def cntk_baseline_lstm():
import cntk as C
import cntk.contrib.crosstalk.crosstalk_cntk as crct
ci = crct.instance
input_var = C.sequence.input_variable(shape=(in_dim))
fwbw = C.splice(
C.layers.Recurrence(C.layers.LSTM(
dim, init_bias=C.glorot_uniform()))(input_var),
C.layers.Recurrence(C.layers.LSTM(dim), go_backwards=True)(input_var))
ci.watch(fwbw,
'birnn',
var_type=cstk.RnnAttr,
attr=cstk.RnnAttr(bidirectional=True,
op_type='lstm',
input_dim=in_dim,
hidden_dim=dim,
forget_bias=0))
ci.watch(fwbw, 'birnn_out')
data = {input_var: data_cntk}
ci.set_data(data)
ci.set_workdir(workdir)
ci.fetch('birnn', save=True)
ci.fetch('birnn_out', save=True)
ci.reset()
def test_cntk_cudnn():
try:
import tensorflow
has_tensorflow = True
except:
has_tensorflow = False
if has_tensorflow:
tf_baseline_lstm()
else:
cntk_baseline_lstm()
import cntk as C
import cntk.contrib.crosstalk.crosstalk_cntk as crct
ci = crct.instance
input_var = C.sequence.input(shape=(in_dim))
data = {input_var:data_cntk}
ci.set_data(data)
ci.set_workdir(workdir)
W = C.parameter((-1,dim,), init=C.glorot_uniform())
cudnn_fwbw = C.optimized_rnnstack(input_var, W, dim, 1, bidirectional=True, recurrent_op='lstm')
ci.watch(cudnn_fwbw, 'cntk_birnn_cudnn', var_type=cstk.RnnAttr,
attr=cstk.RnnAttr(bidirectional=True, op_type='lstm', input_dim=in_dim, hidden_dim=dim, forget_bias=0))
ci.watch(cudnn_fwbw, 'cntk_birnn_cudnn_out')
ci.assign('cntk_birnn_cudnn', load=True, load_name='cntk_birnn')
assert ci.compare('cntk_birnn_cudnn_out', compare_name='cntk_birnn_out')
ci.fetch('cntk_birnn_cudnn', save=True)
ci.assign('cntk_birnn_cudnn', load=True)
assert ci.compare('cntk_birnn_cudnn_out', compare_name='cntk_birnn_out')
ci.reset()
def tf_baseline_lstm():
import tensorflow as tf
import cntk.contrib.crosstalk.crosstalk_tensorflow as crtf
ci = crtf.instance
tf.reset_default_graph()
with tf.variable_scope("rnn"):
x = tf.placeholder(tf.float32, [batch_size, max_seq_len, in_dim])
l = tf.placeholder(tf.int32, [batch_size])
if tf.VERSION.startswith('0.12'):
cell = tf.nn.rnn_cell.BasicLSTMCell(dim)
(fw, bw), _ = tf.nn.bidirectional_dynamic_rnn(cell,
cell,
x,
l,
dtype=tf.float32)
scope = 'rnn/BiRNN'
elif tf.VERSION.startswith('1'):
(fw, bw), _ = tf.nn.bidirectional_dynamic_rnn(
tf.contrib.rnn.BasicLSTMCell(dim),
tf.contrib.rnn.BasicLSTMCell(dim),
x,
l,
dtype=tf.float32)
scope = 'rnn/bidirectional_rnn'
else:
raise Exception('only supports 0.12.* and 1.*')
ci.watch(scope,
'birnn',
var_type=cstk.RnnAttr,
attr=cstk.RnnAttr(bidirectional=True,
op_type='lstm',
input_dim=in_dim,
hidden_dim=dim,
forget_bias=1)) # tf default forget_bias==1
if tf.VERSION.startswith('0.12'):
output = tf.concat(2, [fw, bw])
elif tf.VERSION.startswith('1'):
output = tf.concat([fw, bw], 2)
else:
raise Exception('only supports 0.12.* and 1.*')
ci.watch(output, 'birnn_out', var_type=crtf.VariableType)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
data = {x: data_tf, l: data_tf_len}
ci.set_workdir(workdir)
ci.set_data(sess, data)
ci.fetch('birnn', save=True)
ci.fetch('birnn_out', save=True)
ci.reset()
sess.close()