[FRONTEND][TENSORFLOW] Support Unstack and SplitV

Author: alexeyr

nnvm/python/nnvm/frontend/tensorflow.py

@@ -787,13 +787,55 @@ def _impl(inputs, attr, params):
         )(inputs, attr)
     return _impl
 
-def _split():
+def _split(has_size_vector):
+    # TF documentation https://www.tensorflow.org/api_docs/python/tf/split
     def _impl(inputs, attr, params):
-        axis = params.pop(inputs[0].list_output_names()[0])
-        return AttrCvt(
-            op_name="split", ignores=['T'],
-            transforms={'num_split': 'indices_or_sections'},
-            extras={'axis': axis.asnumpy()[0]})(inputs[1], attr)
+        try:
+            # order and number of inputs are different:
+            # if has_size_vector:
+            #     https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/split-v
+            # else:
+            #     https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/split
+
+            # in addition, `axis` and `num_or_size_splits` can be tensors in TensorFlow,
+            # we can only support constants
+            if has_size_vector:
+                input_node_index = 0
+                input_axis_index = 2
+                size_splits_input_name = inputs[1].list_output_names()[0]
+                size_splits = params[size_splits_input_name].asnumpy()
+                section_beginnings = np.cumsum(size_splits)[:-1]
+                indices_or_sections = tuple(section_beginnings)
+            else:
+                input_node_index = 1
+                input_axis_index = 0
+                indices_or_sections = attr['num_split']
+            input_node = inputs[input_node_index]
+            axis_input_name = inputs[input_axis_index].list_output_names()[0]
+            axis_input_value = params[axis_input_name].asnumpy()[0]
+        except (IndexError, KeyError):
+            raise TypeError( \
+                "Unsupported argument for split: `axis` and `num_or_size_splits` " \
+                "should be constants")
+        return _sym.split(input_node,
+                          indices_or_sections=indices_or_sections,
+                          axis=axis_input_value)
+    return _impl
+
+def _unpack():
+    def _impl(inputs, attr, params):
+        input_node = inputs[0]
+        axis = attr['axis']
+        input_shape = attr['_input_shapes'][input_node][0]
+        axis_length = input_shape[axis]
+        if axis_length < 0:
+            raise TypeError("Unstack with unknown axis length")
+        splitted = _sym.split(input_node,
+                              indices_or_sections=axis_length,
+                              axis=axis,
+                              name=attr.get('_node_name', 'unstack'))
+
+        return _sym.Group([_sym.squeeze(split_item, axis=axis) for split_item in splitted])
     return _impl
 
 # compatible operators that do NOT require any conversion.
@@ -863,7 +905,9 @@ def _impl(inputs, attr, params):
     'GreaterEqual'                      : _broadcast('greater_equal'),
     'Equal'                             : _broadcast('equal'),
     'NotEqual'                          : _broadcast('not_equal'),
-    'Split'                             : _split(),
+    'Split'                             : _split(False),
+    'SplitV'                            : _split(True),
+    'Unpack'                            : _unpack(),
 }
 
 # _convert_map_rnn defines maps of rnn operator name to
@@ -1162,19 +1206,21 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
                 # Fill shapes for all inputs in a list
                 inputs = []
                 for i in node.input:
-                    #ToDo: Some of the tensorflow operators internaly maintain
-                    #execution layers and its output name will the layer number along with
-                    #graph node name.eg: Node name:- 'Model/RNN/cell_0/RnnCell', but the
-                    #output name will be 'Model/RNN/cell_0/RnnCell:0'. In this case,
-                    #the digit has to be ignored.
+                    # Some TensorFlow operators internally maintain execution layers
+                    # and their output name includes the layer number along with
+                    # graph node name. E.g. the node name is 'Model/RNN/cell_0/RnnCell', but the
+                    # output tensor name is 'Model/RNN/cell_0/RnnCell:0'. In this case,
+                    # the number has to be ignored for single-output nodes.
+                    # On the other hand, for multi-output nodes the number is the output index,
+                    # and the lack of the number implies 0.
                     tensor_name = i.split(':')
                     node_name = tensor_name[0]
                     if node_name in self._nodes:
                         in_sym = self._nodes[node_name]
                         if len(in_sym.list_output_names()) > 1:
                             tensor_slot = int(tensor_name[1]) if len(tensor_name) > 1 else 0
                             in_sym = in_sym[tensor_slot]
-                            input_shape = (self._output_shapes[node_name])[tensor_slot]
+                            input_shape = self._output_shapes[node_name][tensor_slot]
                         else:
                             input_shape = self._output_shapes[node_name][0]
                         inputs.append(in_sym)
@@ -1207,15 +1253,21 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
         if outputs is None:
             out.append(final_op)
         else:
-            out = [self._nodes[out_name] for out_name in outputs]
+            for out_name in outputs:
+                if ":" in out_name:
+                    out_name, out_num = out_name.split(":")
+                    out_num = int(out_num)
+                    out.append(self._nodes[out_name][out_num])
+                else:
+                    out.append(self._nodes[out_name])
 
         #Add the RNN outputs also with 'head' nodes of the nnvm graph
         if self._num_rnn_layer:
             out_rnn = _sym.concatenate(*self._out_rnn, axis=0)
             out.append(out_rnn)
 
         if isinstance(out, list):
-            out = _sym.Group(out)
+            out = _sym.Group(out) if len(out) > 1 else out[0]
 
         return out, self._params
 

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

@@ -124,7 +124,8 @@ def compare_tf_with_tvm(in_data, in_name, out_name, init_global_variables=False,
             if no_gpu and device == 'cuda':
                 continue
 
-            tvm_output = run_tvm_graph(final_graph_def, in_data, in_node, target=device)
+            tvm_output = run_tvm_graph(final_graph_def, in_data, in_node,
+                                       num_output=len(out_node), target=device, out_names=out_name)
             # since the names from tensorflow and nnvm runs are not exactly same, 
             # first len(tf_output) will be compared
             for i in range(len(tf_output)):
@@ -506,14 +507,24 @@ def test_forward_gather():
 # Split
 # -----
 
-def _test_split(in_shape, axis, num_split, dtype):
+def _test_split(in_shape, axis, num_or_size_splits, dtype):
+    np_data = np.random.uniform(-5, 5, size=in_shape).astype(dtype)
+
     """ One iteration of a Split """
+    tf.reset_default_graph()
+    in_data = tf.placeholder(dtype, in_shape, name="in_data")
+    num_split = len(num_or_size_splits) if isinstance(num_or_size_splits, list) else num_or_size_splits
+    tf.split(in_data, num_or_size_splits, axis=axis)
 
-    with tf.Graph().as_default():
-        in_data = tf.placeholder(dtype, in_shape, name="in_data")
-        tf.split(in_data, num_split, axis)
-        np_data = np.random.uniform(size=in_shape).astype(dtype)
-        compare_tf_with_tvm(np_data, 'in_data:0', 'split:0')
+    compare_tf_with_tvm([np_data], ['in_data:0'], [f'split:{n}' for n in range(num_split)])
+
+    # and now test together with concat
+    tf.reset_default_graph()
+    in_data = tf.placeholder(dtype, in_shape, name="in_data")
+    splitted = tf.split(in_data, num_or_size_splits, axis=axis)
+    tf.concat(splitted, axis)
+
+    compare_tf_with_tvm([np_data], 'in_data:0', 'concat:0')
 
 def test_forward_split():
     '''test split layer'''
@@ -523,11 +534,11 @@ def test_forward_split():
     _test_split((6,), 0, 3, 'float32')
     # rank 2
     _test_split((6, 2), 0, 3, 'float32')
-    _test_split((2, 6), 1, 3, 'float32')
+    _test_split((2, 6), 1, 6, 'float32')
     # rank 3
-    _test_split((6, 2, 4), 0, 3, 'float32')
+    _test_split((6, 2, 4), 0, 2, 'int32')
     _test_split((2, 6, 4), 1, 3, 'float32')
-    _test_split((2, 4, 6), 2, 3, 'float32')
+    _test_split((2, 4, 6), 2, 1, 'float32')
     # rank 4
     _test_split((6, 1, 3, 5), 0, 3, 'float32')
     _test_split((1, 6, 3, 5), 1, 3, 'float32')
@@ -538,45 +549,30 @@ def test_forward_split():
     _test_split((1, 6, 3, 5), -3, 3, 'float32')
     _test_split((1, 3, 6, 5), -2, 3, 'float32')
     _test_split((1, 3, 5, 6), -1, 3, 'float32')
+    # size_splits list
+    _test_split((6,), 0, [1, 2, 3], 'int32')
+    _test_split((3, 6, 4), -2, [1, 4, 1], 'float32')
 
 
 #######################################################################
-# Split followed by concat
-# ------------------------
+# Unstack
+# -------
 
-def _test_split_concat(in_shape, axis, num_split, dtype):
-    """ One iteration of a split_concat pair"""
+def _test_unstack(ip_shape, axis, dtype):
+    tf.reset_default_graph()
+    in_data = tf.placeholder(dtype, ip_shape, name="in_data")
+    tf.unstack(in_data, axis=axis)
+    np_data = np.random.uniform(-5, 5, size=ip_shape).astype(dtype)
 
-    with tf.Graph().as_default():
-        in_data = tf.placeholder(dtype, in_shape, name="in_data")
-        splitted = tf.split(in_data, num_split, axis)
-        tf.concat(splitted, axis)
-        np_data = np.random.uniform(size=in_shape).astype(dtype)
-        compare_tf_with_tvm(np_data, 'in_data:0', 'concat:0')
-
-def test_forward_split_concat():
-    '''test split followed by concat layers'''
-    # rank 1
-    _test_split_concat((3,), 0, 1, 'float32')
-    _test_split_concat((3,), 0, 3, 'float32')
-    _test_split_concat((6,), 0, 3, 'float32')
-    # rank 2
-    _test_split_concat((6, 2), 0, 3, 'float32')
-    _test_split_concat((2, 6), 1, 3, 'float32')
-    # rank 3
-    _test_split_concat((6, 2, 4), 0, 3, 'float32')
-    _test_split_concat((2, 6, 4), 1, 3, 'float32')
-    _test_split_concat((2, 4, 6), 2, 3, 'float32')
-    # rank 4
-    _test_split((6, 1, 3, 5), 0, 3, 'float32')
-    _test_split((1, 6, 3, 5), 1, 3, 'float32')
-    _test_split((1, 3, 6, 5), 2, 3, 'float32')
-    _test_split((1, 3, 5, 6), 3, 3, 'float32')
-    # split along negative axis
-    _test_split((6, 1, 3, 5), -4, 3, 'float32')
-    _test_split((1, 6, 3, 5), -3, 3, 'float32')
-    _test_split((1, 3, 6, 5), -2, 3, 'float32')
-    _test_split((1, 3, 5, 6), -1, 3, 'float32')
+    compare_tf_with_tvm([np_data], ['in_data:0'], [f'unstack:{n}' for n in range(ip_shape[axis])])
+
+def test_forward_unstack():
+    '''test unstack layer'''
+    _test_unstack((6,), 0, 'int32')
+    _test_unstack((2,6), 1, 'float64')
+    # negative axis
+    _test_unstack((1,4), -1, 'int32')
+    _test_unstack((3,6,4), -2, 'float32')
 
 
 #######################################################################
@@ -1139,7 +1135,7 @@ def test_forward_rel_ops():
     test_forward_gather()
     test_forward_stridedslice()
     test_forward_split()
-    test_forward_split_concat()
+    test_forward_unstack()
 
     # Activations
     test_forward_sigmoid()