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The following commit(s) were added to refs/heads/master by this push:
new 65061dc fix rnn (#10954)
65061dc is described below
commit 65061dc93710afce92edfe548aa3352473da3cdb
Author: Sheng Zha <s...@users.noreply.github.com>
AuthorDate: Tue May 15 14:30:08 2018 -0700
fix rnn (#10954)
---
python/mxnet/gluon/rnn/rnn_layer.py | 5 +++--
tests/python/unittest/test_gluon_rnn.py | 29 ++++++++++++++++++++++-------
2 files changed, 25 insertions(+), 9 deletions(-)
diff --git a/python/mxnet/gluon/rnn/rnn_layer.py
b/python/mxnet/gluon/rnn/rnn_layer.py
index 34ad05d..89224cf 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -23,7 +23,7 @@
from __future__ import print_function
__all__ = ['RNN', 'LSTM', 'GRU']
-from ... import ndarray
+from ... import ndarray, autograd
from .. import Block
from . import rnn_cell
@@ -185,7 +185,8 @@ class _RNNLayer(Block):
for i in range(self._dir):
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' or self._mode == 'lstm':
+ if inputs.context.device_type == 'gpu' or \
+ self._mode == 'lstm' and not (self._dropout and
autograd.is_training()):
out = self._forward_kernel(inputs, states)
else:
out = self._forward(inputs, states)
diff --git a/tests/python/unittest/test_gluon_rnn.py
b/tests/python/unittest/test_gluon_rnn.py
index f22b13d..24d5a93 100644
--- a/tests/python/unittest/test_gluon_rnn.py
+++ b/tests/python/unittest/test_gluon_rnn.py
@@ -80,7 +80,7 @@ def test_lstm_cpu_inference():
mx.test_utils.assert_almost_equal(y, EXPECTED_LSTM_OUTPUT,
rtol=1e-3, atol=1e-5)
-
+
def test_gru():
cell = gluon.rnn.GRUCell(100, prefix='rnn_')
@@ -242,7 +242,7 @@ 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):
+def check_rnn_layer_forward(layer, inputs, states=None, run_only=False):
layer.collect_params().initialize()
inputs.attach_grad()
with mx.autograd.record():
@@ -268,17 +268,32 @@ def check_rnn_layer_forward(layer, inputs, states=None):
assert isinstance(out, mx.nd.NDArray)
out.backward()
- mx.test_utils.assert_almost_equal(np_out, out.asnumpy(), rtol=1e-3,
atol=1e-5)
- mx.test_utils.assert_almost_equal(np_dx, inputs.grad.asnumpy(), rtol=1e-3,
atol=1e-5)
+ if not run_only:
+ mx.test_utils.assert_almost_equal(np_out, out.asnumpy(), rtol=1e-3,
atol=1e-5)
+ mx.test_utils.assert_almost_equal(np_dx, inputs.grad.asnumpy(),
rtol=1e-3, atol=1e-5)
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.RNN(10, 2, bidirectional=True),
mx.nd.ones((8, 3, 20)), mx.nd.ones((4, 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.LSTM(10, 2, bidirectional=True),
mx.nd.ones((8, 3, 20)), [mx.nd.ones((4, 3, 10)), mx.nd.ones((4, 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)))
+ check_rnn_layer_forward(gluon.rnn.GRU(10, 2, bidirectional=True),
mx.nd.ones((8, 3, 20)), mx.nd.ones((4, 3, 10)))
+
+ check_rnn_layer_forward(gluon.rnn.RNN(10, 2, dropout=0.5), mx.nd.ones((8,
3, 20)),
+ run_only=True)
+ check_rnn_layer_forward(gluon.rnn.RNN(10, 2, bidirectional=True,
dropout=0.5),
+ mx.nd.ones((8, 3, 20)), mx.nd.ones((4, 3, 10)),
run_only=True)
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, dropout=0.5), mx.nd.ones((8,
3, 20)),
+ run_only=True)
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, bidirectional=True,
dropout=0.5),
+ mx.nd.ones((8, 3, 20)),
+ [mx.nd.ones((4, 3, 10)), mx.nd.ones((4, 3, 10))],
run_only=True)
+ check_rnn_layer_forward(gluon.rnn.GRU(10, 2, dropout=0.5), mx.nd.ones((8,
3, 20)),
+ run_only=True)
+ check_rnn_layer_forward(gluon.rnn.GRU(10, 2, bidirectional=True,
dropout=0.5),
+ mx.nd.ones((8, 3, 20)), mx.nd.ones((4, 3, 10)),
run_only=True)
net = gluon.nn.Sequential()
net.add(gluon.rnn.LSTM(10, 2, bidirectional=True))
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