Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into int8_quantize

src/onnx/onnx.cpp

@@ -675,7 +675,6 @@ struct onnx_parser
         float epsilon                                     = 1e-5f;
         float momentum                                    = 0.9f;
         op::batch_norm_inference::bn_infer_mode_t bn_mode = op::batch_norm_inference::spatial;
-        bool is_test                                      = false;
         if(contains(attributes, "epsilon"))
         {
             epsilon = parse_value(attributes.at("epsilon")).at<float>();
@@ -684,17 +683,12 @@ struct onnx_parser
         {
             momentum = parse_value(attributes.at("momentum")).at<float>();
         }
-        if(contains(attributes, "is_test"))
-        {
-            is_test = parse_value(attributes.at("is_test")).at<uint64_t>() > 0;
-        }
         if(contains(attributes, "spatial"))
         {
             bn_mode = (parse_value(attributes.at("spatial")).at<uint64_t>() > 0)
                           ? op::batch_norm_inference::spatial
                           : op::batch_norm_inference::per_activation;
         }
-        (void)is_test;
         op::batch_norm_inference op{epsilon, momentum, bn_mode};
         return prog.add_instruction(op, std::move(args));
     }

test/onnx/constant_scalar.onnx → test/onnx/constant_scalar_test.onnx

-constant-scalar-example:R
+constant-scalar-example:R
 0
0"Constant*!
 value*
*

Bconst_tensor 

test-constantb
 
0
 
 

-
B
\ No newline at end of file
+
B

test/onnx/conv_bias_test.onnx

+conv-example:­

+8
+
0
+
1
+
2
3"Conv*
+	dilations@
@
 
*
+strides@
@
 
	test_convZ
+
0
+

+

+
+ 
+ Z
+
1
+

+

+
+
+Z
+
2
+
+

+
b
+
3
+

+

+
+
+B

test/onnx/gen_onnx.py

+import numpy as np
+import onnx
+from onnx import helper
+from onnx import numpy_helper
+from onnx import AttributeProto, TensorProto, GraphProto
+
+
+def onnx_test(op_test):
+    def run_test():
+        op_info = op_test()
+        if len(op_info) > 3:
+            graph_def = helper.make_graph(op_info[0],
+                                          op_test.__name__,
+                                          op_info[1],
+                                          op_info[2],
+                                          initializer=op_info[3])
+        else:
+            graph_def = helper.make_graph(op_info[0], op_test.__name__,
+                                          op_info[1], op_info[2])
+        model_def = helper.make_model(graph_def,
+                                      producer_name=op_test.__name__)
+        onnx.save(model_def, '{}.onnx'.format(op_test.__name__))
+
+    return run_test
+
+
+@onnx_test
+def acos_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Acos',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def add_bcast_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node('Add',
+                                 inputs=['0', '1'],
+                                 broadcast=1,
+                                 axis=1,
+                                 outputs=['2'])
+
+    return ([node], [x, y], [z])
+
+
+@onnx_test
+def add_fp16_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1])
+
+    node = onnx.helper.make_node(
+        'Add',
+        inputs=['0', '1'],
+        outputs=['2'],
+    )
+
+    return (
+        [node],
+        [x, y],
+        [z],
+        # '0' -> 1.5, '1' -> 2.5
+        [
+            onnx.helper.make_tensor('0', TensorProto.FLOAT16, [1], [15872]),
+            onnx.helper.make_tensor('1', TensorProto.FLOAT16, [1], [16640])
+        ])
+
+
+@onnx_test
+def add_scalar_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node('Add', inputs=['0', '1'], outputs=['2'])
+
+    return ([node], [x, y], [z],
+            [helper.make_tensor('1', TensorProto.FLOAT, [], [1])])
+
+
+@onnx_test
+def argmax_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
+
+    node = onnx.helper.make_node('ArgMax',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axis=2,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def argmin_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
+
+    node = onnx.helper.make_node('ArgMin',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axis=3,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def asin_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Asin',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def atan_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Atan',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def cast_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node('Cast', inputs=['x'], outputs=['y'], to=1)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def clip_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node('Clip',
+                                 inputs=['0'],
+                                 outputs=['1'],
+                                 max=6.0,
+                                 min=0.0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def concat_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [9, 4, 3])
+
+    node = onnx.helper.make_node(
+        'Concat',
+        inputs=['0', '1'],
+        axis=0,
+        outputs=['2'],
+    )
+
+    return ([node], [x, y], [z])
+
+
+@onnx_test
+def constant_test():
+    x = np.array([0, 1, 2])
+    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['0'],
+        value=onnx.helper.make_tensor(
+            name='const_tensor',
+            data_type=TensorProto.FLOAT,
+            dims=x.shape,
+            vals=x.flatten().astype(float),
+        ),
+    )
+
+    return ([node], [], [y])
+
+
+@onnx_test
+def constant_fill_test():
+    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])
+
+    node = onnx.helper.make_node(
+        'ConstantFill',
+        inputs=[],
+        outputs=['value'],
+        dtype=1,
+        value=1.0,
+        shape=[2, 3],
+        input_as_shape=0,
+    )
+
+    return ([node], [], [value])
+
+
+@onnx_test
+def constant_fill_input_as_shape_test():
+    np_shape = np.array([2, 3])
+    shape = helper.make_tensor_value_info('shape', TensorProto.INT32, [2])
+    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])
+
+    ts_shape = helper.make_tensor(name='shape_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=np_shape.shape,
+                                  vals=np_shape.flatten().astype(int))
+
+    const_shape_node = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=ts_shape,
+    )
+
+    node = onnx.helper.make_node(
+        'ConstantFill',
+        inputs=['shape'],
+        outputs=['value'],
+        dtype=1,
+        value=1.0,
+        input_as_shape=1,
+    )
+
+    return ([const_shape_node, node], [], [value])
+
+
+@onnx_test
+def constant_scalar_test():
+    x = np.array([1])
+    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1])
+
+    node = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['0'],
+        value=onnx.helper.make_tensor(
+            name='const_tensor',
+            data_type=TensorProto.INT32,
+            dims=x.shape,
+            vals=x.flatten().astype(int),
+        ),
+    )
+
+    return ([node], [], [y])
+
+
+@onnx_test
+def const_of_shape_empty_input_test():
+    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
+                                         [10])
+    shape_val = np.array([2, 3, 4]).astype(np.int64)
+    empty_val = np.array([]).astype(np.int64)
+    empty_ts = helper.make_tensor(name='empty_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=empty_val.shape,
+                                  vals=empty_val.flatten().astype(int))
+    shape_const = helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=empty_ts,
+    )
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
+
+    node = onnx.helper.make_node(
+        'ConstantOfShape',
+        inputs=['shape'],
+        outputs=['y'],
+        value=tensor_val,
+    )
+
+    return ([shape_const, node], [], [y])
+
+
+@onnx_test
+def const_of_shape_float_test():
+    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.FLOAT, [1],
+                                         [10])
+
+    shape_val = np.array([2, 3, 4]).astype(np.int64)
+    shape_ts = helper.make_tensor(name='shape_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=shape_val.shape,
+                                  vals=shape_val.flatten().astype(int))
+
+    shape_const = helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=shape_ts,
+    )
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
+
+    node = onnx.helper.make_node('ConstantOfShape',
+                                 inputs=['shape'],
+                                 outputs=['y'],
+                                 value=tensor_val)
+
+    return ([shape_const, node], [], [y])
+
+
+@onnx_test
+def const_of_shape_int64_test():
+    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
+                                         [10])
+    shape_val = np.array([2, 3, 4]).astype(np.int64)
+    shape_ts = helper.make_tensor(name='shape_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=shape_val.shape,
+                                  vals=shape_val.flatten().astype(int))
+    shape_const = helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=shape_ts,
+    )
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
+
+    node = onnx.helper.make_node('ConstantOfShape',
+                                 inputs=['shape'],
+                                 outputs=['y'],
+                                 value=tensor_val)
+
+    return ([shape_const, node], [], [y])
+
+
+@onnx_test
+def const_of_shape_no_value_attr_test():
+    shape_val = np.array([2, 3, 4]).astype(np.int64)
+    shape_ts = helper.make_tensor(name='shape_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=shape_val.shape,
+                                  vals=shape_val.flatten().astype(int))
+    shape_const = helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=shape_ts,
+    )
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
+
+    node = onnx.helper.make_node(
+        'ConstantOfShape',
+        inputs=['shape'],
+        outputs=['y'],
+    )
+
+    return ([shape_const, node], [], [y])
+
+
+@onnx_test
+def conv_autopad_fail_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 34, 34])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1'],
+                                 outputs=['2'],
+                                 dilations=[1, 1],
+                                 strides=[1, 1],
+                                 auto_pad='SAME',
+                                 pads=[0, 0, 1, 1, 0, 0, 1, 1])
+
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_bias_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
+    out = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 28, 28])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'],
+                                 dilations=[1, 1],
+                                 strides=[1, 1])
+
+    return ([node], [x, y, z], [out])
+
+
+@onnx_test
+def conv_bn_relu_maxpool_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
+    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1])
+    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
+    k = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1])
+    l = helper.make_tensor_value_info('6', TensorProto.FLOAT, [1])
+    out = helper.make_tensor_value_info('10', TensorProto.FLOAT,
+                                        [1, 1, 14, 14])
+
+    node0 = onnx.helper.make_node('Conv',
+                                  inputs=['0', '1', '2'],
+                                  outputs=['7'],
+                                  dilations=[1, 1],
+                                  strides=[1, 1],
+                                  pads=[0, 0, 0, 0])
+
+    node1 = onnx.helper.make_node('BatchNormalization',
+                                  inputs=['7', '3', '4', '5', '6'],
+                                  outputs=['8'],
+                                  epsilon=9.99999974737875e-06,
+                                  momentum=0.899999976158142)
+
+    node2 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9'])
+    node3 = onnx.helper.make_node('MaxPool',
+                                  inputs=['9'],
+                                  outputs=['10'],
+                                  pads=[0, 0, 0, 0],
+                                  strides=[2, 2],
+                                  kernel_shape=[2, 2])
+
+    return ([node0, node1, node2, node3], [x, y, z, m, n, k, l], [out])
+
+
+@onnx_test
+def conv_relu_maxpool_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
+    out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 1, 14, 14])
+
+    node1 = onnx.helper.make_node('Conv',
+                                  inputs=['0', '1', '2'],
+                                  outputs=['3'],
+                                  dilations=[1, 1],
+                                  strides=[1, 1],
+                                  pads=[0, 0, 0, 0])
+
+    node2 = onnx.helper.make_node('Relu', inputs=['3'], outputs=['4'])
+
+    node3 = onnx.helper.make_node('MaxPool',
+                                  inputs=['4'],
+                                  outputs=['5'],
+                                  pads=[0, 0, 0, 0],
+                                  strides=[2, 2],
+                                  kernel_shape=[2, 2])
+
+    return ([node1, node2, node3], [x, y, z], [out])
+
+
+@onnx_test
+def conv_relu_maxpool_x2_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5])
+    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 5, 5, 5])
+    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
+    out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 5, 5])
+
+    node1 = onnx.helper.make_node('Conv',
+                                  inputs=['0', '1', '2'],
+                                  outputs=['5'],
+                                  dilations=[1, 1],
+                                  strides=[1, 1],
+                                  pads=[0, 0, 0, 0])
+
+    node2 = onnx.helper.make_node('Relu', inputs=['5'], outputs=['6'])
+
+    node3 = onnx.helper.make_node('MaxPool',
+                                  inputs=['6'],
+                                  outputs=['7'],
+                                  pads=[0, 0, 0, 0],
+                                  strides=[2, 2],
+                                  kernel_shape=[2, 2])
+
+    node4 = onnx.helper.make_node('Conv',
+                                  inputs=['7', '3', '4'],
+                                  outputs=['8'],
+                                  dilations=[1, 1],
+                                  strides=[1, 1],
+                                  pads=[0, 0, 0, 0])
+
+    node5 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9'])
+
+    node6 = onnx.helper.make_node('MaxPool',
+                                  inputs=['9'],
+                                  outputs=['10'],
+                                  pads=[0, 0, 0, 0],
+                                  strides=[2, 2],
+                                  kernel_shape=[2, 2])
+
+    return ([node1, node2, node3, node4, node5, node6], [x, y, z, m, n], [out])
+
+
+@onnx_test
+def cos_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Cos',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def cosh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
+
+    node = onnx.helper.make_node(
+        'Cosh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def dropout_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
+
+    node = onnx.helper.make_node(
+        'Dropout',
+        inputs=['0'],
+        outputs=['1'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def elu_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node('Elu',
+                                 inputs=['0'],
+                                 outputs=['1'],
+                                 alpha=0.01)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def erf_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])
+
+    node = onnx.helper.make_node(
+        'Erf',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def exp_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Exp',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def expand_test():
+    shape_val = np.array([2, 3, 4, 5]).astype(np.int64)
+    shape_ts = helper.make_tensor(name='shape_tensor',
+                                  data_type=TensorProto.INT32,
+                                  dims=shape_val.shape,
+                                  vals=shape_val.flatten().astype(int))
+    shape_const = helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['shape'],
+        value=shape_ts,
+    )
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node('Expand',
+                                 inputs=['x', 'shape'],
+                                 outputs=['y'])
+
+    return ([shape_const, node], [x], [y])
+
+
+@onnx_test
+def flatten_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20])
+    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60])
+
+    node = onnx.helper.make_node('Flatten',
+                                 inputs=['0'],
+                                 axis=2,
+                                 outputs=['2'])
+
+    node2 = onnx.helper.make_node('Flatten', inputs=['0'], outputs=['3'])
+
+    return ([node, node2], [x], [y, y2])
+
+
+@onnx_test
+def gather_test():
+    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
+    i = helper.make_tensor_value_info('indices', TensorProto.INT32,
+                                      [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Gather',
+        inputs=['data', 'indices'],
+        outputs=['y'],
+        axis=1,
+    )
+
+    return ([node], [x, i], [y])
+
+
+@onnx_test
+def gemm_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [])
+    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11])
+
+    node = onnx.helper.make_node('Gemm',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'],
+                                 alpha=2.0,
+                                 beta=2.0,
+                                 transA=1,
+                                 transB=1)
+
+    return ([node], [x, y, z], [a])
+
+
+@onnx_test
+def gemm_ex_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
+    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
+
+    node = onnx.helper.make_node('Gemm',
+                                 inputs=['1', '2', '3'],
+                                 outputs=['y'],
+                                 alpha=0.5,
+                                 beta=0.8,
+                                 transA=1)
+
+    return ([node], [m1, m2, m3], [y])
+
+
+@onnx_test
+def gemm_ex_brcst_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
+    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
+
+    node = onnx.helper.make_node('Gemm',
+                                 inputs=['1', '2', '3'],
+                                 outputs=['y'],
+                                 alpha=0.5,
+                                 beta=0.8,
+                                 transA=1)
+
+    return ([node], [m1, m2, m3], [y])
+
+
+@onnx_test
+def globalavgpool_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
+
+    node = onnx.helper.make_node(
+        'GlobalAveragePool',
+        inputs=['0'],
+        outputs=['1'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def globalmaxpool_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
+
+    node = onnx.helper.make_node(
+        'GlobalMaxPool',
+        inputs=['0'],
+        outputs=['1'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def group_conv_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 4, 14, 14])
+
+    node = onnx.helper.make_node(
+        'Conv',
+        inputs=['0', '1'],
+        group=4,
+        outputs=['2'],
+    )
+
+    return ([node], [x, y], [z])
+
+
+@onnx_test
+def imagescaler_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 16, 16])
+
+    node = onnx.helper.make_node('ImageScaler',
+                                 inputs=['0'],
+                                 outputs=['1'],
+                                 bias=[0.01, 0.02, 0.03],
+                                 scale=0.5)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def implicit_add_bcast_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Add',
+        inputs=['0', '1'],
+        outputs=['2'],
+    )
+
+    return ([node], [x, y], [z])
+
+
+@onnx_test
+def implicit_pow_bcast_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Pow',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def implicit_sub_bcast_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Sub',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def leaky_relu_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node('LeakyRelu',
+                                 inputs=['0'],
+                                 outputs=['1'],
+                                 alpha=0.01)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def log_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Log',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def logsoftmax_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6])
+
+    node = onnx.helper.make_node('LogSoftmax',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axis=1)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def lrn_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24])
+
+    node = onnx.helper.make_node('LRN',
+                                 inputs=['0'],
+                                 size=5,
+                                 alpha=0.0001,
+                                 beta=0.75,
+                                 bias=1.0,
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def matmul_bmbm_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 2, 3, 6, 8])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def matmul_bmv_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 6])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def matmul_mv_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [6])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def matmul_vbm_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 8])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def matmul_vm_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def matmul_vv_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test
+def max_test():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node(
+        'Max',
+        inputs=['0', '1', '2'],
+        outputs=['3'],
+    )
+
+    return ([node], [a, b, c], [y])
+
+
+@onnx_test
+def min_test():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node(
+        'Min',
+        inputs=['0', '1', '2'],
+        outputs=['3'],
+    )
+
+    return ([node], [a, b, c], [y])
+
+
+@onnx_test
+def no_pad_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2])
+
+    node = onnx.helper.make_node('Pad',
+                                 inputs=['0'],
+                                 pads=[0, 0, 0, 0],
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def pad_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4])
+
+    node = onnx.helper.make_node('Pad',
+                                 inputs=['0'],
+                                 pads=[1, 1, 1, 1],
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def pow_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Pow',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def reducemean_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
+    axes = [2, 3]
+
+    node = onnx.helper.make_node('ReduceMean',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducemean_keepdims_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
+    axes = [2]
+
+    node = onnx.helper.make_node('ReduceMean',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=1)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducesum_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
+    axes = [2]
+
+    node = onnx.helper.make_node('ReduceSum',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducesum_multiaxis_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
+    axes = [2, 3]
+
+    node = onnx.helper.make_node('ReduceSum',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducesum_keepdims_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
+    axes = [2, 3]
+
+    node = onnx.helper.make_node('ReduceSum',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=1)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reshape_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3])
+    x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2])
+    x_shape_list = [3, 8]
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 8])
+    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3, 8])
+
+    node = onnx.helper.make_node('Reshape', inputs=['0', '1'], outputs=['2'])
+
+    node2 = onnx.helper.make_node('Reshape',
+                                  inputs=['0'],
+                                  shape=x_shape_list,
+                                  outputs=['3'])
+
+    return ([node, node2], [x, x_shape], [y, y2],
+            [helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])])
+
+
+@onnx_test
+def reshape_non_standard_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])
+    trans_x = helper.make_tensor_value_info('trans_x', TensorProto.FLOAT,
+                                            [2, 4, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 3, 2])
+
+    trans = helper.make_node(
+        'Transpose',
+        inputs=['x'],
+        outputs=['trans_x'],
+        perm=[0, 2, 1],
+    )
+
+    res = onnx.helper.make_node('Reshape',
+                                inputs=['trans_x'],
+                                outputs=['y'],
+                                shape=[4, 3, 2])
+
+    return ([trans, res], [x], [y])
+
+
+@onnx_test
+def shape_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Shape',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def shape_gather_test():
+    values = np.array([1])
+    value = helper.make_tensor_value_info('value', TensorProto.INT32, [1])
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [7, 3, 10])
+    y = helper.make_tensor_value_info('y', TensorProto.INT64, [3])
+    z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [1])
+
+    value_tensor = helper.make_tensor(name='const_tensor',
+                                      data_type=TensorProto.INT32,
+                                      dims=values.shape,
+                                      vals=values.flatten().astype(int))
+
+    node_const = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['value'],
+        value=value_tensor,
+    )
+
+    node_shape = onnx.helper.make_node(
+        'Shape',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    node_gather = helper.make_node(
+        'Gather',
+        inputs=['y', 'value'],
+        outputs=['z'],
+        axis=0,
+    )
+
+    return ([node_const, node_shape, node_gather], [x], [z])
+
+
+@onnx_test
+def sign_test():
+    x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5])
+    y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [10, 5])
+
+    node = onnx.helper.make_node(
+        'Sign',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def sin_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Sin',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def sinh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Sinh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def slice_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node('Slice',
+                                 inputs=['0'],
+                                 axes=[0, 1],
+                                 starts=[1, 0],
+                                 ends=[2, 2],
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def softmax_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3])
+
+    node = onnx.helper.make_node('Softmax', inputs=['0'], outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def sqrt_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])
+
+    node = onnx.helper.make_node(
+        'Sqrt',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def squeeze_unsqueeze_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                      [1, 3, 1, 1, 2, 1])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 2])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                      [1, 1, 3, 1, 2, 1])
+
+    node = onnx.helper.make_node('Squeeze',
+                                 inputs=['0'],
+                                 axes=[0, 2, 3, 5],
+                                 outputs=['1'])
+
+    node2 = onnx.helper.make_node('Unsqueeze',
+                                  inputs=['1'],
+                                  axes=[0, 1, 3, 5],
+                                  outputs=['2'])
+
+    return ([node, node2], [x], [z])
+
+
+@onnx_test
+def sub_bcast_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [2, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Sub',
+        inputs=['0', '1'],
+        outputs=['out'],
+        broadcast=1,
+        axis=1,
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def sub_scalar_test():
+    values = np.array([1])
+    arg_node = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                             [2, 3, 4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [2, 3, 4, 5])
+
+    values_tensor = helper.make_tensor(name='const',
+                                       data_type=TensorProto.FLOAT,
+                                       dims=values.shape,
+                                       vals=values.flatten().astype(float))
+
+    arg_const = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['arg_const'],
+        value=values_tensor,
+    )
+
+    node = onnx.helper.make_node(
+        'Sub',
+        inputs=['0', 'arg_const'],
+        outputs=['out'],
+    )
+
+    return ([arg_const, node], [arg_node], [arg_out])
+
+
+@onnx_test
+def sum_test():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node(
+        'Sum',
+        inputs=['0', '1', '2'],
+        outputs=['3'],
+    )
+
+    return ([node], [a, b, c], [y])
+
+
+@onnx_test
+def sum_test():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
+    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
+
+    node = onnx.helper.make_node(
+        'Sum',
+        inputs=['0', '1', '2'],
+        outputs=['3'],
+    )
+
+    return ([node], [a, b, c], [y])
+
+
+@onnx_test
+def tan_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Tan',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def tanh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
+
+    node = onnx.helper.make_node(
+        'Tanh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def transpose_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
+
+    node = onnx.helper.make_node(
+        'Transpose',
+        perm=[0, 3, 1, 2],
+        inputs=['0'],
+        outputs=['1'],
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def transpose_gather_test():
+    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6])
+    i = helper.make_tensor_value_info('indices', TensorProto.INT32,
+                                      [2, 4, 3, 5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT,
+                                      [3, 2, 3, 4, 5, 4, 5, 6])
+
+    td = onnx.helper.make_node(
+        'Transpose',
+        inputs=['data'],
+        outputs=['tdata'],
+        perm=[0, 2, 1, 3],
+    )
+
+    ti = onnx.helper.make_node('Transpose',
+                               inputs=['indices'],
+                               outputs=['tindices'],
+                               perm=[0, 2, 1, 3])
+
+    node = onnx.helper.make_node(
+        'Gather',
+        inputs=['tdata', 'tindices'],
+        outputs=['y'],
+        axis=1,
+    )
+
+    return ([td, ti, node], [x, i], [y])
+
+
+@onnx_test
+def unknown_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
+    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5])
+
+    node = onnx.helper.make_node('Unknown', inputs=['0', '1'], outputs=['2'])
+
+    node2 = onnx.helper.make_node('Unknown', inputs=['2'], outputs=['3'])
+
+    return ([node, node2], [x, y], [a])

test/onnx/implicit_add_bcast_test.onnx

+implicit_bcast-example:u
+
+
0
+
1
2"Addtest-multi_bcastZ
+
0
+

+
+
+
+Z
+
1
+

+
+
+
b
+
2
+

+
+
+
+B