Differences with torch.optim.SparseAdam
torch.optim.SparseAdam
class torch.optim.SparseAdam(
params,
lr=0.001,
betas=(0.9, 0.999),
eps=1e-08
)
For more information, see torch.optim.SparseAdam.
mindspore.nn.LazyAdam
class mindspore.nn.LazyAdam(
params,
learning_rate=1e-3,
beta1=0.9,
beta2=0.999,
eps=1e-8,
use_locking=False,
use_nesterov=False,
weight_decay=0.0,
loss_scale=1.0
)
For more information, see mindspore.nn.LazyAdam.
Differences
torch.optimize.SparseAdam
is an Adam algorithm in PyTorch specifically for sparse scenarios.
mindspore.nn.LazyAdam
can be used for both regular and sparse scenarios:
mindspore.nn.LazyAdam
is consistent withtorch.optimize.SparseAdam
with default parameters when the input gradient is a sparse Tensor, butmindspore.nn.LazyAdam
currently only supports CPU backends;When the input gradient is a non-sparse Tensor,
mindspore.nn.LazyAdam
automatically executes themindspore.nn.Adam
algorithm, and supports CPU/GPU/Ascend backends.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
params |
params |
Consistent function |
Parameter 2 |
lr |
learning_rate |
Same function, different parameter names |
|
Parameter 3 |
betas |
beta1, beta2 |
Same function, different parameter names |
|
Parameter 4 |
eps |
eps |
Consistent function |
|
Parameter 5 |
- |
use_locking |
MindSpore |
|
Parameter 6 |
- |
use_nesterov |
Whether MindSpore |
|
Parameter 7 |
- |
weight_decay |
PyTorch does not have this parameter |
|
Parameter 8 |
- |
loss_scale |
MindSpore's |
Code Example
# MindSpore.
import mindspore
from mindspore import nn
net = nn.Dense(2, 3)
optimizer = nn.LazyAdam(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.SparseAdam(model.parameters())
def train_step(data, label):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()