def _rnn_getter(f, attr):
if not attr.bidirectional:
raise NotImplementedError()
use_cudnn = (len(f.parameters) == 1
) # CNTK has only 1 big fat parameter when using cudnn
if use_cudnn:
gates = _get_rnn_gates(attr.op_type)
fw_Wt, fw_Ht, bw_Wt, bw_Ht, fw_b1, fw_b2, bw_b1, bw_b2 = np.split(
f.parameters[0].value.reshape(-1), _get_cudnn_rnn_splitter(attr))
return cstk.RnnArgs(fw_W=_adjust_lstm_gate_order(
fw_Wt.reshape(gates * attr.hidden_dim, -1).transpose()),
fw_H=_adjust_lstm_gate_order(
fw_Ht.reshape(gates * attr.hidden_dim,
-1).transpose()),
fw_b=_adjust_lstm_gate_order(fw_b1 + fw_b2),
bw_W=_adjust_lstm_gate_order(
bw_Wt.reshape(gates * attr.hidden_dim,
-1).transpose()),
bw_H=_adjust_lstm_gate_order(
bw_Ht.reshape(gates * attr.hidden_dim,
-1).transpose()),
bw_b=_adjust_lstm_gate_order(bw_b1 + bw_b2))
else:
param = _get_birnn_param(f)
return cstk.RnnArgs(fw_W=_parameter_getter(param.fw_W),
fw_H=_parameter_getter(param.fw_H),
fw_b=_parameter_getter(param.fw_b),
bw_W=_parameter_getter(param.bw_W),
bw_H=_parameter_getter(param.bw_H),
bw_b=_parameter_getter(param.bw_b))
def _get(scope, attr):
if not attr.bidirectional:
raise NotImplementedError()
fw_M, fw_b, bw_M, bw_b = _rnn_trainable_in_scope(scope)
fw_W, fw_H = np.split(_trainable_getter(sess)(fw_M), [attr.input_dim])
fw_b = _adjust_forget_bias(_trainable_getter(sess)(fw_b), attr.hidden_dim, attr.forget_bias)
bw_W, bw_H = np.split(_trainable_getter(sess)(bw_M), [attr.input_dim])
bw_b = _adjust_forget_bias(_trainable_getter(sess)(bw_b), attr.hidden_dim, attr.forget_bias)
return cstk.RnnArgs(fw_W=fw_W, fw_H=fw_H, fw_b=fw_b, bw_W=bw_W, bw_H=bw_H, bw_b=bw_b)
def _get_birnn_param(f):
if f.root_function.op_name != 'Splice':
raise NotImplementedError()
# assuming forward/backward cell first/second input to Splice
fw = f.root_function.inputs[0].owner
bw = f.root_function.inputs[1].owner
return cstk.RnnArgs(fw_W=find_func_param(fw, name='W'),
fw_H=find_func_param(fw, name='H'),
fw_b=find_func_param(fw, name='b'),
bw_W=find_func_param(bw, name='W'),
bw_H=find_func_param(bw, name='H'),
bw_b=find_func_param(bw, name='b'))
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_variable(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='birnn')
assert ci.compare('cntk_birnn_cudnn_out',
compare_name='birnn_out',
rtol=1e-4,
atol=1e-6)
ci.fetch('cntk_birnn_cudnn', save=True)
ci.assign('cntk_birnn_cudnn', load=True)
assert ci.compare('cntk_birnn_cudnn_out',
compare_name='birnn_out',
rtol=1e-4,
atol=1e-6)
# test assign with value
num_gates = 4
ci.assign('cntk_birnn_cudnn',
value=cstk.RnnArgs(
fw_W=np.random.random(
(in_dim, num_gates * dim)).astype(np.float32),
fw_H=np.random.random(
(dim, num_gates * dim)).astype(np.float32),
fw_b=np.random.random(
(num_gates * dim, )).astype(np.float32),
bw_W=np.random.random(
(in_dim, num_gates * dim)).astype(np.float32),
bw_H=np.random.random(
(dim, num_gates * dim)).astype(np.float32),
bw_b=np.random.random(
(num_gates * dim, )).astype(np.float32)))
ci.reset()