Add support for Tflite operator SPLIT (#3520)

* [RFC] Initial support for Tflite operator SPLIT

This patch adds initial support for the tflite operator split. However
I am not yet sure how to handle the axis parameter for the split
operator and support it in the test infrastructure. Putting this up for
an initial review and comment.

The split operator in tflite according to
https://www.tensorflow.org/lite/guide/ops_compatibility

appears to take num_or_size_split as a 0D tensor.

I also note that tflite.split is one of the few operators that returns
multiple outputs and thus the helper routines in the tests needed some
massaging to make this work.

@apivarov , could you please review this ?

Thanks,
Ramana

* Fix the axis parameter

Add more tests

* Address review comments

* Try out frozen_gene's suggestion

* Handle split of 1 element

* int32 is only supported in tflite 1.14, let's check that version here.

* Keep this at python3.5

* Add packaging as a python package to be installed

docker/install/ubuntu_install_python_package.sh

@@ -21,5 +21,5 @@ set -u
 set -o pipefail
 
 # install libraries for python package on ubuntu
-pip2 install nose pylint==1.9.4 six numpy nose-timer cython decorator scipy tornado typing antlr4-python2-runtime attrs
-pip3 install nose pylint==1.9.4 six numpy nose-timer cython decorator scipy tornado typed_ast pytest mypy orderedset antlr4-python3-runtime attrs requests Pillow
+pip2 install nose pylint==1.9.4 six numpy nose-timer cython decorator scipy tornado typing antlr4-python2-runtime attrs packaging
+pip3 install nose pylint==1.9.4 six numpy nose-timer cython decorator scipy tornado typed_ast pytest mypy orderedset antlr4-python3-runtime attrs requests Pillow packaging

python/tvm/relay/frontend/tflite.py

@@ -81,6 +81,7 @@ def __init__(self, model, subgraph, exp_tab):
             'PAD': self.convert_pad,
             'PACK': self.convert_pack,
             'LOGISTIC': self.convert_logistic,
+            'SPLIT': self.convert_split
         }
 
     def check_unsupported_ops(self):
@@ -705,6 +706,43 @@ def convert_conv(self, op, conv_type):
 
         return out
 
+    def convert_split(self, op):
+        """split implementation."""
+        try:
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.Operator import Operator
+            from tflite.SplitOptions import SplitOptions
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        assert isinstance(op, Operator)
+        input_tensors = self.get_input_tensors(op)
+
+        assert len(input_tensors) == 2, "input tensors length should be == 2"
+
+        axis_tensor = input_tensors[0]
+        split_axis = self.get_tensor_value(axis_tensor)
+        input_tensor = input_tensors[1]
+        input_tensor_idx = input_tensor.tensor_idx
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.SplitOptions
+        op_options = op.BuiltinOptions()
+        split_options = SplitOptions()
+        split_options.Init(op_options.Bytes, op_options.Pos)
+        num_splits = split_options.NumSplits()
+
+        in_expr = self.get_expr(input_tensor_idx)
+        out = _op.split(in_expr, num_splits, axis=int(split_axis))
+        # Relay does not like a TupleWrapper of 1 element, further this
+        # only shows up with tf1.13 if we use a split with num_splits==1.
+        # In tf 1.14 this doesn't appear as it is automatically a reshape
+        # operation.
+        if isinstance(out, _expr.TupleWrapper):
+            if out.size == 1:
+                out = out[0]
+
+        return out
+
     def convert_pool2d(self, op, pool_type):
         """pool2d implementation."""
         try:

tests/python/frontend/tflite/test_forward.py

@@ -38,6 +38,7 @@
     from tensorflow.contrib import lite as interpreter_wrapper
 
 import tvm.relay.testing.tf as tf_testing
+from packaging import version as package_version
 
 #######################################################################
 # Generic run functions for TVM & TFLite
@@ -120,10 +121,11 @@ def run_tflite_graph(tflite_model_buf, input_data):
 
 
 def compare_tflite_with_tvm(in_data, in_name, input_tensors,
-                            output_tensors, init_global_variables=False):
+                            output_tensors, init_global_variables=False, out_names=None):
     """Generic function to generate and compare TFLite and TVM output"""
     in_data = convert_to_list(in_data)
     in_name = convert_to_list(in_name)
+    out_names = convert_to_list(out_names)
     in_node = [0] * len(in_name)
     for i in range(len(in_name)):
         in_node[i] = in_name[i].split(':')[0] if ":" in in_name[i] else in_name[i]
@@ -143,7 +145,8 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
                 print("Skip because %s is not enabled" % device)
                 continue
 
-            tvm_output = run_tvm_graph(tflite_model_buffer, in_data, in_node, target=device)
+            tvm_output = run_tvm_graph(tflite_model_buffer, in_data, in_node, target=device,
+                                       num_output=len(out_names), out_names=out_names)
             for i in range(len(tflite_output)):
                 tvm.testing.assert_allclose(tflite_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
 
@@ -161,6 +164,42 @@ def with_fused_activation_function(input_tensor, fn_name):
         return math_ops.tanh(input_tensor)
     raise AssertionError("Unknown fused_activation_function {}".format(fn_name))
 
+def _test_split(in_shape, axis, num_Splits, dtype):
+    '''internal split tester taking as parameters in_shape, number of tensors to split into
+       and dtype (data type)'''
+    np_data = np.random.uniform(-5, 5, size=in_shape).astype(dtype)
+    with tf.Graph().as_default():
+        in_data = array_ops.placeholder(shape=in_shape, dtype=dtype)
+        out = array_ops.split(in_data, num_Splits, axis=axis)
+        out_names = ['out_' + str(n) + ':0' for n in range(num_Splits)]
+        compare_tflite_with_tvm([np_data], ['Placeholder:0'],  [in_data], out,
+                                out_names=out_names)
+
+def test_forward_split():
+    '''test split layer'''
+    # rank 1
+    _test_split((3,), 0, 1, 'float32')
+    _test_split((3,), 0, 3, 'float32')
+    _test_split((6,), 0, 3, 'float32')
+    # rank 2
+    _test_split((6, 2), 0, 3, 'float32')
+    _test_split((2, 6), 1, 6, 'float32')
+    # rank 3
+    if package_version.parse(tf.VERSION) >= package_version.parse('1.14.0'):
+        _test_split((6, 2, 4), 0, 2, 'int32')
+
+    _test_split((2, 6, 4), 1, 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')
+    _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')
 
 #######################################################################
 # Pooling
@@ -782,6 +821,8 @@ def test_forward_ssd_mobilenet_v1():
 # Main
 # ----
 if __name__ == '__main__':
+    # Split
+    test_forward_split()
     # Transforms
     test_forward_concatenation()
     test_forward_pad()