Differences between torch.optim.AdaMax and mindspore.nn.AdaMax

torch.optim.AdaMax

class torch.optim.AdaMax(
    params,
    lr=0.002,
    betas=(0.9, 0.999),
    eps=1e-08,
    weight_decay=0
)

For more information, see torch.optim.AdaMax.

mindspore.nn.AdaMax

class mindspore.nn.AdaMax(
    params,
    learning_rate=0.001,
    beta1=0.9,
    beta2=0.999,
    eps=1e-08,
    weight_decay=0.0,
    loss_scale=1.0
)

For more information, see mindspore.nn.AdaMax.

Differences

PyTorch and MindSpore implement different algorithms for this optimizer. Please refer to the formula on the official website for details.

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	Consistent function
	Parameter 5	weight_decay	weight_decay	Consistent function
	Parameter 6	-	loss_scale	MindSpore loss_scale is the gradient scaling factor, and PyTorch does not have this parameter

Code Example

# MindSpore
import mindspore
from mindspore import nn

net = nn.Dense(2, 3)
optimizer = nn.AdaMax(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.AdaMax(model.parameters())
def train_step(data, label):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, label)
    loss.backward()
    optimizer.step()