What is Dynet Autobatch explain with example

This recipe explains what is Dynet Autobatch explain with example

Recipe Objective - What is Dynet Autobatch explain with example?

DyNet provides the feature of auto batching. Because writing batching code for RNN's is pretty complex. So Dynet will transform the way you think and run the batching code and it's also good for the speed.

To run the auto-batching support, just simply add the "--dynet-autobatch 1" to the command-line flags when running a DyNet program.

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Non-Batched Code:

import dynet as dy
import numpy as np

# acceptor LSTM
class LstmAcceptor(object):
    def __init__(self, in_dim, lstm_dim, out_dim, model):
        self.builder = dy.VanillaLSTMBuilder(1, in_dim, lstm_dim, model)
        self.W = model.add_parameters((out_dim, lstm_dim))

    def __call__(self, sequence):
        lstm = self.builder.initial_state()
        W = self.W.expr() # convert the parameter into an Expession (add it to graph)
        outputs = lstm.transduce(sequence)
        result = W*outputs[-1]
        return result

# usage:
vocab_size = 1000
embed_size = 100

m = dy.Model()
trainer = dy.AdamTrainer(m)
embeds = m.add_lookup_parameters((vocab_size, embed_size))
acceptor = LstmAcceptor(embed_size, 100, 3, m)


# training code
sum_of_losses = 0.0
for epoch in range(10):
    for sequence,label in [((1,4,5,1),1), ((42,1),2), ((56,2,17),1)]:
        dy.renew_cg() # new computation graph
        vecs = [embeds[i] for i in sequence]
        preds = acceptor(vecs)
        loss = dy.pickneglogsoftmax(preds, label)
        sum_of_losses += loss.npvalue()
        loss.backward()
        trainer.update()
    print(sum_of_losses / 3)
    sum_of_losses = 0.0

print("\n\nPrediction time!\n")
# prediction code:
for sequence in [(1,4,12,1), (42,2), (56,2,17)]:
    dy.renew_cg() # new computation graph
    vecs = [embeds[i] for i in sequence]
    preds = dy.softmax(acceptor(vecs))
    vals = preds.npvalue()
    print(np.argmax(vals), vals)

Batched Code:

# training code: batched.
for epoch in range(10):
    dy.renew_cg() # we create a new computation graph for the epoch, not each item.
    # we will treat all these 3 datapoints as a single batch
    losses = []
    for sequence,label in [((1,4,5,1),1), ((42,1),2), ((56,2,17),1)]:
        vecs = [embeds[i] for i in sequence]
        preds = acceptor(vecs)
        loss = dy.pickneglogsoftmax(preds, label)
        losses.append(loss)
# we accumulated the losses from all the batch.
# Now we sum them, and do forward-backward as usual.
# Things will run with efficient batch operations.
    batch_loss = dy.esum(losses)/3
    print(batch_loss.npvalue()) # this calls forward on the batch
    batch_loss.backward()
    trainer.update()

print("\n\nPrediction time!\n")
# prediction code:
dy.renew_cg() # new computation graph
batch_preds = []
for sequence in [(1,4,12,1), (42,2), (56,2,17)]:
    vecs = [embeds[i] for i in sequence]
    preds = dy.softmax(acceptor(vecs))
    batch_preds.append(preds)

# now that we accumulated the prediction expressions,
# we run forward on all of them:
dy.forward(batch_preds)
# and now we can efficiently access the individual values:
for preds in batch_preds:
    vals = preds.npvalue()
    print(np.argmax(vals), vals)

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