szha commented on a change in pull request #14208: Add support for fast
variable-length LSTM
URL: https://github.com/apache/incubator-mxnet/pull/14208#discussion_r281429171
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File path: tests/python/gpu/test_gluon_gpu.py
##########
@@ -225,6 +226,54 @@ def forward(self, inpt):
assert_allclose(net(data).asnumpy(), ref_net(data).asnumpy())
+def check_layer_bidirectional_varseqlen(size, in_size):
+ class RefBiLSTMVarSeqLen(gluon.Block):
+ def __init__(self, size, **kwargs):
+ super(RefBiLSTMVarSeqLen, self).__init__(**kwargs)
+ with self.name_scope():
+ self._lstm_fwd = gluon.rnn.LSTM(size, bidirectional=False,
prefix='l0')
+ self._lstm_bwd = gluon.rnn.LSTM(size, bidirectional=False,
prefix='r0')
+
+ def forward(self, inpt, sequence_length):
+ fwd = self._lstm_fwd(inpt)
+ bwd_inpt = nd.SequenceReverse(inpt,
sequence_length=sequence_length, use_sequence_length=True)
+ bwd = self._lstm_bwd(bwd_inpt)
+ bwd = nd.SequenceReverse(bwd, sequence_length=sequence_length,
use_sequence_length=True)
+ return nd.concat(fwd, bwd, dim=2)
+ weights = {}
+ for d in ['l', 'r']:
+ weights['lstm_{}0_i2h_weight'.format(d)] =
mx.random.uniform(shape=(size*4, in_size))
+ weights['lstm_{}0_h2h_weight'.format(d)] =
mx.random.uniform(shape=(size*4, size))
+ weights['lstm_{}0_i2h_bias'.format(d)] =
mx.random.uniform(shape=(size*4,))
+ weights['lstm_{}0_h2h_bias'.format(d)] =
mx.random.uniform(shape=(size*4,))
+
+ net = gluon.rnn.LSTM(size, bidirectional=True, use_sequence_length=True,
prefix='lstm_')
+ ref_net = RefBiLSTMVarSeqLen(size, prefix='lstm_')
+ net.initialize()
+ ref_net.initialize()
+ net_params = net.collect_params()
+ ref_net_params = ref_net.collect_params()
+ for k in weights:
+ net_params[k].set_data(weights[k])
+ ref_net_params[k.replace('l0', 'l0l0').replace('r0',
'r0l0')].set_data(weights[k])
+
+
+ batch_size = 10
+ num_timesteps = 11
+ data = mx.random.uniform(shape=(num_timesteps, batch_size, in_size))
+
+ # TODO: figure out why int32 doesn't work here
+ sequence_length = nd.random.randint(1, num_timesteps+1,
shape=(batch_size)).astype("float")
+
+ net_output = net(data, sequence_length=sequence_length).asnumpy()
+ ref_net_output = ref_net(data, sequence_length).asnumpy()
+ sequence_length_np = sequence_length.asnumpy().astype("int32")
+
+ # Only compare the valid sections for each batch entry
+ for b in range(batch_size):
+ assert_allclose(net_output[:sequence_length_np[b], b],
ref_net_output[:sequence_length_np[b], b])
Review comment:
I see. I mistook the use_sequence_length flag to be in rnn op. Still,
whether the returned state is of the right step or not is not tested, which is
also an important aspect of variable length RNN support. It may be hard to test
it using RNN layer as reference.
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