The differences of inputs setting with torch.optim.optimizer

torch.optim.Optimizer

class torch.optim.Optimizer(
    params,
    defaults
)

For more information, see torch.optim.Optimizer.

mindspore.nn.Optimizer

class mindspore.nn.Optimizer(
    learning_rate,
    parameters,
    weight_decay=0.0,
    loss_scale=1.0
)

For more information, see mindspore.nn.Optimizer.

Differences

Parameters Setting

• Default function interface

MindSpore: The parameter params of the optimizer is configured by directly using the trainable_params method.

from mindspore import nn

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3)
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
    def construct(self, x):
        x = self.conv(x)
        x = self.bn(x)
        out = self.relu(x)
        return out

net = Net()
optim_sgd = nn.SGD(params=net.trainable_params())

PyTorch: The parameter params of the optimizer is configured by directly using the parameters method.

from torch import optim
import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3)
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

net = Net()
optim_sgd = optim.SGD(params=net.parameters(), lr=0.01)

• User-defined input parameters

MindSpore: Firstly, get all the parameters in the network by get_parameters method, then filter parameters under certain conditions, like names of them, and pass it to the optimizer.

from mindspore import nn

net = Net()
all_params = net.get_parameters()
no_conv_params = list(filter(lambda x: "conv" not in x.name, all_params))
optim_sgd = nn.SGD(no_conv_params)

PyTorch: Firstly, get all the parameters in the network by named_parameters method, then filter parameters under certain conditions, like names of them, and pass it to the optimizer.

from torch import optim

net = Net()
all_params = net.named_parameters()
no_conv_params = []
for pname, p in all_params:
    if "conv" not in pname:
        no_conv_params.append(p)
optim_sgd = optim.SGD(no_conv_params, lr=0.01)

learning_rate Setting

fix learning rate: same.

dynamic learning rate:

MindSpore:

from mindspore import nn
import mindspore as ms

# dynamic_lr
milestone = [2, 5, 10]
learning_rates = [0.1, 0.05, 0.01]
lr_dynamic = nn.dynamic_lr.piecewise_constant_lr(milestone, learning_rates)
print(lr_dynamic)

# learning_rate_schedule
lr_schedule = nn.learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1,
                                   end_learning_rate=0.01,
                                   decay_steps=4,
                                   power=0.5 )

global_step = ms.Tensor(2, ms.int32)
result = lr_schedule(global_step)
print(result)

# lr as input of optimizer
optimizer1 = nn.Momentum(net.trainable_params(), learning_rate=lr_dynamic, momentum=0.9, weight_decay=0.9)
optimizer2 = nn.Momentum(net.trainable_params(), learning_rate=lr_schedule, momentum=0.9, weight_decay=0.9)

[0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
0.0736396

PyTorch：

from torch import optim
import numpy as np

optimizer = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
loss_fn = torch.nn.MSELoss()
dataset = [(torch.tensor(np.random.rand(1, 3, 64, 32).astype(np.float32)),
            torch.tensor(np.random.rand(1, 64, 62, 30).astype(np.float32)))]
for epoch in range(5):
    for input, target in dataset:
        optimizer.zero_grad()
        output = net(input)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()
    scheduler.step()
    print(scheduler.get_last_lr())

[0.09000000000000001]
[0.08100000000000002]
[0.07290000000000002]
[0.06561000000000002]
[0.05904900000000002]

weight_decay Setting

Same.

loss_scale Setting

• mindspore: As an input parameter of the optimizer, it should be used with FixedLossScaleManager.

• pytorch: Do not set the mixed precision separately for the optimizer.

Groups of Parameters

Both PyTorch and MindSpore support grouping parameters and can be used in similar ways. But mindspore only supports groups for ‘params’, ‘weight_decay’, ‘lr’, ‘grad_centralizaiton’, while pytorch supports groups for all optimizer inputs.

Currently, there are individual optimizers in Mindspore and pytorch that do not support grouping parameters. For details, refer to the instructions of each optimizer.

MindSpore:

from mindspore import nn

net = Net()

conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
no_conv_params = list(filter(lambda x: "conv" not in x.name, net.trainable_params()))

fix_lr = 0.01
polynomial_decay_lr = nn.learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1,
                                   end_learning_rate=0.01,
                                   decay_steps=4,
                                   power=0.5 )

group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': fix_lr},
                {'params': no_conv_params, 'lr': polynomial_decay_lr},
                {'order_params': net.trainable_params()}]

optim_sgd = nn.SGD(group_params, learning_rate=0.1)

PyTorch:

from torch import optim

net = Net()

all_params = net.parameters()
conv_params = []
no_conv_params = []

for pname, p in net.named_parameters():
    if 'conv' in pname:
        conv_params += [p]
    else:
        no_conv_params += [p]

group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': 0.01},
                {'params': no_conv_params, 'nesterov': True}]

optim_sgd = optim.SGD(group_params, lr=0.01)