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[FRONTEND][DARKNET]LSTM and GRU support #1576

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Merged
tqchen merged 3 commits into from
Aug 14, 2018
Merged

[FRONTEND][DARKNET]LSTM and GRU support #1576

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8b86982
[FRONTEND][DARKNET]LSTM and GRU support
siju-samuel Aug 9, 2018
6697ca8
Review comments updated
siju-samuel Aug 13, 2018
e13ee92
Merge branch 'master' into darknet_lstm_gru
siju-samuel Aug 14, 2018
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140 changes: 131 additions & 9 deletions nnvm/python/nnvm/frontend/darknet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -412,7 +412,12 @@ def __init__(self, net, dtype='float32'):
self._sym_array = {}
self._tvmparams = {}
self._outs = []
self._rnn_state_ctr = 0
self._state_ctr = {}
self._state_ctr['rnn'] = 0
self._state_ctr['crnn'] = 0
self._state_ctr['lstm'] = 0
self._state_ctr['cell_state'] = 0
self._state_ctr['gru'] = 0

def _read_memory_buffer(self, shape, data):
length = 1
Expand Down Expand Up @@ -623,16 +628,16 @@ def _get_opname(self, layer):
"""Returs the layer name."""
return layer.type

def _new_rnn_state_sym(self, state=None):
def _new_rnn_state_sym(self, state=None, name='rnn'):
"""Returs a symbol for state"""
name = "rnn%d_state" % (self._rnn_state_ctr)
self._rnn_state_ctr += 1
return _sym.Variable(name=name, init=state)
sym_name = name + "%d_state" % self._state_ctr[name]
self._state_ctr[name] += 1
return _sym.Variable(name=sym_name, init=state)

def _get_rnn_state_buffer(self, layer):
def _get_rnn_state_buffer(self, layer, name):
"""Get the state buffer for rnn."""
buffer = np.zeros((1, layer.outputs), self.dtype)
return self._new_rnn_state_sym(buffer)
return self._new_rnn_state_sym(buffer, name)

def _get_darknet_rnn_attrs(self, layer, sym):
"""Get the rnn converted symbol from attributes."""
Expand All @@ -653,7 +658,7 @@ def _handle_darknet_rnn_layers(self, layer_num, sym):
attr.update({'batch' : layer.batch})
attr.update({'num_hidden' : str(layer.outputs)})

state = self._get_rnn_state_buffer(layer)
state = self._get_rnn_state_buffer(layer, 'rnn')

for _ in range(layer.steps):
input_layer = layer.input_layer
Expand All @@ -678,7 +683,7 @@ def _handle_darknet_rnn_layers(self, layer_num, sym):
attr.update({'batch' : layer.batch})
attr.update({'num_hidden' : str(layer.outputs)})

state = self._get_rnn_state_buffer(layer)
state = self._get_rnn_state_buffer(layer, 'crnn')

for _ in range(layer.steps):
input_layer = layer.input_layer
Expand All @@ -698,6 +703,123 @@ def _handle_darknet_rnn_layers(self, layer_num, sym):
self._sym_array[layer_num] = sym
processed = True

elif LAYERTYPE.LSTM == layer.type:
if layer.steps > 1:
raise NotImplementedError("Currently support only single step GRU")

op_name_add = 'elemwise_add'
op_name_mul = 'elemwise_mul'
attrs = {}
act_attr = {}

h_state = self._get_rnn_state_buffer(layer, 'lstm')
c_state = self._get_rnn_state_buffer(layer, 'cell_state')
for _ in range(layer.steps):
sym_wf = self._get_darknet_rnn_attrs(layer.wf, h_state)
sym_wi = self._get_darknet_rnn_attrs(layer.wi, h_state)
sym_wg = self._get_darknet_rnn_attrs(layer.wg, h_state)
sym_wo = self._get_darknet_rnn_attrs(layer.wo, h_state)

input_sym = sym
sym_uf = self._get_darknet_rnn_attrs(layer.uf, input_sym)
sym_ui = self._get_darknet_rnn_attrs(layer.ui, input_sym)
sym_ug = self._get_darknet_rnn_attrs(layer.ug, input_sym)
sym_uo = self._get_darknet_rnn_attrs(layer.uo, input_sym)

new_inputs = _as_list([sym_wf, sym_uf])
add_f = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

new_inputs = _as_list([sym_wi, sym_ui])
add_i = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

new_inputs = _as_list([sym_wg, sym_ug])
add_g = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

new_inputs = _as_list([sym_wo, sym_uo])
add_o = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

act_attr['activation'] = ACTIVATION.LOGISTIC
act_f, _ = _darknet_activations(_as_list(add_f), act_attr)

act_attr['activation'] = ACTIVATION.LOGISTIC
act_i, _ = _darknet_activations(_as_list(add_i), act_attr)

act_attr['activation'] = ACTIVATION.TANH
act_g, _ = _darknet_activations(_as_list(add_g), act_attr)

act_attr['activation'] = ACTIVATION.LOGISTIC
act_o, _ = _darknet_activations(_as_list(add_o), act_attr)

new_inputs = _as_list([act_i, act_g])
mul_t = _darknet_get_nnvm_op(op_name_mul)(*new_inputs, **attrs)

new_inputs = _as_list([act_f, c_state])
c_state = _darknet_get_nnvm_op(op_name_mul)(*new_inputs, **attrs)

new_inputs = _as_list([mul_t, c_state])
c_state = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

act_attr['activation'] = ACTIVATION.TANH
h_state, _ = _darknet_activations(_as_list(c_state), act_attr)

new_inputs = _as_list([act_o, h_state])
h_state = _darknet_get_nnvm_op(op_name_mul)(*new_inputs, **attrs)
self._outs = self._outs + [c_state, h_state]
sym = h_state
self._sym_array[layer_num] = sym
processed = True

elif LAYERTYPE.GRU == layer.type:
if layer.steps > 1:
raise NotImplementedError("Currently support only single step GRU")

op_name_add = 'elemwise_add'
op_name_mul = 'elemwise_mul'
attrs = {}
act_attr = {}

state = self._get_rnn_state_buffer(layer, "gru")
for _ in range(layer.steps):
sym_wz = self._get_darknet_rnn_attrs(layer.wz, state)
sym_wr = self._get_darknet_rnn_attrs(layer.wr, state)

input_sym = sym
sym_uz = self._get_darknet_rnn_attrs(layer.uz, input_sym)
sym_ur = self._get_darknet_rnn_attrs(layer.ur, input_sym)
sym_uh = self._get_darknet_rnn_attrs(layer.uh, input_sym)

new_inputs = _as_list([sym_uz, sym_wz])
add_z = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

new_inputs = _as_list([sym_ur, sym_wr])
add_r = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

act_attr['activation'] = ACTIVATION.LOGISTIC
act_z, _ = _darknet_activations(_as_list(add_z), act_attr)

act_attr['activation'] = ACTIVATION.LOGISTIC
act_r, _ = _darknet_activations(_as_list(add_r), act_attr)

new_inputs = _as_list([act_r, state])
forgot = _darknet_get_nnvm_op(op_name_mul)(*new_inputs, **attrs)

sym_wh = self._get_darknet_rnn_attrs(layer.wh, forgot)

new_inputs = _as_list([sym_uh, sym_wh])
h_state = _darknet_get_nnvm_op(op_name_add)(*new_inputs, **attrs)

if layer.tanh == 1:
act_attr['activation'] = ACTIVATION.TANH
else:
act_attr['activation'] = ACTIVATION.LOGISTIC
h_state, _ = _darknet_activations(_as_list(h_state), act_attr)

sym = act_z * state + (1 - act_z) * h_state

self._outs = self._outs + [sym]
self._sym_array[layer_num] = sym
processed = True

return processed, sym

def from_darknet(self):
Expand Down
3 changes: 3 additions & 0 deletions nnvm/python/nnvm/testing/darknet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -491,6 +491,9 @@ class ACTIVATION(object):
layer make_region_layer(int batch, int w, int h, int n, int classes, int coords);
layer make_softmax_layer(int batch, int inputs, int groups);
layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam);
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize);
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
void free_network(network *net);
"""
)
44 changes: 41 additions & 3 deletions nnvm/tests/python/frontend/darknet/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,7 +306,7 @@ def test_forward_softmax_temperature():
LIB.free_network(net)

def test_forward_rnn():
'''test softmax layer'''
'''test RNN layer'''
net = LIB.make_network(1)
batch = 1
inputs = 256
Expand All @@ -325,7 +325,7 @@ def test_forward_rnn():
LIB.free_network(net)

def test_forward_crnn():
'''test softmax layer'''
'''test CRNN layer'''
net = LIB.make_network(1)
batch = 1
c = 3
Expand All @@ -349,6 +349,42 @@ def test_forward_crnn():
test_forward(net)
LIB.free_network(net)

def test_forward_lstm():
'''test LSTM layer'''
net = LIB.make_network(1)
batch = 1
inputs = 256
outputs = 256
steps = 1
batch_normalize = 0
adam = 0
layer_1 = LIB.make_lstm_layer(batch, inputs, outputs, steps, batch_normalize, adam)
net.layers[0] = layer_1
net.inputs = inputs
net.outputs = outputs
net.w = net.h = 0
LIB.resize_network(net, net.w, net.h)
test_rnn_forward(net)
LIB.free_network(net)

def test_forward_gru():
'''test GRU layer'''
net = LIB.make_network(1)
batch = 1
inputs = 256
outputs = 256
steps = 1
batch_normalize = 0
adam = 0
layer_1 = LIB.make_gru_layer(batch, inputs, outputs, steps, batch_normalize, adam)
net.layers[0] = layer_1
net.inputs = inputs
net.outputs = outputs
net.w = net.h = 0
LIB.resize_network(net, net.w, net.h)
test_rnn_forward(net)
LIB.free_network(net)

def test_forward_activation_logistic():
'''test logistic activation layer'''
net = LIB.make_network(1)
Expand Down Expand Up @@ -395,4 +431,6 @@ def test_forward_activation_logistic():
test_forward_elu()
test_forward_rnn()
test_forward_crnn()
test_forward_activation_logistic()
test_forward_lstm()
test_forward_gru()
test_forward_activation_logistic()