Cuda Out of Memory
Barely a few steps through my forward propagation for an LSTM I received an error: <SCODE>THCudaCheck FAIL file=/home/soumith/local/builder/wheel/pytorch-src/torch/lib/THC/generic/THCStorage.cu line=66 error=2 : out of memory
Traceback (most recent call last):
  File "external_script.py", line 1002, in <module>
    final_loss = run()
  File "external_script.py", line 584, in run
    optimizer_iter_num, feature, x_dim)
  File "/home/ubuntu/lstm_special/rnn.py", line 74, in lstm_forward
    output, hn = self.net.rnn(input, (self.h, self.c))
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 210, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/rnn.py", line 79, in forward
    return func(input, self.all_weights, hx)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/rnn.py", line 228, in forward
    return func(input, *fargs, **fkwargs)
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/function.py", line 202, in _do_forward
    flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/function.py", line 218, in forward
    result = self.forward_extended(*nested_tensors)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/rnn.py", line 180, in forward_extended
    cudnn.rnn.forward(self, input, hx, weight, output, hy)
  File "/usr/local/lib/python2.7/dist-packages/torch/backends/cudnn/rnn.py", line 257, in forward
    fn.workspace = torch.cuda.ByteTensor(workspace_size.value)
RuntimeError: cuda runtime error (2) : out of memory at /home/soumith/local/builder/wheel/pytorch-src/torch/lib/THC/generic/THCStorage.cu:66
<ECODE> I’ve a 12gb Tesla K80 NVIDIA GPU so this shouldn’t be an issue I believe it has something to do with the variables. 12GB RAM. Checking CUDA is working. <SCODE>>>> torch.cuda.is_available()
True
>>> torch.cuda.current_stream()
<torch.cuda.Stream device=0 cuda_stream=0x0>
>>> torch.cuda.device_count()
1L
<ECODE> NVIDIA SMI <SCODE>+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.57                 Driver Version: 367.57                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 0000:00:1E.0     Off |                    0 |
| N/A   51C    P8    27W / 149W |      2MiB / 11439MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
<ECODE> Script <SCODE>from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib as mpl
import numpy as np

torch.manual_seed(0)


class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, bias, dropout):
        super(Net, self).__init__()
        self.rnn = nn.LSTM(input_size=input_size,
                           hidden_size=hidden_size,
                           num_layers=num_layers,
                           bias=bias,
                           dropout=dropout)


class lstmModule:
    def __init__(self, input_size, hidden_size, num_layers, bias, dropout,
                 seq_len, batch_size, meta_lr, n_meta_iter):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bias = bias
        self.dropout = dropout
        self.seq_len = seq_len
        self.batch_size = batch_size
        self.meta_lr = meta_lr
        self.n_meta_iter = n_meta_iter

        self.net = Net(input_size=input_size,
                       hidden_size=hidden_size,
                       num_layers=num_layers,
                       bias=bias,
                       dropout=dropout)

        self.net.cuda()

        self.h0 = Variable(torch.randn(self.num_layers,
                                       self.batch_size,
                                       self.hidden_size)).cuda()

        self.c0 = Variable(torch.randn(self.num_layers,
                                       self.batch_size,
                                       self.hidden_size)).cuda()

        self.optimizer = optim.Adam(self.net.rnn.parameters(), lr=self.meta_lr)

        self.loss_lst = []
        self.loss_lst2 = []

    def lstm_forward(self, seq_num, inp, x_dim):
        inp = inp.reshape(1, 1, inp.shape[0]*inp.shape[1])
        inp = torch.from_numpy(inp).float()
        input = Variable(inp).cuda()

        if seq_num == 0:
            # Ensure clear gradient buffer
            self.optimizer.zero_grad()
            self.loss_tot = [0 for i in range(self.hidden_size)]

            # LSTM
            output, hn = self.net.rnn(input, (self.h0, self.c0))
            output = torch.abs(2 * output)
            op = [output[:, :, i] for i in range(self.hidden_size)]
            self.output_all = op
            self.h, self.c = hn
            return output.cpu().data.numpy()
        else:
            output, hn = self.net.rnn(input, (self.h, self.c))
            output = torch.abs(2 * output)
            op = [output[:, :, i] for i in range(self.hidden_size)]
            self.h, self.c = hn
            self.output_all = [torch.cat((self.output_all[i], op[i]), 0) for i in range(self.hidden_size)]
            return output.cpu().data.numpy()

    def lstm_update(self, lab):
        def pseudo_loss(output, label):
            # print('output size', output.size())
            # print('label size', label.size())
            # return torch.mean(torch.abs(output*label))
            return torch.mean(output * label)

        lab = torch.from_numpy(lab).float()
        self.label = Variable(lab).cuda()

        # Get loss
        self.loss_tot = [
            self.loss_tot[i] + pseudo_loss(self.output_all[i], self.label[:, i]) for i in range(self.hidden_size)]

        # Append loss
        self.loss_lst.append(self.loss_tot[0].cpu().data.numpy()[0])
        self.loss_lst2.append(self.loss_tot[1].cpu().data.numpy()[0])

        # Backprop
        sum(self.loss_tot).backward()

        # Update optimizer
        self.optimizer.step()

        return self.loss_lst, self.loss_lst2
<ECODE>
ritchieng
apaszke
If you know you’re not going to use the intermediate Variables for backpropagation, it’s safer to keep references to tensors only.
apaszke
What do you mean by keeping references to tensors only?
ritchieng
When you do this: <SCODE>self.output_all = op
<ECODE> <SCODE>self.output_all = [o.data for o in op]
<ECODE> you’ll only save the tensors i.e. the final values.
apaszke
I’m starting to get the hang of how Variables work in PyTorch. Thanks. I’ll try it freeing some variables and let you know.
ritchieng
Tried your recommendation that made the process super fast but it prevented me from backpropagating because the resulting loss is not a variable type but a float type (no history). I was running the other CPU version with a larger dataset and this came out: <SCODE> File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/torch/optim/adam.py", line 52, in step
    state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_()
RuntimeError: $ Torch: not enough memory: you tried to allocate 7GB. Buy new RAM! at /data/users/soumith/miniconda2/conda-bld/pytorch-cuda80-0.1.8_1486039719409/work/torch/lib/TH/THGeneral.c:270
<ECODE> This is weird considering how I’ve more than 60GB RAM. May I know if PyTorch is limiting the amount of RAM somehow?
ritchieng
No, it means that the allocation has failed - you didn’t have enough free RAM at that moment. Since you’re running low even on CPU memory it doesn’t seem surprising for me that it also fails on the GPU. If you think there’s a bug, it would be very helpful if you could isolate the minimal portion of the code that would allow us to reproduce the error.
apaszke
<SCODE>'''
GPU LSTM
    1 single input
    1 single output
'''

from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt


class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, bias, dropout):
        super(Net, self).__init__()
        self.rnn = nn.LSTM(input_size=input_size,
                           hidden_size=hidden_size,
                           num_layers=num_layers,
                           bias=bias,
                           dropout=dropout)


def input_var(i):
    test = np.array([i])
#     print(test.shape)
    # test = np.array([i])
    input_var = test.reshape(1, 1, 1)  # (seq_len, batch, input_size)
    input_var = torch.from_numpy(input_var).float()
    return input_var


def label_var(i):
    test = np.array([i*4])
    label_var = test.reshape(1, 1)  #
    label_var = torch.from_numpy(label_var).float()
    return label_var


class lstmModule:
    def __init__(self, input_size, hidden_size, num_layers, bias, dropout,
                 seq_len, batch_size, meta_lr, n_meta_iter):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bias = bias
        self.dropout = dropout
        self.seq_len = seq_len
        self.batch_size = batch_size
        self.meta_lr = meta_lr
        self.n_meta_iter = n_meta_iter

        self.net = Net(input_size=input_size,
                       hidden_size=hidden_size,
                       num_layers=num_layers,
                       bias=bias,
                       dropout=dropout)

        self.net.cuda()

        self.h0 = Variable(torch.randn(self.num_layers,
                                       self.batch_size,
                                       self.hidden_size)).cuda()

        self.c0 = Variable(torch.randn(self.num_layers,
                                       self.batch_size,
                                       self.hidden_size)).cuda()

        self.optimizer = optim.Adam(self.net.rnn.parameters(), lr=self.meta_lr)

        self.loss_lst = []

    def lstm_forward(self, seq_num, meta_num):
        def pseudo_loss(output, label):
            return torch.mean(torch.sum(torch.abs(output - label)))

        inp = input_var(seq_num)
        input = Variable(inp).cuda()

        lab = label_var(seq_num)
        label = Variable(lab).cuda()

        if seq_num == 0:

            # Ensure clear gradient buffer
            self.optimizer.zero_grad()
            self.loss_tot = [0 for i in range(self.hidden_size)]

            # Label concatenation
            self.label_all = label

            # LSTM
            output, hn = self.net.rnn(input, (self.h0, self.c0))
            output = 100 * output

            op = [output[:, :, i] for i in range(self.hidden_size)]

            self.output_all = op
            #             print('1 step length:', len(self.output_all))
            self.h, self.c = hn
        else:
            self.label_all = torch.cat((self.label_all, label), 0)
            output, hn = self.net.rnn(input, (self.h, self.c))
            output = 100 * output
            op = [output[:, :, i] for i in range(self.hidden_size)]
            self.h, self.c = hn
            self.output_all = [torch.cat((self.output_all[i], op[i]), 0) for i in range(self.hidden_size)]

        if seq_num == (self.seq_len - 1):
            # Get loss
            self.loss_tot = [self.loss_tot[i] + pseudo_loss(self.output_all[i], self.label_all) for i in range(self.hidden_size)]

            # Append loss
            self.loss_lst.append(sum(self.loss_tot).cpu().data.numpy()[0])

            # Backprop
            sum(self.loss_tot).backward()

            # Update optimizer
            self.optimizer.step()

        if seq_num == (self.seq_len - 1) and meta_num == (self.n_meta_iter - 1):
            # print(len(self.loss_lst))
            print('Loss 1', self.loss_tot[0].cpu().data.numpy())
            print('Loss 2', self.loss_tot[1].cpu().data.numpy())
            plt.clf()
            plt.plot()
            plt.title('Loss Curve')
            plt.plot(self.loss_lst, label='Loss Curve')
            plt.legend(loc='best')
            plt.savefig('loss.png')

    def lstm_check(self, seq_num):
        inp = input_var(seq_num)
        input = Variable(inp).cuda()
        lab = label_var(seq_num)
        label = Variable(lab).cuda()

        if seq_num == 0:
            # Ensure clear gradient buffer
            self.optimizer.zero_grad()
            self.loss_tot = [0 for i in range(self.hidden_size)]

            # Label concatenation
            self.label_all = label

            # LSTM
            output, hn = self.net.rnn(input, (self.h0, self.c0))
            output = 100 * output
            op = [output[:, :, i] for i in range(self.hidden_size)]
            self.output_all = op
            self.h, self.c = hn
        else:
            self.label_all = torch.cat((self.label_all, label), 0)
            output, hn = self.net.rnn(input, (self.h, self.c))
            output = 100 * output
            op = [output[:, :, i] for i in range(self.hidden_size)]
            self.h, self.c = hn
            self.output_all = [torch.cat((self.output_all[i], op[i]), 0) for i in range(self.hidden_size)]

        if seq_num == (self.seq_len - 1):
            print('-' * 10)
            print(self.output_all[0].cpu().data.numpy())
            print(self.label_all.cpu().data.numpy())
            print('-' * 10)
            print(self.output_all[1].cpu().data.numpy())
            print(self.label_all.cpu().data.numpy())

N_meta = 10
LR_meta = 0.1
N_seq = 4
batch_size = 1
layers = 4
input_size = 1
hidden_size = 15000

# Initialize and assign class to object once
# input_size, hidden_size, num_layers, bias, dropout, seq_len, batch_size, meta_lr, n_meta_iter):
print 'Initializing LSTM'
lstm = lstmModule(input_size, hidden_size, layers, True, 0.1, N_seq, batch_size, LR_meta, N_meta)
print 'Initialized LSTM'

# Run through meta iterations
print 'Training'
for j in range(N_meta):
    print('Meta iteration', j)
    # Run through each step
    for i in range(N_seq):
        lstm.lstm_forward(i, j)
print 'Done Training'

# Check
print('-' * 10)
print 'Checking'
for i in range(N_seq):
    lstm.lstm_check(i)
print 'Done Checking'
<ECODE>
ritchieng
And why do you think it should work with a history size of 10000? That requires a lot of stuff to keep around. You can’t expect the framework to work with arbitrarily large inputs, and that size sounds like something that possibly could raise an OOM error.
apaszke
Was wondering if you’ve any suggestions on saving memory?
ritchieng
apaszke
I’m having a similar CUDA memory issue but I’m only running a shallow CNN…I was wondering if anyone could provide any insight as to where this memory leak is? My model is pretty small: <SCODE>class RegressionalNet(torch.nn.Module):
    
    def __init__(self):
        super().__init__()
        self.feature_extractor = torch.nn.Sequential(
            
            torch.nn.Conv2d(1,64,5,padding=2), 
            torch.nn.ReLU(),
            torch.nn.Conv2d(64,128,5,padding=2,bias=False),
            torch.nn.ReLU(),
            torch.nn.BatchNorm2d(128),
            torch.nn.MaxPool2d(2),
            torch.nn.Conv2d(128,256,5,padding=2,bias=False),
            torch.nn.ReLU(),
            torch.nn.BatchNorm2d(256),
            torch.nn.MaxPool2d(2))
 
        self.classifier = torch.nn.Sequential(
	        torch.nn.Dropout(0.5),
            torch.nn.Linear(256*16*16,256), # Fully connected layer 
            torch.nn.ReLU(),            
            torch.nn.Linear(256,1))
        
    def forward(self,x):
        features = self.feature_extractor(x)
        output = self.classifier(features.view(int(x.size()[0]),-1))
        return output
<ECODE> My training procedure looks as  follows: <SCODE>def train(
          model: torch.nn.Module, 
          transforms, 
          data_path= DATA_PATH, 
          val_path= VAL_PATH, 
          num_epochs=101, 
          batch_size=64, 
          verbose=True,
          cube_length=640, img_size=(64, 64), 
          loss=torch.nn.MSELoss(), 
          lr_schedule=True, initial_lr=1e-3, suffix=""):

    data_path = os.path.abspath(data_path)
    val_path = os.path.abspath(val_path)	
    model = model.train()
    device = torch.device("cuda")
    model = model.to(device).to(torch.float)
    
    """ LOADING IN THE REAL TRAINING AND VALIDATION DATASETS """
    
    loader = DataLoader(FITSCubeDataset(data_path, cube_length, transforms, img_size), 
                        batch_size=batch_size, shuffle=True)                        
    validation_loader = DataLoader(FITSCubeDataset(val_path, cube_length, transforms, img_size), 
                                   batch_size=batch_size, shuffle=True)                                     
    
    optim = torch.optim.Adam(model.parameters(), initial_lr)	
    accuracies, val_accuracies, epochs, val_epochs = [0], [0], [0], [0]
	
    for i in range(num_epochs):
        print("Epoch %d of %d" % (i+1, num_epochs))
        _accuracies,_val_accuracies = [],[]    
        model.train(True) 
        for idx, (batch, target) in enumerate(tqdm(loader)):
            batch = batch.to(device).to(torch.float)
            if isinstance(loss, torch.nn.CrossEntropyLoss):
                target = target.to(device).to(torch.long)
            else:
                target = target.to(device).to(torch.float)
            pred = model(batch).reshape(-1)
            loss_value = loss(pred, target)
            optim.zero_grad()
            loss_value.backward()
            optim.step()
                
            ###Change the error metric here###

            _accuracies.append(loss_value)   
        epochs.append(i+1)                
        mean_accuracy = sum(_accuracies)/len(_accuracies)
        accuracies.append(mean_accuracy)
        print("Mean training loss: %f" % mean_accuracy)        
<ECODE>
spacemeerkat
You need to do <SCODE>_accuracies.append(loss_value.detach())
<ECODE> because otherwise you are saving the complete computation graph for every sample which causes the OOM
justusschock
spacemeerkat
maqboolurrahim