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new c55fc57 add Sequential compatibility to rnn layers (#7352)
c55fc57 is described below
commit c55fc571d22ec458365d87740b589827ddfd86cf
Author: Sheng Zha <s...@users.noreply.github.com>
AuthorDate: Thu Aug 10 10:24:03 2017 -0700
add Sequential compatibility to rnn layers (#7352)
---
python/mxnet/gluon/rnn/rnn_layer.py | 46 ++++++++++++++++++++++++---------
tests/python/unittest/test_gluon_rnn.py | 29 +++++++++++++++++++++
2 files changed, 63 insertions(+), 12 deletions(-)
diff --git a/python/mxnet/gluon/rnn/rnn_layer.py
b/python/mxnet/gluon/rnn/rnn_layer.py
index a9bcee5..86b7c61 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -168,10 +168,13 @@ class _RNNLayer(Block):
states.append(func(name='%sh0_%d'%(self.prefix, i), **info))
return states
- def forward(self, inputs, states):
+ def forward(self, inputs, states=None):
+ batch_size = inputs.shape[self._layout.find('N')]
+ skip_states = states is None
+ if skip_states:
+ states = self.begin_state(batch_size)
if isinstance(states, ndarray.NDArray):
states = [states]
- batch_size = inputs.shape[self._layout.find('N')]
for state, info in zip(states, self.state_info(batch_size)):
if state.shape != info['shape']:
raise ValueError(
@@ -182,8 +185,12 @@ class _RNNLayer(Block):
self.i2h_weight[i].shape = (self._gates*self._hidden_size,
inputs.shape[2])
self.i2h_weight[i]._finish_deferred_init()
if inputs.context.device_type == 'gpu':
- return self._forward_gpu(inputs, states)
- return self._forward_cpu(inputs, states)
+ out = self._forward_gpu(inputs, states)
+ else:
+ out = self._forward_cpu(inputs, states)
+
+ # out is (output, state)
+ return out[0] if skip_states else out
def _forward_cpu(self, inputs, states):
ns = len(states)
@@ -282,10 +289,12 @@ class RNN(_RNNLayer):
If `bidirectional` is True, output shape will instead be
`(sequence_length, batch_size, 2*num_hidden)`
- Recurrent state shape:
- The recurrent state's shape is `(num_layers, batch_size, num_hidden)`.
- If `bidirectional` is True, state shape will instead be
+ Recurrent state:
+ The recurrent state is an NDArray with shape `(num_layers, batch_size,
num_hidden)`.
+ If `bidirectional` is True, the recurrent state shape will instead be
`(2*num_layers, batch_size, num_hidden)`
+ If input recurrent state is None, zeros are used as default begin
states,
+ and the output recurrent state is omitted.
Examples
@@ -293,6 +302,9 @@ class RNN(_RNNLayer):
>>> layer = mx.gluon.rnn.RNN(100, 3)
>>> layer.initialize()
>>> input = mx.nd.random_uniform(shape=(5, 3, 10))
+ >>> # by default zeros are used as begin state
+ >>> output = layer(input)
+ >>> # manually specify begin state.
>>> h0 = mx.nd.random_uniform(shape=(3, 3, 100))
>>> output, hn = layer(input, h0)
"""
@@ -379,11 +391,13 @@ class LSTM(_RNNLayer):
If `bidirectional` is True, output shape will instead be
`(sequence_length, batch_size, 2*num_hidden)`
- Recurrent state shape:
+ Recurrent state:
The recurrent state is a list of two NDArrays. Both has shape
`(num_layers, batch_size, num_hidden)`.
- If `bidirectional` is True, state shape will instead be
+ If `bidirectional` is True, each recurrent state will instead have
shape
`(2*num_layers, batch_size, num_hidden)`.
+ If input recurrent state is None, zeros are used as default begin
states,
+ and the output recurrent state is omitted.
Examples
@@ -391,6 +405,9 @@ class LSTM(_RNNLayer):
>>> layer = mx.gluon.rnn.LSTM(100, 3)
>>> layer.initialize()
>>> input = mx.nd.random_uniform(shape=(5, 3, 10))
+ >>> # by default zeros are used as begin state
+ >>> output = layer(input)
+ >>> # manually specify begin state.
>>> h0 = mx.nd.random_uniform(shape=(3, 3, 100))
>>> c0 = mx.nd.random_uniform(shape=(3, 3, 100))
>>> output, hn = layer(input, [h0, c0])
@@ -474,10 +491,12 @@ class GRU(_RNNLayer):
If `bidirectional` is True, output shape will instead be
`(sequence_length, batch_size, 2*num_hidden)`
- Recurrent state shape:
- The recurrent state's shape is `(num_layers, batch_size, num_hidden)`.
- If `bidirectional` is True, state shape will instead be
+ Recurrent state:
+ The recurrent state is an NDArray with shape `(num_layers, batch_size,
num_hidden)`.
+ If `bidirectional` is True, the recurrent state shape will instead be
`(2*num_layers, batch_size, num_hidden)`
+ If input recurrent state is None, zeros are used as default begin
states,
+ and the output recurrent state is omitted.
Examples
@@ -485,6 +504,9 @@ class GRU(_RNNLayer):
>>> layer = mx.gluon.rnn.GRU(100, 3)
>>> layer.initialize()
>>> input = mx.nd.random_uniform(shape=(5, 3, 10))
+ >>> # by default zeros are used as begin state
+ >>> output = layer(input)
+ >>> # manually specify begin state.
>>> h0 = mx.nd.random_uniform(shape=(3, 3, 100))
>>> output, hn = layer(input, h0)
"""
diff --git a/tests/python/unittest/test_gluon_rnn.py
b/tests/python/unittest/test_gluon_rnn.py
index ac671e5..4062013 100644
--- a/tests/python/unittest/test_gluon_rnn.py
+++ b/tests/python/unittest/test_gluon_rnn.py
@@ -209,6 +209,35 @@ def test_rnn_cells():
net.add(gluon.rnn.GRUCell(100, input_size=100))
check_rnn_forward(net, mx.nd.ones((8, 3, 200)))
+def check_rnn_layer_forward(layer, inputs, states=None):
+ layer.collect_params().initialize()
+ with mx.autograd.record():
+ out = layer(inputs, states)
+ if states is not None:
+ assert isinstance(out, tuple) and len(out) == 2
+ out = out[0]
+ else:
+ assert isinstance(out, mx.nd.NDArray)
+ out.backward()
+ mx.nd.waitall()
+
+def test_rnn_layers():
+ check_rnn_layer_forward(gluon.rnn.RNN(10, 2), mx.nd.ones((8, 3, 20)))
+ check_rnn_layer_forward(gluon.rnn.RNN(10, 2), mx.nd.ones((8, 3, 20)),
mx.nd.ones((2, 3, 10)))
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2), mx.nd.ones((8, 3, 20)))
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2), mx.nd.ones((8, 3, 20)),
[mx.nd.ones((2, 3, 10)), mx.nd.ones((2, 3, 10))])
+ check_rnn_layer_forward(gluon.rnn.GRU(10, 2), mx.nd.ones((8, 3, 20)))
+ check_rnn_layer_forward(gluon.rnn.GRU(10, 2), mx.nd.ones((8, 3, 20)),
mx.nd.ones((2, 3, 10)))
+
+ net = gluon.nn.Sequential()
+ net.add(gluon.rnn.LSTM(10, 2, bidirectional=True))
+ net.add(gluon.nn.BatchNorm(axis=2))
+ net.add(gluon.nn.Flatten())
+ net.add(gluon.nn.Dense(3, activation='relu'))
+ net.collect_params().initialize()
+ with mx.autograd.record():
+ net(mx.nd.ones((2, 3, 10))).backward()
+
if __name__ == '__main__':
import nose
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