我正在PyTorch上运行具有8xV100 GPU的批处理LSTM模型（批量大小为32），每个GPU具有16GB内存。
我有一个具有以下结构的LSTM模型：
# Class containing the LSTM model initialization and feed-forward logic
class LSTMClassifier(nn.Module):
# LSTM initialization
def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, static_size):
super(LSTMClassifier, self).__init__()
# Setting the hidden layer dimension of the LSTM
self.hidden_dim = hidden_dim
# Initializing the embedding layer
self.embeddings = nn.Embedding(vocab_size, embedding_dim-2)
# Initializing the LSTM layer with one hidden layer
self.lstm = nn.LSTM(((embedding_dim*vocab_size)+static_size), hidden_dim, num_layers = 1, batch_first=True)
# Initializing linear linear that takes the hidden layer output
self.hidden2label = nn.Linear(hidden_dim, label_size)
# Defining the hidden state of the LSTM
def init_hidden(self, batch_size):
# the first is the hidden h
# the second is the cell c
return [autograd.Variable(torch.zeros(batch_size, 1, self.hidden_dim).cuda()),
autograd.Variable(torch.zeros(batch_size, 1, self.hidden_dim).cuda())]
# Defining the feed forward logic of the LSTM. It contains:
# 1. The embedding layer
# 2. The LSTM layer with one hidden layer
# 3. The softmax layer
def forward(self, seq, freq, time, static):
print(seq.size())
# Grab the mini-batch length and max sequence length (pre-ordered)
# (need to do this in the forward logic because of data parallelism and how the GPU's will split up the batch)
sequence_length = seq.size()[1]
batch_length = seq.size()[0]
# reset the LSTM hidden state.
# Must be done before you run a new batch. Otherwise the LSTM will treat a new batch as a continuation of a sequence
self.hidden = self.init_hidden(batch_length)
# Permute the cell and hidden layers. This is because when using Batch_first = True on data parallel,
# the hidden state will still expect an input of (nLayer, batch size, hidden dim), but we are feeding it (batch size, nLayer, hidden dim)
# Thus, to fix it, we need to swap the first and 2nd inputs before feeding to hidden dim
self.hidden[0] = self.hidden[0].permute(1, 0, 2).contiguous()
self.hidden[1] = self.hidden[1].permute(1, 0, 2).contiguous()
# This is the pass to the embedding layer.
# The sequence is of dimension N and the output is N x Demb
embeds = self.embeddings(seq)
# Concatenate the embedding output with the time and frequency vectors
embeds = torch.cat((embeds,freq), dim=3)
embeds = torch.cat((embeds,time), dim=3)
# Flatten the tensor
x = embeds.view(batch_length, sequence_length, -1)
# Concatenate the static information
x = torch.cat((x, static), dim=2)
# Grab the list of lengths of sequences, for the purpose of packing the padded sequenes
seq_lengths = torch.LongTensor(list(map(len, seq)))
# pack the padded sequence so that paddings are ignored
packed_x = torch.nn.utils.rnn.pack_padded_sequence(x, seq_lengths, batch_first=True)
# Feed to the LSTM layer
self.lstm.flatten_parameters()
lstm_out, self.hidden = self.lstm(packed_x, self.hidden)
# Swap back the 1st and 2nd inputs to the hidden layer back to its original configuration
self.hidden = list(self.hidden)
self.hidden[0] = self.hidden[0].permute(1, 0, 2).contiguous()
self.hidden[1] = self.hidden[1].permute(1, 0, 2).contiguous()
# Unpack the packed padded sequence so that it is ready for prediction
unpacked_lstm_out, input_sizes = torch.nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)
# Feed the last layer of the LSTM into the linear layer
y = self.hidden2label(unpacked_lstm_out[:,-1,:])
# Produce the softmax probabilities
log_probs = F.log_softmax(y)
return log_probs
我只运行了一个批处理数据集的一次迭代（批处理大小为32）：
torch.cuda.empty_cache()
EMBEDDING_DIM = 32
HIDDEN_DIM = 50
EPOCH = 10
BATCH_SIZE = 16
best_val_auc = 0.0
model = LSTMClassifier(embedding_dim=EMBEDDING_DIM, hidden_dim=HIDDEN_DIM, vocab_size=len(events_to_ix), label_size=len(targets_to_ix), static_size=(len(gender_to_ix)+1)).cuda()
model = torch.nn.DataParallel(model).cuda()
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
no_up = 0
model.train()
avg_loss = 0.0
count = 0
truth_res = []
pred_res = []
g= train_data.groupby(np.arange(len(train_data)) // 32)
rows = g.get_group((list(g.groups)[0])) # Batch size of 32
torch.cuda.empty_cache()
# Grab the targets into a list and append it into the truth_res list in order to measure AUC performance
target = [targets_to_ix[target] for target in rows['event_target']]
truth_res.extend(target)
# Encode the data and output to tensors (based on the previous description)
seq, freq, time_data, static = encode_data(rows, events_to_ix)
print(len(seq))
# Pad the sequences
seq = rnn_utils.pad_sequence(seq, batch_first = True)
freq = rnn_utils.pad_sequence(freq, batch_first = True)
time_data = rnn_utils.pad_sequence(time_data, batch_first = True)
static = rnn_utils.pad_sequence(static, batch_first = True)
# Put the padded sequences into Variable and Cuda cores
seq = autograd.Variable(seq).cuda()
freq = autograd.Variable(freq).cuda()
time_data = autograd.Variable(time_data).cuda()
static = autograd.Variable(static).cuda()
target = autograd.Variable(torch.LongTensor(target)).cuda()
# Feed the tensor Variables into the model
pred = model(seq,freq,time_data,static)
这是我在加载到模型之前的nvidia-smi输出：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104 Driver Version: 410.104 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| 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 V100-SXM2... On | 00000000:00:17.0 Off | 0 |
| N/A 48C P0 64W / 300W | 2438MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-SXM2... On | 00000000:00:18.0 Off | 0 |
| N/A 42C P0 44W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-SXM2... On | 00000000:00:19.0 Off | 0 |
| N/A 41C P0 46W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-SXM2... On | 00000000:00:1A.0 Off | 0 |
| N/A 44C P0 44W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-SXM2... On | 00000000:00:1B.0 Off | 0 |
| N/A 44C P0 42W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-SXM2... On | 00000000:00:1C.0 Off | 0 |
| N/A 44C P0 45W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-SXM2... On | 00000000:00:1D.0 Off | 0 |
| N/A 42C P0 44W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-SXM2... On | 00000000:00:1E.0 Off | 0 |
| N/A 45C P0 47W / 300W | 11MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 93165 C ...r/anaconda3/envs/pytorch_p36/bin/python 2427MiB |
+-----------------------------------------------------------------------------+
在加载模型之后，我在正向传递开始时调用nvidia-smi，nvidia-smi的输出为：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104 Driver Version: 410.104 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| 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 V100-SXM2... On | 00000000:00:17.0 Off | 0 |
| N/A 50C P0 59W / 300W | 3040MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-SXM2... On | 00000000:00:18.0 Off | 0 |
| N/A 44C P0 58W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-SXM2... On | 00000000:00:19.0 Off | 0 |
| N/A 44C P0 62W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-SXM2... On | 00000000:00:1A.0 Off | 0 |
| N/A 47C P0 57W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-SXM2... On | 00000000:00:1B.0 Off | 0 |
| N/A 47C P0 57W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-SXM2... On | 00000000:00:1C.0 Off | 0 |
| N/A 47C P0 59W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-SXM2... On | 00000000:00:1D.0 Off | 0 |
| N/A 44C P0 59W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-SXM2... On | 00000000:00:1E.0 Off | 0 |
| N/A 48C P0 62W / 300W | 1342MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
我希望8个GPU上的内存更均匀，更小，因为我传入的批处理数据仅占用了大约2GB的空间。我在做错什么吗？