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

nnvm/python/nnvm/frontend/darknet.py

@@ -412,7 +412,12 @@ class GraphProto(object):
         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
@@ -623,16 +628,16 @@ class GraphProto(object):
         """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."""
@@ -653,7 +658,7 @@ class GraphProto(object):
             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
@@ -678,7 +683,7 @@ class GraphProto(object):
             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
@@ -698,6 +703,123 @@ class GraphProto(object):
             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):

nnvm/python/nnvm/testing/darknet.py

@@ -491,6 +491,9 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse,
 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);
 """
                    )

nnvm/tests/python/frontend/darknet/test_forward.py

@@ -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
@@ -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
@@ -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)
@@ -395,4 +431,6 @@ if __name__ == '__main__':
     test_forward_elu()
     test_forward_rnn()
     test_forward_crnn()
-    test_forward_activation_logistic()
\ No newline at end of file
+    test_forward_lstm()
+    test_forward_gru()
+    test_forward_activation_logistic()