Cuda oom when calculating loss function

username: Bika
cooked: <p>Hello, I am getting cuda oom at the following code:</p>
<pre><code class="lang-auto">---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-13-2e31ab04f4ad&gt; in &lt;module&gt;
     13         golden = german_sentences[:,1:]
     14         loss, n_correct, n_word = cal_performance(
---&gt; 15             output, golden.to('cuda'), 3, smoothing=True) 
     16         loss.backward()
     17         optimizer.step_and_update_lr()

&lt;ipython-input-12-657c60d25b9d&gt; in cal_performance(pred, gold, trg_pad_idx, smoothing)
      2     ''' Apply label smoothing if needed '''
      3 
----&gt; 4     loss = cal_loss(pred, gold, trg_pad_idx, smoothing=smoothing)
      5 
      6     pred = pred.max(1)[1]

&lt;ipython-input-12-657c60d25b9d&gt; in cal_loss(pred, gold, trg_pad_idx, smoothing)
     22         n_class = pred.size(1)
     23 
---&gt; 24         one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
     25         one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
     26         log_prb = F.log_softmax(pred, dim=1)

RuntimeError: CUDA out of memory. Tried to allocate 4.91 GiB (GPU 0; 15.78 GiB total capacity; 13.49 GiB already allocated; 895.75 MiB free; 13.85 GiB reserved in total by PyTorch)
</code></pre>
<p>I have 8 V100 GPUS and this is the loss function calculation :</p>
<pre><code class="lang-auto">def cal_performance(pred, gold, trg_pad_idx, smoothing=False):
    ''' Apply label smoothing if needed '''

    loss = cal_loss(pred, gold, trg_pad_idx, smoothing=smoothing)

    pred = pred.max(1)[1]
    gold = gold.contiguous().view(-1)
    non_pad_mask = gold.ne(trg_pad_idx)
    n_correct = pred.eq(gold).masked_select(non_pad_mask).sum().item()
    n_word = non_pad_mask.sum().item()

    return loss, n_correct, n_word


def cal_loss(pred, gold, trg_pad_idx, smoothing=False):
    ''' Calculate cross entropy loss, apply label smoothing if needed. '''

    gold = gold.contiguous().view(-1)

    if smoothing:
        eps = 0.1
        n_class = pred.size(1)

        one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
        log_prb = F.log_softmax(pred, dim=1)

        non_pad_mask = gold.ne(trg_pad_idx)
        loss = -(one_hot * log_prb).sum(dim=1)
        loss = loss.masked_select(non_pad_mask).sum()  # average later
    else:
        loss = F.cross_entropy(pred, gold, ignore_index=trg_pad_idx, reduction='sum')
    return loss
</code></pre>
<p>This is my model code:</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torch
import torch.distributed as dist

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, attn_dropout=0.1):
        super().__init__()
        self.dropout = nn.Dropout(attn_dropout)

    def forward(self, q, k, v, mask=None):

        attn = torch.matmul(q, k.transpose(2, 3))

        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e18)

        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)

        return output, attn
    
class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)
        self.fc = nn.Linear(n_head * d_v, d_model)

        self.attention = ScaledDotProductAttention()

        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)


    def forward(self, q, k, v, mask=None):

        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)

        residual = q

        # Pass through the pre-attention projection: b x lq x (n*dv)
        # Separate different heads: b x lq x n x dv
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # Transpose for attention dot product: b x n x lq x dv
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)

        if mask is not None:
            mask = mask.unsqueeze(1)   # For head axis broadcasting.

        q, attn = self.attention(q, k, v, mask=mask)

        # Transpose to move the head dimension back: b x lq x n x dv
        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc(q))
        q += residual

        q = self.layer_norm(q)

        return q, attn


class PositionwiseFeedForward(nn.Module):
    ''' A two-feed-forward-layer module '''

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid) # position-wise
        self.w_2 = nn.Linear(d_hid, d_in) # position-wise
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):

        residual = x

        x = self.w_2(F.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual

        x = self.layer_norm(x)

        return x
    

class PositionalEncoding(nn.Module):

    def __init__(self, d_hid, n_position=128):
        super(PositionalEncoding, self).__init__()

        # Not a parameter
        self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))

    def _get_sinusoid_encoding_table(self, n_position, d_hid):
        ''' Sinusoid position encoding table '''
        # TODO: make it with torch instead of numpy

        def get_position_angle_vec(position):
            return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]

        sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
        sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
        sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

        return torch.FloatTensor(sinusoid_table).unsqueeze(0)

    def forward(self, x):
        return x + self.pos_table[:, :x.size(1)].clone().detach()
    
def get_subseq_mask(seq):
    batch_size, seq_len = seq.size()
    mask = (1 - torch.triu(torch.ones((1,seq_len,seq_len),device='cuda'),diagonal=1)).bool()
    return mask

class EncoderLayer(nn.Module):
    ''' Compose with two layers '''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(self, enc_input, slf_attn_mask=None):
        enc_output, enc_slf_attn = self.slf_attn(
            enc_input, enc_input, enc_input, mask=slf_attn_mask)
        enc_output = self.pos_ffn(enc_output)
        return enc_output, enc_slf_attn


class DecoderLayer(nn.Module):
    ''' Compose with three layers '''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(DecoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.enc_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(
            self, dec_input, enc_output,
            slf_attn_mask=None, dec_enc_attn_mask=None):
        dec_output, dec_slf_attn = self.slf_attn(
            dec_input, dec_input, dec_input, mask=slf_attn_mask)
        dec_output, dec_enc_attn = self.enc_attn(
            dec_output, enc_output, enc_output, mask=dec_enc_attn_mask)
        dec_output = self.pos_ffn(dec_output)
        return dec_output, dec_slf_attn, dec_enc_attn
    
def get_pad_mask(seq, pad_idx):
    return (seq != pad_idx).unsqueeze(-2)


def get_subsequent_mask(seq):
    ''' For masking out the subsequent info. '''
    sz_b, len_s = seq.size()
    subsequent_mask = (1 - torch.triu(
        torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
    return subsequent_mask


class PositionalEncoding(nn.Module):

    def __init__(self, d_hid, n_position=200):
        super(PositionalEncoding, self).__init__()

        # Not a parameter
        self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))

    def _get_sinusoid_encoding_table(self, n_position, d_hid):
        ''' Sinusoid position encoding table '''
        # TODO: make it with torch instead of numpy

        def get_position_angle_vec(position):
            return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]

        sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
        sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
        sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

        return torch.FloatTensor(sinusoid_table).unsqueeze(0)

    def forward(self, x):
        return x + self.pos_table[:, :x.size(1)].clone().detach()


class Encoder(nn.Module):
    ''' A encoder model with self attention mechanism. '''

    def __init__(
            self, n_src_vocab, d_word_vec, n_layers, n_head, d_k, d_v,
            d_model, d_inner, pad_idx, dropout=0.1, n_position=200, scale_emb=False):

        super().__init__()

        self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=pad_idx)
        self.position_enc = PositionalEncoding(d_word_vec, n_position=n_position)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_stack = nn.ModuleList([
            EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
            for _ in range(n_layers)])
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
        self.scale_emb = scale_emb
        self.d_model = d_model

    def forward(self, src_seq, src_mask, return_attns=False):

        enc_slf_attn_list = []

        # -- Forward
        enc_output = self.src_word_emb(src_seq)
        if self.scale_emb:
            enc_output *= self.d_model ** 0.5
        enc_output = self.dropout(self.position_enc(enc_output))
        enc_output = self.layer_norm(enc_output)

        for enc_layer in self.layer_stack:
            enc_output, enc_slf_attn = enc_layer(enc_output, slf_attn_mask=src_mask)
            enc_slf_attn_list += [enc_slf_attn] if return_attns else []

        if return_attns:
            return enc_output, enc_slf_attn_list
        return enc_output,


class Decoder(nn.Module):
    ''' A decoder model with self attention mechanism. '''

    def __init__(
            self, n_trg_vocab, d_word_vec, n_layers, n_head, d_k, d_v,
            d_model, d_inner, pad_idx, n_position=200, dropout=0.1, scale_emb=False):

        super().__init__()

        self.trg_word_emb = nn.Embedding(n_trg_vocab, d_word_vec, padding_idx=pad_idx)
        self.position_enc = PositionalEncoding(d_word_vec, n_position=n_position)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_stack = nn.ModuleList([
            DecoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
            for _ in range(n_layers)])
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
        self.scale_emb = scale_emb
        self.d_model = d_model

    def forward(self, trg_seq, trg_mask, enc_output, src_mask, return_attns=False):

        dec_slf_attn_list, dec_enc_attn_list = [], []

        # -- Forward
        dec_output = self.trg_word_emb(trg_seq)
        if self.scale_emb:
            dec_output *= self.d_model ** 0.5
        dec_output = self.dropout(self.position_enc(dec_output))
        dec_output = self.layer_norm(dec_output)

        for dec_layer in self.layer_stack:
            dec_output, dec_slf_attn, dec_enc_attn = dec_layer(
                dec_output, enc_output, slf_attn_mask=trg_mask, dec_enc_attn_mask=src_mask)
            dec_slf_attn_list += [dec_slf_attn] if return_attns else []
            dec_enc_attn_list += [dec_enc_attn] if return_attns else []

        if return_attns:
            return dec_output, dec_slf_attn_list, dec_enc_attn_list
        return dec_output,


class Transformer(nn.Module):
    ''' A sequence to sequence model with attention mechanism. '''

    def __init__(
            self, n_src_vocab, n_trg_vocab, src_pad_idx, trg_pad_idx,
            d_word_vec=512, d_model=512, d_inner=2048,
            n_layers=6, n_head=8, d_k=64, d_v=64, dropout=0.1, n_position=200,
            trg_emb_prj_weight_sharing=False, emb_src_trg_weight_sharing=False):

        super().__init__()

        self.src_pad_idx, self.trg_pad_idx = src_pad_idx, trg_pad_idx

        # In section 3.4 of paper "Attention Is All You Need", there is such detail:
        # "In our model, we share the same weight matrix between the two
        # embedding layers and the pre-softmax linear transformation...
        # In the embedding layers, we multiply those weights by \sqrt{d_model}".
        #
        # Options here:
        #   'emb': multiply \sqrt{d_model} to embedding output
        #   'prj': multiply (\sqrt{d_model} ^ -1) to linear projection output
        #   'none': no multiplication

   
        self.d_model = d_model

        self.encoder = Encoder(
            n_src_vocab=n_src_vocab, n_position=n_position,
            d_word_vec=d_word_vec, d_model=d_model, d_inner=d_inner,
            n_layers=n_layers, n_head=n_head, d_k=d_k, d_v=d_v,
            pad_idx=src_pad_idx, dropout=dropout)

        self.decoder = Decoder(
            n_trg_vocab=n_trg_vocab, n_position=n_position,
            d_word_vec=d_word_vec, d_model=d_model, d_inner=d_inner,
            n_layers=n_layers, n_head=n_head, d_k=d_k, d_v=d_v,
            pad_idx=trg_pad_idx, dropout=dropout)

        self.trg_word_prj = nn.Linear(d_model, n_trg_vocab, bias=False)

        for p in self.parameters():
            if p.dim() &gt; 1:
                nn.init.xavier_uniform_(p) 

        assert d_model == d_word_vec, \
        'To facilitate the residual connections, \
         the dimensions of all module outputs shall be the same.'

        if trg_emb_prj_weight_sharing:
            # Share the weight between target word embedding &amp; last dense layer
            self.trg_word_prj.weight = self.decoder.trg_word_emb.weight

        if emb_src_trg_weight_sharing:
            self.encoder.src_word_emb.weight = self.decoder.trg_word_emb.weight


    def forward(self, src_seq, trg_seq):

        src_mask = get_pad_mask(src_seq, self.src_pad_idx)
        trg_mask = get_pad_mask(trg_seq, self.trg_pad_idx) &amp; get_subsequent_mask(trg_seq)

        enc_output, *_ = self.encoder(src_seq, src_mask)
        dec_output, *_ = self.decoder(trg_seq, trg_mask, enc_output, src_mask)
        seq_logit = self.trg_word_prj(dec_output)

        return seq_logit.view(-1, seq_logit.size(2))
</code></pre>

username: my3bikaht
cooked: <aside class="quote no-group" data-username="Bika" data-post="1" data-topic="139746">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/bika/48/20326_2.png" class="avatar"> Bika:</div>
<blockquote>
<p><code>one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)</code></p>
</blockquote>
</aside>
<p>Error says that creating this tensor requires 5gb of GPU memory.<br>
What are the shapes of pred and gold?</p>

username: Bika
cooked: <p>pred is (52736,32000) and gold (52736,1). Can i divide the tensor to have different calculation for each GPU , like i have 8 GPUs and i think the problem is that the calculation is acculamated. So if i divide the tensor to 8 parts and do the calculation seperately and then add up , can this work ?</p>

username: my3bikaht
cooked: <p>You can use DataParallel or DistributedDataParallel for the multi-gpu config.<br>
As for the OOM error, you need to split you data into batches anyway. Even if you manage to compute loss anyhow, you’ll get same error while trying to run loss backward()</p>

username: Bika
cooked: <p>I am currently using DataParallel. I am using batches yes. What iam thinking about is i saw someone normalizing the loss of different GPUs, so i did similar thing , and just loss.backward the normalized loss of the 8 GPUs, shouldn’t this propagate on the default GPU computation grpah ?</p>

username: my3bikaht
cooked: <p>I see. So you want to calculate loss for whole training set and propagate same loss for each GPU.</p>
<p>Question is, do you really need this? With reduction=sum you can simply calculate partial losses per GPU, then sum across all of them. You can also do sum across batches within each GPU.</p>

username: Bika
cooked: <p>Thank you for your reply.<br>
This is exactly what i want to do. Like i have batch size of 1024 and with 8 gpu , each gpu has a batch size of 128. However during the loss.backward() , the backpropagation calculate as if the batch size was 1024 not 128 so it runs out of memory. What i really want is to write code that calculate the loss over the 8 gpus, then sum it up for the optimizer to run. Any guidance on where to find that guide</p>