Hello, I am getting cuda oom at the following code:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-13-2e31ab04f4ad> in <module>
13 golden = german_sentences[:,1:]
14 loss, n_correct, n_word = cal_performance(
---> 15 output, golden.to('cuda'), 3, smoothing=True)
16 loss.backward()
17 optimizer.step_and_update_lr()
<ipython-input-12-657c60d25b9d> in cal_performance(pred, gold, trg_pad_idx, smoothing)
2 ''' Apply label smoothing if needed '''
3
----> 4 loss = cal_loss(pred, gold, trg_pad_idx, smoothing=smoothing)
5
6 pred = pred.max(1)[1]
<ipython-input-12-657c60d25b9d> in cal_loss(pred, gold, trg_pad_idx, smoothing)
22 n_class = pred.size(1)
23
---> 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)
I have 8 V100 GPUS and this is the loss function calculation :
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
This is my model code:
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() > 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 & 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) & 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))