheduler to use.

optimizer (`torch.optim.Optimizer`):

The optimizer that will be used during training.

num_warmup_steps (`int`, *optional*):

The number of warmup steps to do. This is not required by all schedulers (hence the argument being

optional), the function will raise an error if it's unset and the scheduler type requires it.

num_training_steps (`int``, *optional*):

The number of training steps to do. This is not required by all schedulers (hence the argument being

optional), the function will raise an error if it's unset and the scheduler type requires it.

"""

name = SchedulerType(name)

schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]

if name == SchedulerType.CONSTANT:

return schedule_func(optimizer)

# All other schedulers require `num_warmup_steps`

if num_warmup_steps is None:

raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")

if name == SchedulerType.CONSTANT_WITH_WARMUP:

return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)

# All other schedulers require `num_training_steps`

if num_training_steps is None:

raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")

return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)

diffusers/src/diffusers/optimization.py at main · AndrewMohawk/diffusers · GitHub

Expand file tree

Breadcrumbs

optimization.py

Latest commit

History

Breadcrumbs

optimization.py

File metadata and controls