recompute¶
重新计算中间激活函数值来节省显存。
参数¶
function (paddle.nn.Sequential) - 模型前向传播的部分连续的层函数组成的序列,它们的中间激活函数值将在前向传播过程中被释放掉来节省显存,并且在反向梯度计算的时候会重新被计算。
args (Tensor) - function 的输入。
kwargs (Dict) - kwargs 只应该包含 preserve_rng_state 的键值对,用来表示是否保存前向的 rng,如果为 True,那么在反向传播的重计算前向时会还原上次前向的 rng 值。默认 preserve_rng_state 为 True。
返回¶
function 作用在输入的输出
代码示例¶
import paddle
from paddle.distributed.fleet.utils import recompute
import random
# required: gpu
def get_fc_block(block_idx, input_size, is_last=False):
block_name = "block_" + str(block_idx)
block = paddle.nn.Sequential(
(block_name + "_fc_0", paddle.nn.Linear(input_size, input_size, bias_attr=False)),
(block_name + "_dropout", paddle.nn.Dropout(p=0.5)),
(block_name + "_relu_1", paddle.nn.ReLU()),
(block_name + "_fc_1", paddle.nn.Linear(input_size, input_size, bias_attr=False)),
(block_name + "_relu_2", paddle.nn.ReLU()),
)
if is_last:
block.add_sublayer(
block_name + "_fc_2",
paddle.nn.Linear(
input_size, 1, bias_attr=False
)
)
else:
block.add_sublayer(
block_name + "_fc_2",
paddle.nn.Linear(input_size, input_size, bias_attr=False)
)
return block
class Naive_fc_net(paddle.nn.Layer):
def __init__(self, input_size=10,
recompute_blocks=[1, 3],
recompute_kwargs={}):
super(Naive_fc_net, self).__init__()
self.recompute_blocks = recompute_blocks
self.recompute_kwargs = recompute_kwargs
self.runfunc0 = get_fc_block(0, input_size, is_last=False)
self.runfunc1 = get_fc_block(1, input_size, is_last=False)
self.runfunc2 = get_fc_block(2, input_size, is_last=False)
self.runfunc3 = get_fc_block(3, input_size, is_last=False)
self.runfunc4 = get_fc_block(4, input_size, is_last=True)
self.total_func = [self.runfunc0, self.runfunc1, self.runfunc2, self.runfunc3, self.runfunc4]
def forward(self, inputs):
nums = len(self.total_func)
for i in range(nums):
if i in self.recompute_blocks:
inputs = recompute(self.total_func[i], inputs, **{"preserve_rng_state": True})
else:
inputs = self.total_func[i](inputs)
return inputs
def run_model(cuda_state, recompute_block=[], recompute_kwargs={}):
gen = paddle.seed(10)
gen.manual_seed(10)
random.seed(10)
if cuda_state:
paddle.set_cuda_rng_state(cuda_state)
batch_size, input_size = 1, 10
model = Naive_fc_net(
input_size,
recompute_blocks=recompute_block,
recompute_kwargs=recompute_kwargs)
optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
loss_ = []
param_ = []
grad_ = []
for _ in range(5):
x = paddle.rand(shape=[batch_size, input_size], dtype="float32")
y_pred = model(x)
loss = y_pred.mean()
loss_.append(loss.item())
loss.backward()
optimizer.step()
param_.append(model.parameters()[9])
grad_.append(model.parameters()[3]._grad_ivar())
optimizer.clear_grad()
return loss_, param_, grad_
cuda_state = paddle.get_cuda_rng_state()
# without recompute
loss_ref, param_ref, grad_ref = run_model(
cuda_state, recompute_block=[]
)
loss, param, grad = run_model(cuda_state, recompute_block=[1, 2])
print("normal_loss: {}, recompute_loss: {}".format(loss_ref, loss))
# The result of the recompute_loss should be the same as the normal_loss.