Recompute: Training with bigger batch size¶
As the amount of training data increases, training deeper neural network models becomes more and more popular. Current deep-learning training usually keeps the hidden layer outputs in memory during the forward propagation, and the number of outputs increases linearly with the increase of the number of model layers, which becomes a challenge of the memory size for common devices.
As we know, a training process of a deep-learning network contains 3 steps:
Forward Propagation：Running forward operators and generate temporary variables as output
Backward Propagation：Running backward operators to compute gradients of parameters
Optimization：Applying optimization algorithm to update parameters
When the model becomes deeper, the number of temporary variables generated in the forward propagation process can reach tens of thousands, occupying a large amount of memory. The Garbage Collection mechanism in Paddle can delete useless variables for the sake of saving memory. However, some variables serve as inputs of backward operators, they must be kept in memory until particular operator finish.
Take a simple example, define a network contains two mul operators, the forward propagation works as follows:
where \(x, y, z\) are vectors， \(W_1, W_2\) are matrix。It is easy to conduct that the gradient of \(W_2\) is:
We can see that \(y\) is used in the backward propagation process, thus it must be kept in the memory during the whole forward propagation. When network grows deeper, more ‘y’s need to be stored, adding more requirements to the memory.
Forward Recomputation Backpropagation(FRB) splits a deep network to k segments. For each segment, in forward propagation, most of the temporary variables are erased in time, except for some special variables (we will talk about that later); in backward propagation, the forward operators will be recomputed to get these temporary variables before running backward operators. In short, FBR runs forward operators twice.
But how to split the network? A deep learning network usually consists of connecting modules in series: ResNet-50 contains 16 blocks and Bert-Large contains 24 transformers. It is a good choice to treat such modules as segments. The variables among segments are called as checkpoints.
The following picture is a network with 4 fc layers, 3 relu layers, 1 sigmoid layer and 1 log-loss layer in series. The left column is the forward propagation, the middle column is the normal backward propagation, and the right column is the FRB. Rectangular boxes represent the operators, red dots represent the intermediate variables in forward computation, blue dots represent checkpoints and arrows represent the dependencies between operators.
Note: the complete source code of this example: source
After applying FBR, the forward computation only needs to store 2 variables (the blue dots) instead of 4 variables (the red dots), saving the corresponding memories. It is notable that recomputing operators generate new intermediate variables at the same time, a trade-off needs to be considered in this situation. While according to our experiments, FBR usually saves rather than increase the memory load.
There are 2 methods to apply RecomputeOptimizer in your Paddle program: call RecomputeOptimizer directly or use it with Fleet API. For single-GPU card training or CPU training, we recommend directly calling; For multi-GPU training, we recommend using with Fleet API.
1. Directly calling
Calling RecomputeOptimizer is very easy: first, define a classic optimizer, such as Adam; second, wrap it with RecomputeOptimizer; third, set the checkpoints.
import paddle.fluid as fluid # Define the network def mlp(input_x, input_y, hid_dim=128, label_dim=2): print(input_x) fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=, dtype='float32') input_y = fluid.layers.data(name="y", shape=, dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) # define RecomputeOptimizer sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) # set checkpoints sgd._set_checkpoints([fc_1, pred]) # apply optimization sgd.minimize(cost)
In principle, recompute is for all kinds of optimizers in Paddle.
2. Using Recompute in Fleet API
Fleet API is a high-level API for distributed training in Fluid. Adding RecomputeOptimizer to Fluid takes two steps:
set dist_strategy.forward_recompute to True
from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy dist_strategy = DistributedStrategy() dist_strategy.forward_recompute = True dist_strategy.recompute_checkpoints=checkpoints optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) optimizer.minimize(loss)
We supply some examples of using recompute in Fleet API for users. We also post corresponding training speed, test results and memory usages of these examples for reference.
Fine-tuning Bert Large model with recomputing: source
Training object detection models with recomputing：developing.
Does RecomputeOptimizer support operators with random outputs?
We currently found that the dropout operator has random results and RecomputeOptimizer is able to keep the outputs of first-computation and recomputation consistent.
Are there more official examples of Recompute?
More examples will be updated at examples
and Fleet . Feel free to raise issues if you get any problem with these examples.
How should I set checkpoints?
The position of checkpoints is important: we suggest setting the variable between the sub-model as checkpoints, that is, set a variable as a checkpoint if it can separate the network into two parts without short-cut connections. The number of checkpoints is also important: too few checkpoints will reduce the memory saved by recomputing while too many checkpoints will occupy a lot of memory themselves. We will add a tool to estimate the memory usage with specific checkpoints, helping users to choose checkpointing variables.
 Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin . Training deep nets with sublinear memory cost. arXiv preprint, arXiv:1604.06174, 2016.
 Audrunas Gruslys , Rémi Munos , Ivo Danihelka , Marc Lanctot , and Alex Graves. Memory efficient backpropagation through time. In Advances in Neural Information Processing Systems (NIPS), pages 4125 4133, 2016.
 Kusumoto, Mitsuru, et al. “A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation.” arXiv preprint arXiv:1905.11722 (2019).