Differences between torch.optim.AdamW and mindspore.nn.AdamWeightDecay

torch.optim.AdamW

class torch.optim.AdamW(
    params,
    lr=0.001,
    betas=(0.9, 0.999),
    eps=1e-08,
    weight_decay=0.01,
    amsgrad=False
)

For more information, see torch.optim.AdamW.

mindspore.nn.AdamWeightDecay

class mindspore.nn.AdamWeightDecay(
    params,
    learning_rate=1e-3,
    beta1=0.9,
    beta2=0.999,
    eps=1e-6,
    weight_decay=0.0
)

For more information, see mindspore.nn.AdamWeightDecay.

Differences

Optimizers in PyTorch and MindSpore implement different algorithms. Please refer to the official website for more details on the formulas.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	params	params	Consistent function
	Parameter 2	lr	learning_rate	Same function, different parameter names and default values
	Parameter 3	betas	beta1, beta2	Same function, different parameter names
	Parameter 4	eps	eps	Same function, different default values
	Parameter 5	weight_decay	weight_decay	Consistent function
	Parameter 6	amsgrad	-	PyTorch amsgrad indicates whether to apply the amsgrad algorithm, and MindSpore does not have this parameter

Code Example

# MindSpore
import mindspore
from mindspore import nn

net = nn.Dense(2, 3)
optimizer = nn.AdamWeightDecay(net.trainable_params())
criterion = nn.MAELoss(reduction="mean")

def forward_fn(data, label):
    logits = net(data)
    loss = criterion(logits, label)
    return loss, logits

grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)

def train_step(data, label):
    (loss, _), grads = grad_fn(data, label)
    optimizer(grads)
    return loss

# PyTorch
import torch

model = torch.nn.Linear(2, 3)
criterion = torch.nn.L1Loss(reduction='mean')
optimizer = torch.optim.AdamW(model.parameters())
def train_step(data, label):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, label)
    loss.backward()
    optimizer.step()