File size: 6,392 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import torch
import torch.nn as nn
import torch.optim as optim
from ding.torch_utils.optimizer_helper import Adam, RMSprop, calculate_grad_norm, \
calculate_grad_norm_without_bias_two_norm, PCGrad, configure_weight_decay
import pytest
import time
class LinearNet(nn.Module):
def __init__(self, features_in=1, features_out=1):
super().__init__()
self.linear = nn.Linear(features_in, features_out)
self._init_weight()
def forward(self, x):
return self.linear(x)
def _init_weight(self):
nn.init.constant_(self.linear.weight, val=1)
nn.init.constant_(self.linear.bias, val=0)
def try_optim_with(tname, t, optim_t):
net = LinearNet()
mse_fn = nn.L1Loss()
if tname == 'grad_clip':
if optim_t == 'rmsprop':
optimizer = RMSprop(
net.parameters(),
grad_clip_type=t,
clip_value=0.000001,
clip_norm_type=1.2,
lr=0.1,
clip_momentum_timestep=2,
ignore_momentum_timestep=2,
clip_coef=0.5
)
else:
optimizer = Adam(
net.parameters(),
grad_clip_type=t,
clip_value=0.000001,
clip_norm_type=1.2,
lr=0.1,
optim_type=optim_t,
clip_momentum_timestep=2,
ignore_momentum_timestep=2,
clip_coef=0.5
)
if tname == 'grad_ignore':
if optim_t == 'rmsprop':
optimizer = RMSprop(
net.parameters(),
grad_ignore_type=t,
clip_value=0.000001,
ignore_value=0.000001,
ignore_norm_type=1.2,
lr=0.1,
clip_momentum_timestep=2,
ignore_momentum_timestep=2,
)
else:
optimizer = Adam(
net.parameters(),
grad_ignore_type=t,
clip_value=0.000001,
ignore_value=0.000001,
ignore_norm_type=1.2,
lr=0.1,
optim_type=optim_t,
clip_momentum_timestep=2,
ignore_momentum_timestep=2,
ignore_coef=0.01
)
# 网络输入和标签
x = torch.FloatTensor([120])
x.requires_grad = True
target_value = torch.FloatTensor([2])
target_value.requires_grad = True
# loss计算
for _ in range(10):
predict = net(x)
loss = mse_fn(predict, target_value)
loss.backward()
optimizer.step()
if t is not None and 'ignore' not in t:
assert optimizer.get_grad() != 0.
for _ in range(10):
target_value = torch.FloatTensor([_ ** 2])
target_value.requires_grad = True
predict = net(x)
loss = mse_fn(predict, target_value)
loss.backward()
optimizer.step()
if t is None:
print("weight without optimizer clip:" + str(net.linear.weight))
else:
print("weight with optimizer {} of type: {} is ".format(tname, t) + str(net.linear.weight))
weight = net.linear.weight
return weight
@pytest.mark.unittest
class TestAdam:
def test_naive(self):
support_type = {
'optim': ['adam', 'adamw'],
'grad_clip': [None, 'clip_momentum', 'clip_value', 'clip_norm', 'clip_momentum_norm'],
'grad_norm': [None],
'grad_ignore': [None, 'ignore_momentum', 'ignore_value', 'ignore_norm', 'ignore_momentum_norm'],
}
for optim_t in support_type['optim']:
for tname in ['grad_clip', 'grad_ignore']:
for t in support_type[tname]:
try_optim_with(tname=tname, t=t, optim_t=optim_t)
@pytest.mark.unittest
class TestRMSprop:
def test_naive(self):
support_type = {
'grad_clip': [None, 'clip_momentum', 'clip_value', 'clip_norm', 'clip_momentum_norm'],
'grad_norm': [None],
'grad_ignore': [None, 'ignore_momentum', 'ignore_value', 'ignore_norm', 'ignore_momentum_norm'],
}
for tname in ['grad_clip', 'grad_ignore']:
for t in support_type[tname]:
try_optim_with(tname=tname, t=t, optim_t='rmsprop')
@pytest.mark.unittest
class Test_calculate_grad_norm_with_without_bias:
def test_two_functions(self):
net = LinearNet()
mse_fn = nn.L1Loss()
optimizer = Adam(net.parameters(), )
x = torch.FloatTensor([120])
x.requires_grad = True
target_value = torch.FloatTensor([2])
target_value.requires_grad = True
for _ in range(10):
predict = net(x)
loss = mse_fn(predict, target_value)
loss.backward()
optimizer.step()
inf_norm = calculate_grad_norm(model=net, norm_type='inf')
two_norm = calculate_grad_norm(model=net)
two_norm_nobias = float(calculate_grad_norm_without_bias_two_norm(model=net))
one_norm = calculate_grad_norm(model=net, norm_type=1)
assert isinstance(two_norm, float)
assert isinstance(inf_norm, float)
assert isinstance(one_norm, float)
assert isinstance(two_norm_nobias, float)
@pytest.mark.unittest
class TestPCGrad:
def naive_test(self):
x, y = torch.randn(2, 3), torch.randn(2, 4)
net = LinearNet(3, 4)
y_pred = net(x)
pc_adam = PCGrad(optim.Adam(net.parameters()))
pc_adam.zero_grad()
loss1_fn, loss2_fn = nn.L1Loss(), nn.MSELoss()
loss1, loss2 = loss1_fn(y_pred, y), loss2_fn(y_pred, y)
pc_adam.pc_backward([loss1, loss2])
for p in net.parameters():
assert isinstance(p, torch.Tensor)
@pytest.mark.unittest
class TestWeightDecay:
def test_wd(self):
net = nn.Sequential(nn.Linear(3, 4), nn.LayerNorm(4))
x = torch.randn(1, 3)
group_params = configure_weight_decay(model=net, weight_decay=1e-4)
assert group_params[0]['weight_decay'] == 1e-4
assert group_params[1]['weight_decay'] == 0
assert len(group_params[0]['params']) == 1
assert len(group_params[1]['params']) == 3
opt = Adam(group_params, lr=1e-2)
opt.zero_grad()
y = torch.sum(net(x))
y.backward()
opt.step()
|