I wrote the following code to solve a Seq2Seq regression problem. My implementation is based on the GRU and multi-head attention. The performance is horrible. I tried playing with the hyperparameters, but nothing changed. This led me to think it was a network architecture issue.
class Seq2Seq(nn.Module):
def __init__(self, input_size, output_size, hidden, num_heads):
super(Seq2Seq, self).__init__()
self.encoder = nn.GRU(input_size, hidden, 2)
self.decoder = nn.GRU(hidden, hidden, 2)
self.multihead_attn = nn.MultiheadAttention(hidden, num_heads)
self.linear = nn.Linear(hidden, output_size)
self.init_weights()
def init_weights(self):
self.linear.weight.data.normal_(0, 0.1)
def forward(self, x):
encoded, _ = self.encoder(x)
decoded, _ = self.decoder(encoded)
attention_output, _ = self.multihead_attn(decoded, decoded, decoded)
out = self.linear(attention_output)
return out
D_in = 4
D_out = 1
hidden = 16
num_heads = 4
seq2seq = Seq2Seq(input_size=D_in, output_size=D_out, hidden=hidden, num_heads=num_heads)
inputs = torch.rand((7, 100, D_in))
outputs = seq2seq(inputs)
Related
Coming from TensorFlow background, I am trying to convert a snippet of code of the custom layer from Keras to PyTorch.
The custom layer in Keras looks like this:
class Attention_module(tf.keras.layers.Layer):
def __init__(self, class_num):
super(Attention_module,self).__init__(class_num)
self.class_num = class_num
self.Ws = None
def build(self, input_shape):
embedding_length = int(input_shape[2])
self.Ws = self.add_weight(shape=(self.class_num, embedding_length),
initializer=tf.keras.initializers.get('glorot_uniform'), trainable=True)
super(Attention_module, self).build(input_shape)
def call(self, inputs):
sentence_trans = tf.transpose(inputs, [0, 2, 1])
at = tf.matmul(self.Ws, sentence_trans)
at = tf.math.tanh(at)
at = K.exp(at - K.max(at, axis=-1, keepdims=True))
at = at / K.sum(at, axis=-1, keepdims=True)
v = K.batch_dot(at, inputs)
return v
I want to implement the same in the torch; I have already done the forward pass block but am confused about how to do the embedding and weight initialization the same as the above layer in PyTorch?
class Attention_module(torch.nn.Module):
def __init__(self, class_num):
# how to initialize weight with same as above keras layer?
def forward(self, inputs):
sentence_trans = inputs.permute(0, 2, 1)
at = torch.mm(self.Ws, sentence_trans)
at = torch.nn.Tanh(at)
at = torch.exp(at - torch.max(torch.Tensor(at), dim=-1, keepdims=True).values)
at = at / torch.sum(at, dim = -1, keepdims=True)
v = torch.einsum('ijk,ikl->ijl', at, inputs)
return v
Thank you!
class Attention_module(torch.nn.Module):
def __init__(self, class_num, input_shape):
super().__init__()
self.class_num = class_num
embedding_length = int(input_shape[2])
self.Ws = torch.nn.Embedding(num_embeddings=class_num,
embedding_dim=embedding_length) # Embedding layer
torch.nn.init.xavier_uniform_(self.Ws.weight) # Glorot initialization
Here's the reference for layer initialization methods. Xavier init is another name for Glorot init.
The _ at the end of torch.nn.init.xavier_uniform_ is a pytorch convention that signifies an inplace operation.
You can also use torch.nn.init at runtime. It doesn't have to be within __init__(). Like:
att = Attention_module(class_num, input_shape)
torch.nn.init.xavier_uniform_(att.Ws.weight)
or :
for param in att.parameters():
torch.nn.init.xavier_uniform_(param)
I have two neural networks running in parallel. Each gives a features map of same size say Nx1. Now I want weighted average of these embedding like this w1 * embed1 + w2 * embed2. I have tried these 1 2.But the weights are not updating. Any help would be appreciated. Here is how I am trying to do it:
class LinearWeightedAvg(nn.Module):
def __init__(self, n_inputs):
super(LinearWeightedAvg, self).__init__()
self.weight1 = Variable(torch.randn(1), requires_grad=True).cuda()
self.weight2 = Variable(torch.randn(1), requires_grad=True).cuda()
def forward(self, inp_embed):
return self.weight1 * inp_embed[0] + self.weight2 * inp_embed[1]
class EmbedBranch(nn.Module):
def __init__(self, feat_dim, embedding_dim):
super(EmbedBranch, self).__init__()
fc_layer1 = fc_layer
def forward(self, x):
x = self.fc_layer1(x)
return x
class EmbeddingNetwork(nn.Module):
def __init__(self, args, N):
super(EmbeddingNetwork, self).__init__()
embedding_dim = N
self.embed1 = EmbedBranch(N, N)
self.embed2 = EmbedBranch(N, N)
self.comb_branch = LinearWeightedAvg(metric_dim)
self.args = args
if args.cuda:
self.cuda()
def forward(self, emb1, emb2):
embeds1 = self.text_branch(emb1)
embeds2 = self.image_branch(emb2)
combined = self.comb_branch([embeds1, embeds2])
return combined
def train_forward(self, embed1, embed2):
combined = self(embed1, embed2)
embeds = model.train_forward(embed1, embed2)
loss = loss_func(embeds, labels)
running_loss.update(loss.data.item())
optimizer.zero_grad()
loss.backward()
Also I want the weight to be within 0-1 range.
Thanks,
You should use self.weightx = torch.nn.Parameter(your_inital_tensor) to register a tensor as a learnable parameter of the model.
I am trying to specify a dynamic amount of layers, which I seem to be doing wrong.
My issue is that when I define the 100 layers here, I will get an error in the forward step.
But when I define the layer properly it works?
Below simplified example
class PredictFromEmbeddParaSmall(LightningModule):
def __init__(self, hyperparams={'lr': 0.0001}):
super(PredictFromEmbeddParaSmall, self).__init__()
#Input is something like tensor.size=[768*100]
self.TO_ILLUSTRATE = nn.Linear(768, 5)
self.enc_ref=[]
for i in range(100):
self.enc_red.append(nn.Linear(768, 5))
# gather the layers output sth
self.dense_simple1 = nn.Linear(5*100, 2)
self.output = nn.Sigmoid()
def forward(self, x):
# first input to enc_red
x_vecs = []
for i in range(self.para_count):
layer = self.enc_red[i]
# The first dim is the batch size here, output is correct
processed_slice = x[:, i * 768:(i + 1) * 768]
# This works and give the out of size 5
rand = self.TO_ILLUSTRATE(processed_slice)
#This will fail? Error below
ret = layer(processed_slice)
#more things happening we can ignore right now since we fail earlier
I get this error when executing "ret = layer.forward(processed_slice)"
RuntimeError: Expected object of device type cuda but got device type
cpu for argument #1 'self' in call to _th_addmm
Is there a smarter way to program this? OR solve the error?
You should use a ModuleList from pytorch instead of a list: https://pytorch.org/docs/master/generated/torch.nn.ModuleList.html . That is because Pytorch has to keep a graph with all modules of your model, if you just add them in a list they are not properly indexed in the graph, resulting in the error you faced.
Your coude should be something alike:
class PredictFromEmbeddParaSmall(LightningModule):
def __init__(self, hyperparams={'lr': 0.0001}):
super(PredictFromEmbeddParaSmall, self).__init__()
#Input is something like tensor.size=[768*100]
self.TO_ILLUSTRATE = nn.Linear(768, 5)
self.enc_ref=nn.ModuleList() # << MODIFIED LINE <<
for i in range(100):
self.enc_red.append(nn.Linear(768, 5))
# gather the layers output sth
self.dense_simple1 = nn.Linear(5*100, 2)
self.output = nn.Sigmoid()
def forward(self, x):
# first input to enc_red
x_vecs = []
for i in range(self.para_count):
layer = self.enc_red[i]
# The first dim is the batch size here, output is correct
processed_slice = x[:, i * 768:(i + 1) * 768]
# This works and give the out of size 5
rand = self.TO_ILLUSTRATE(processed_slice)
#This will fail? Error below
ret = layer(processed_slice)
#more things happening we can ignore right now since we fail earlier
Then it should work all right!
Edit: alternative way.
Instead of using ModuleList you can also just use nn.Sequential, this allows you to avoid using the for loop in the forward pass. That also means that you will not have access to intermediary activations, so that is not the solution for you if you need them.
class PredictFromEmbeddParaSmall(LightningModule):
def __init__(self, hyperparams={'lr': 0.0001}):
super(PredictFromEmbeddParaSmall, self).__init__()
#Input is something like tensor.size=[768*100]
self.TO_ILLUSTRATE = nn.Linear(768, 5)
self.enc_ref=[]
for i in range(100):
self.enc_red.append(nn.Linear(768, 5))
self.enc_red = nn.Seqential(*self.enc_ref) # << MODIFIED LINE <<
# gather the layers output sth
self.dense_simple1 = nn.Linear(5*100, 2)
self.output = nn.Sigmoid()
def forward(self, x):
# first input to enc_red
x_vecs = []
out = self.enc_red(x) # << MODIFIED LINE <<
A little bit more adjustable solution which comes down to matter of taste or complexity of your exact situation was posted here.
For reference I post an adjusted version of the code here:
import torch
from torch import nn, optim
from torch.nn.modules import Module
from implem.settings import settings
class Model(nn.Module):
def __init__(self, input_size, layers_data: list, learning_rate=0.01, optimizer=optim.Adam):
super().__init__()
self.layers = nn.ModuleList()
self.input_size = input_size # Can be useful later ...
for size, activation in layers_data:
self.layers.append(nn.Linear(input_size, size))
input_size = size # For the next layer
if activation is not None:
assert isinstance(activation, Module), \
"Each tuples should contain a size (int) and a torch.nn.modules.Module."
self.layers.append(activation)
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.learning_rate = learning_rate
self.optimizer = optimizer(params=self.parameters(), lr=learning_rate)
def forward(self, input_data):
for layer in self.layers:
input_data = layer(input_data)
return input_data
# test that the net is working properly
if __name__ == "__main__":
data_size = 5
layer1, layer2 = 10, 10
output_size = 2
data = torch.randn(data_size)
mlp = Model(data_size, [(layer1, nn.ReLU()), (layer2, nn.ReLU()), (output_size, nn.Sigmoid())])
output = mlp(data)
print("done")
So, I am used to use PyTorch and now decided to give Skorch a shot.
Here they define the network as
class ClassifierModule(nn.Module):
def __init__(
self,
num_units=10,
nonlin=F.relu,
dropout=0.5,
):
super(ClassifierModule, self).__init__()
self.num_units = num_units
self.nonlin = nonlin
self.dropout = dropout
self.dense0 = nn.Linear(20, num_units)
self.nonlin = nonlin
self.dropout = nn.Dropout(dropout)
self.dense1 = nn.Linear(num_units, 10)
self.output = nn.Linear(10, 2)
def forward(self, X, **kwargs):
X = self.nonlin(self.dense0(X))
X = self.dropout(X)
X = F.relu(self.dense1(X))
X = F.softmax(self.output(X), dim=-1)
return X
I prefer inputting lists of neurons in each layer i.e num_units=[30,15,5,2] would have 2 hidden layers with 15 and 5 neurons. Furthermore we have 30 features and 2 classes, thus re-writing it to something like this
class Net(nn.Module):
def __init__(
self,
num_units=[30,15,5,2],
nonlin=[F.relu,F.relu,F.relu],
dropout=[0.5,0.5,0.5],
):
super(Net, self).__init__()
self.layer_units = layer_units
self.nonlin = nonlin #Activation function
self.dropout = dropout #Drop-out rates in each layer
self.layers = [nn.Linear(i,p) for i,p in zip(layer_units,layer_units[1:])] #Dense layers
def forward(self, X, **kwargs):
print("Forwards")
for layer,func,drop in zip(self.layers[:-1],self.nonlin,self.dropout):
print(layer,func,drop)
X=drop(func(layer(X)))
X = F.softmax(X, dim=-1)
return X
should do the trick. The problem is that when calling
net = NeuralNetClassifier(Net,max_epochs=20,lr=0.1,device="cuda")
net.fit(X,y)
I get the error "ValueError: optimizer got an empty parameter list". I have narrowed it down to the removal of self.output = nn.Linear(10, 2) simply makes the net not enter forward i.e it seems like output is some kind of "trigger" variable. Is that really the case the network need a variable called output (being a layer) at the end, and that we are not free to define the variable-names ourself ?
Pytorch will look for subclasses of nn.Module, so changing
self.layers = [nn.Linear(i,p) for i,p in zip(layer_units,layer_units[1:])]
to
self.layers = nn.ModuleList([nn.Linear(i,p) for i,p in zip(layer_units,layer_units[1:])])
should work fine
Model loss decreases but the performance of model(such as F1-score) does not increase.
I want to fine-tune a pertained language model XLM from Facebook to do NER tasks, so I linked a BiLSTM and CRF.
This is my model architecture. The entire code repo have been uploaded to github https://github.com/stefensa/XLM_NER
class XLM_BiLSTM_CRF(nn.Module):
def __init__(self, config, num_labels, params, dico, reloaded):
super().__init__()
self.config = config
self.num_labels = num_labels
self.batch_size = config.batch_size
self.hidden_dim = config.hidden_dim
self.xlm = TransformerModel(params, dico, True, True)
self.xlm.eval()
self.xlm.load_state_dict(reloaded['model'])
self.lstm = nn.LSTM(config.embedding_dim, config.hidden_dim // 2,
num_layers=1, bidirectional=True)
self.dropout = nn.Dropout(config.dropout)
self.classifier = nn.Linear(config.hidden_dim, config.num_class)
self.apply(self.init_bert_weights)
self.crf = CRF(config.num_class)
def forward(self, word_ids, lengths, langs=None, causal=False):
sequence_output = self.xlm('fwd', x=word_ids, lengths=lengths, causal=False).contiguous()
sequence_output, _ = self.lstm(sequence_output)
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
return self.crf.decode(logits)
def log_likelihood(self, word_ids, lengths, tags):
sequence_output = self.xlm('fwd', x=word_ids, lengths=lengths, causal=False).contiguous()
sequence_output, _ = self.lstm(sequence_output)
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
return - self.crf(logits, tags.transpose(0,1))
def init_bert_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
This is the initial state of my model.
And this is the 9th epoch performance of my model. The metrics do not change.
Can anyone solve my problem?