def _optimizer(c: Configs):
"""
Create configurable optimizer
"""
opt_conf = OptimizerConfigs()
opt_conf.parameters = c.model.parameters()
return opt_conf
def _optimizer(c: Configs):
optimizer = OptimizerConfigs()
optimizer.parameters = c.model.parameters()
optimizer.optimizer = 'Adam'
optimizer.d_model = c.d_model
return optimizer
def _optimizer(c: NLPAutoRegressionConfigs):
optimizer = OptimizerConfigs()
optimizer.parameters = c.model.parameters()
optimizer.optimizer = 'Adam'
optimizer.d_model = c.d_model
return optimizer
def _optimizer(c: MNISTConfigs):
"""
### Default optimizer configurations
"""
opt_conf = OptimizerConfigs()
opt_conf.parameters = c.model.parameters()
opt_conf.optimizer = 'Adam'
return opt_conf
def _optimizer(c: Configs):
"""
Create a configurable optimizer.
We can change the optimizer type and hyper-parameters using configurations.
"""
opt_conf = OptimizerConfigs()
opt_conf.parameters = c.model.parameters()
return opt_conf
def _optimizer(c: NLPAutoRegressionConfigs):
"""
### Default [optimizer configurations](../optimizers/configs.html)
"""
optimizer = OptimizerConfigs()
optimizer.parameters = c.model.parameters()
optimizer.optimizer = 'Adam'
optimizer.d_model = c.d_model
return optimizer
def transformer_optimizer(c: NLPAutoRegressionConfigs):
"""
### Create custom optimizer with weight decay
This code is taken from [minGPT](https://github.com/karpathy/minGPT).
This applies weight decay only to weights of linear layers.
"""
# Collect names of parameters to apply weight decay
decay = set()
for mn, m in c.model.named_modules():
for pn, p in m.named_parameters():
fpn = f'{mn}.{pn}' if mn else pn # full param name
if fpn.endswith('weight') and isinstance(m, nn.Linear):
decay.add(fpn)
# Get all the parameters
param_dict = {pn: p for pn, p in c.model.named_parameters()}
# Parameters that are not decayed
no_decay = set(param_dict.keys()) - decay
# create the pytorch optimizer object
opt_groups = [
{"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": c.weight_decay},
{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
# Create a [configurable optimizer](../optimizers/configs.html#OptimizerConfigs),
# so that we can change these simple by passing
# a config dictionary.
optimizer = OptimizerConfigs()
# Set parameter groups for optimization
optimizer.parameters = opt_groups
# Use [cosine decay optimizer](../optimizers/adam_warmup_cosine_decay.html)
# This is what GPT uses
optimizer.optimizer = 'AdamWarmupCosineDecay'
# Set model embedding size, required if we use [Noam optimizer](../optimizers/noam.html)
# which has an exponential decay
optimizer.d_model = c.d_model
# Set default weight decay.
# This is not required since we set the weight decay in the parameter groups
optimizer.weight_decay = c.weight_decay
# GPT uses a maximum learning rate of $6 \times 10^{-4}$
optimizer.learning_rate = 6e-4
# $\beta_1 = 0.9, \beta_2 = 0.95$
optimizer.betas = (0.9, 0.95)
# $\epsilon = 10^{-8}$
optimizer.eps = 1e-8
# Weight decay decoupled from gradients
optimizer.weight_decouple = True
# Total number of optimization steps for learning rate cosine decay
optimizer.total_steps = c.epochs * len(c.text.train) // (c.batch_size * c.seq_len)
# Number of warmup optimization steps
optimizer.warmup = c.warmup_steps // (c.batch_size * c.seq_len)
return optimizer
