Merge branch 'develop' into tests

test/onnx/erf_test.onnx

+erf-example:A
+
+
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y"Erftest_erfZ
+
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+

+
+
+b
+
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+

+
+
+B

test/onnx/expand_test.onnx

+expand:

+7shape"Constant*$
+value**Bshape_tensor

+
+
x
+shape
y"ExpandexpandZ
+
x
+

+
+

+
b
+
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+

+
+
+
+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_bcast_test.onnx → test/onnx/implicit_add_bcast_test.onnx

-implicit_bcast-example:q
+implicit_bcast-example:u
 
 
0
 
1
2"Addtest-multi_bcastZ
@@ -7,14 +7,15 @@
 
 
 
-Z
-
1
-

+Z
+
1
+

 
-b
+
+
b
 
2
 

 
 
 
-B
+B
\ No newline at end of file

test/onnx/implicit_pow_bcast_test.onnx

+pow2:q
+
+
0
+
1out"Powpow_testZ
+
0
+

+
+
+
+Z
+
1
+

+
+
+
b
+out
+

+
+
+
+B

test/onnx/implicit_sub_bcast_test.onnx

-subtraction2:q
+add2:q
 
 
0
 
1out"Subsubtraction2Z
@@ -10,11 +10,11 @@
 Z
 
1
 

-
-b
+
+b
 out
 

 
 
 
-B
+B
\ No newline at end of file

test/onnx/leaky_relu.onnx → test/onnx/leaky_relu_test.onnx

-leaky_relu-example:R
+leaky_relu-example:R
 "
 
0
1"	LeakyRelu*
 alpha
@@ -11,4 +11,4 @@ test-modelZ
 
1
 
 

-B
+B
\ No newline at end of file

test/onnx/onnx_cosh.onnx

-cosh-example:;
-
-
x
y"Cosh	test_coshZ
-
x
-
-

-
b
-
y
-
-

-
B	
\ No newline at end of file

test/onnx/onnx_test.cpp

@@ -4,344 +4,519 @@
 #include <migraphx/operators.hpp>
 #include <migraphx/program.hpp>
 #include <migraphx/instruction.hpp>
+#include <migraphx/instruction_ref.hpp>
 #include <migraphx/onnx.hpp>
 #include "test.hpp"
 
-TEST_CASE(pytorch_conv_bias_test)
+TEST_CASE(acos_test)
 {
     migraphx::program p;
-    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
-    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
-    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {1}});
-    uint64_t axis = 1;
-    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
-    p.add_instruction(migraphx::op::add{}, l3, l4);
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::acos{}, input);
+
+    auto prog = migraphx::parse_onnx("acos_test.onnx");
 
-    auto prog = migraphx::parse_onnx("conv.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(pytorch_conv_relu_maxpool)
+TEST_CASE(add_bcast_test)
 {
     migraphx::program p;
-    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
-    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
-    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {1}});
-    uint64_t axis = 1;
-    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
-    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
-    auto l6       = p.add_instruction(migraphx::op::relu{}, l5);
-    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
+    auto l2 = p.add_instruction(migraphx::op::broadcast{1, l0->get_shape().lens()}, l1);
+    p.add_instruction(migraphx::op::add{}, l0, l2);
+
+    auto prog = migraphx::parse_onnx("add_bcast_test.onnx");
 
-    auto prog = migraphx::parse_onnx("conv_relu_maxpool.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(pytorch_conv_bn_relu_maxpool)
+TEST_CASE(add_fp16_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
-    auto l1 = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
-    auto l2 = p.add_parameter("2", {migraphx::shape::float_type, {1}});
-
-    auto p3       = p.add_parameter("3", {migraphx::shape::float_type, {1}});
-    auto p4       = p.add_parameter("4", {migraphx::shape::float_type, {1}});
-    auto p5       = p.add_parameter("5", {migraphx::shape::float_type, {1}});
-    auto p6       = p.add_parameter("6", {migraphx::shape::float_type, {1}});
-    uint64_t axis = 1;
-    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
-    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
-    auto l6 = p.add_instruction(migraphx::op::batch_norm_inference{1.0e-5f}, l5, p3, p4, p5, p6);
-    auto l7 = p.add_instruction(migraphx::op::relu{}, l6);
-    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l7);
+    auto l0 =
+        p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type, {1}}, {1.5}});
+    auto l1 =
+        p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type, {1}}, {2.5}});
+    p.add_instruction(migraphx::op::add{}, l0, l1);
+    auto prog = migraphx::parse_onnx("add_fp16_test.onnx");
 
-    auto prog = migraphx::parse_onnx("conv_bn_relu_maxpool.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(pytorch_conv_relu_maxpool_x2)
+TEST_CASE(add_scalar_test)
 {
     migraphx::program p;
-    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
-    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {5, 3, 5, 5}});
-    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {5}});
-    uint64_t axis = 1;
-    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
-    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
-    auto l6       = p.add_instruction(migraphx::op::relu{}, l5);
-    auto l7 = p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
-
-    auto l8  = p.add_parameter("3", {migraphx::shape::float_type, {1, 5, 5, 5}});
-    auto l9  = p.add_parameter("4", {migraphx::shape::float_type, {1}});
-    auto l10 = p.add_instruction(migraphx::op::convolution{}, l7, l8);
-    auto l11 = p.add_instruction(migraphx::op::broadcast{axis, l10->get_shape().lens()}, l9);
-    auto l12 = p.add_instruction(migraphx::op::add{}, l10, l11);
-    auto l13 = p.add_instruction(migraphx::op::relu{}, l12);
-    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l13);
-
-    auto prog = migraphx::parse_onnx("conv_relu_maxpoolX2.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
+    auto l1 = p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type}, {1}});
+    auto m0 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
+    auto m1 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
+    p.add_instruction(migraphx::op::add{}, m0, m1);
+    auto prog = migraphx::parse_onnx("add_scalar_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(leaky_relu_test)
+TEST_CASE(argmax_test)
 {
     migraphx::program p;
-    float alpha = 0.01f;
-    auto l0     = p.add_parameter("0", {migraphx::shape::float_type, {3}});
-    p.add_instruction(migraphx::op::leaky_relu{alpha}, l0);
-
-    auto prog = migraphx::parse_onnx("leaky_relu.onnx");
+    auto l0  = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto ins = p.add_instruction(migraphx::op::argmax{2}, l0);
+    p.add_instruction(migraphx::op::squeeze{{2}}, ins);
+    auto prog = migraphx::parse_onnx("argmax_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(imagescaler_test)
+TEST_CASE(argmin_test)
 {
     migraphx::program p;
-    migraphx::shape s{migraphx::shape::float_type, {1, 3, 16, 16}};
-    auto l0        = p.add_parameter("0", s);
-    auto scale_val = p.add_literal(0.5f);
-    auto bias_vals = p.add_literal(
-        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {3}}, {0.01, 0.02, 0.03}});
-    auto scaled_tensor = p.add_instruction(migraphx::op::scalar{s.lens()}, scale_val);
-    auto img_scaled    = p.add_instruction(migraphx::op::mul{}, l0, scaled_tensor);
-    auto bias_bcast    = p.add_instruction(migraphx::op::broadcast{1, s.lens()}, bias_vals);
-    p.add_instruction(migraphx::op::add{}, img_scaled, bias_bcast);
-
-    auto prog = migraphx::parse_onnx("imagescaler_test.onnx");
+    auto l0  = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto ins = p.add_instruction(migraphx::op::argmin{3}, l0);
+    p.add_instruction(migraphx::op::squeeze{{3}}, ins);
+    auto prog = migraphx::parse_onnx("argmin_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(globalavgpool_test)
+TEST_CASE(asin_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
-    auto op    = migraphx::op::pooling{"average"};
-    auto lens  = input->get_shape().lens();
-    op.lengths = {lens[2], lens[3]};
-    p.add_instruction(op, input);
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::asin{}, input);
 
-    auto prog = migraphx::parse_onnx("globalavgpool_test.onnx");
+    auto prog = migraphx::parse_onnx("asin_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(globalmaxpool_test)
+TEST_CASE(atan_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
-    auto op    = migraphx::op::pooling{"max"};
-    auto lens  = input->get_shape().lens();
-    op.lengths = {lens[2], lens[3]};
-    p.add_instruction(op, input);
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::atan{}, input);
 
-    auto prog = migraphx::parse_onnx("globalmaxpool_test.onnx");
+    auto prog = migraphx::parse_onnx("atan_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(transpose_test)
+TEST_CASE(cast_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 2, 3}});
-    std::vector<int64_t> perm{0, 3, 1, 2};
-    p.add_instruction(migraphx::op::transpose{perm}, input);
-
-    auto prog = migraphx::parse_onnx("transpose_test.onnx");
+    auto l = p.add_parameter("x", migraphx::shape{migraphx::shape::half_type, {10}});
+    p.add_instruction(migraphx::op::convert{migraphx::shape::float_type}, l);
 
+    auto prog = migraphx::parse_onnx("cast_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(dropout_test)
+TEST_CASE(clip_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 2, 2}});
-    p.add_instruction(migraphx::op::identity{}, input);
-
-    auto prog = migraphx::parse_onnx("dropout_test.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
+    p.add_instruction(migraphx::op::clip{6.0, 0.0}, l0);
+    auto prog = migraphx::parse_onnx("clip_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(sum_test)
+TEST_CASE(concat_test)
 {
     migraphx::program p;
-    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto l0     = p.add_instruction(migraphx::op::add{}, input0, input1);
-    p.add_instruction(migraphx::op::add{}, l0, input2);
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 4, 3}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7, 4, 3}});
+    p.add_instruction(migraphx::op::concat{0}, l0, l1);
+    auto prog = migraphx::parse_onnx("concat_test.onnx");
 
-    auto prog = migraphx::parse_onnx("sum_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(exp_test)
+TEST_CASE(constant_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::exp{}, input);
+    p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type, {3}}, {0, 1, 2}});
+    auto prog = migraphx::parse_onnx("constant_test.onnx");
 
-    auto prog = migraphx::parse_onnx("exp_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(log_test)
+TEST_CASE(constant_fill_test)
 {
+
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::log{}, input);
+    migraphx::shape s{migraphx::shape::float_type, {2, 3}};
+    std::vector<float> value(s.elements(), 1.0);
+    p.add_literal(migraphx::literal{s, value});
+    auto prog = migraphx::parse_onnx("constant_fill_test.onnx");
 
-    auto prog = migraphx::parse_onnx("log_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(sin_test)
+TEST_CASE(constant_fill_input_as_shape_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::sin{}, input);
+    auto l0 = p.add_literal(migraphx::literal{{migraphx::shape::int32_type, {2}}, {2, 3}});
+    std::vector<std::size_t> dims(l0->get_shape().elements());
+    migraphx::literal ls = l0->get_literal();
+    ls.visit([&](auto s) { dims.assign(s.begin(), s.end()); });
+    migraphx::shape s{migraphx::shape::float_type, dims};
+    std::vector<float> value(s.elements(), 1.0);
+    p.add_literal(migraphx::literal{s, value});
+    auto prog = migraphx::parse_onnx("constant_fill_input_as_shape_test.onnx");
 
-    auto prog = migraphx::parse_onnx("sin_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(cos_test)
+TEST_CASE(constant_scalar_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::cos{}, input);
+    p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::int32_type, {1}}, {1}});
+    auto prog = migraphx::parse_onnx("constant_scalar_test.onnx");
 
-    auto prog = migraphx::parse_onnx("cos_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(tan_test)
+TEST_CASE(const_of_shape_empty_input_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::tan{}, input);
+    p.add_literal(migraphx::literal());
+    migraphx::shape s(migraphx::shape::int64_type, {1}, {0});
+    std::vector<int64_t> vec(s.elements(), 10);
+    p.add_literal(migraphx::literal(s, vec));
 
-    auto prog = migraphx::parse_onnx("tan_test.onnx");
+    auto prog = migraphx::parse_onnx("const_of_shape_empty_input_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(sinh_test)
+TEST_CASE(const_of_shape_float_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::sinh{}, input);
-
-    auto prog = migraphx::parse_onnx("sinh_test.onnx");
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::float_type, {2, 3, 4});
+    std::vector<float> vec(s.elements(), 10.0f);
+    p.add_literal(migraphx::literal(s, vec));
 
+    auto prog = migraphx::parse_onnx("const_of_shape_float_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(cosh_test)
+TEST_CASE(const_of_shape_int64_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1}});
-    p.add_instruction(migraphx::op::cosh{}, input);
-
-    auto prog = migraphx::parse_onnx("cosh_test.onnx");
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::int64_type, {2, 3, 4});
+    std::vector<int64_t> vec(s.elements(), 10);
+    p.add_literal(migraphx::literal(s, vec));
 
+    auto prog = migraphx::parse_onnx("const_of_shape_int64_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(tanh_test)
+TEST_CASE(const_of_shape_no_value_attr_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1}});
-    p.add_instruction(migraphx::op::tanh{}, input);
-
-    auto prog = migraphx::parse_onnx("tanh_test.onnx");
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::float_type, {2, 3, 4});
+    std::vector<float> vec(s.elements(), 0.0f);
+    p.add_literal(migraphx::literal(s, vec));
 
+    auto prog = migraphx::parse_onnx("const_of_shape_no_value_attr_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(elu_test)
+TEST_CASE(conv_autopad_fail_test)
 {
-    migraphx::program p;
-    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
-    p.add_instruction(migraphx::op::elu{0.01}, input);
+    EXPECT(test::throws([&] { migraphx::parse_onnx("conv_autopad_fail_test.onnx"); }));
+}
 
-    auto prog = migraphx::parse_onnx("elu_test.onnx");
+TEST_CASE(conv_bias_test)
+{
+    migraphx::program p;
+    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
+    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {1}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
+    p.add_instruction(migraphx::op::add{}, l3, l4);
 
+    auto prog = migraphx::parse_onnx("conv_bias_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(asin_test)
+TEST_CASE(conv_bn_relu_maxpool_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::asin{}, input);
+    auto l0 = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
+    auto l1 = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto l2 = p.add_parameter("2", {migraphx::shape::float_type, {1}});
 
-    auto prog = migraphx::parse_onnx("asin_test.onnx");
+    auto p3       = p.add_parameter("3", {migraphx::shape::float_type, {1}});
+    auto p4       = p.add_parameter("4", {migraphx::shape::float_type, {1}});
+    auto p5       = p.add_parameter("5", {migraphx::shape::float_type, {1}});
+    auto p6       = p.add_parameter("6", {migraphx::shape::float_type, {1}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
+    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
+    auto l6 = p.add_instruction(migraphx::op::batch_norm_inference{1.0e-5f}, l5, p3, p4, p5, p6);
+    auto l7 = p.add_instruction(migraphx::op::relu{}, l6);
+    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l7);
 
+    auto prog = migraphx::parse_onnx("conv_bn_relu_maxpool_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(max_test)
+TEST_CASE(conv_relu_maxpool_test)
 {
     migraphx::program p;
-    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto l0     = p.add_instruction(migraphx::op::max{}, input0, input1);
-    p.add_instruction(migraphx::op::max{}, l0, input2);
+    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
+    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {1}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
+    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
+    auto l6       = p.add_instruction(migraphx::op::relu{}, l5);
+    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
 
-    migraphx::parse_onnx("max_test.onnx");
+    auto prog = migraphx::parse_onnx("conv_relu_maxpool_test.onnx");
+    EXPECT(p == prog);
 }
 
-TEST_CASE(acos_test)
+TEST_CASE(conv_relu_maxpool_x2_test)
 {
     migraphx::program p;
-    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::acos{}, input);
+    auto l0       = p.add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
+    auto l1       = p.add_parameter("1", {migraphx::shape::float_type, {5, 3, 5, 5}});
+    auto l2       = p.add_parameter("2", {migraphx::shape::float_type, {5}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraphx::op::convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
+    auto l5       = p.add_instruction(migraphx::op::add{}, l3, l4);
+    auto l6       = p.add_instruction(migraphx::op::relu{}, l5);
+    auto l7 = p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
 
-    auto prog = migraphx::parse_onnx("acos_test.onnx");
+    auto l8  = p.add_parameter("3", {migraphx::shape::float_type, {1, 5, 5, 5}});
+    auto l9  = p.add_parameter("4", {migraphx::shape::float_type, {1}});
+    auto l10 = p.add_instruction(migraphx::op::convolution{}, l7, l8);
+    auto l11 = p.add_instruction(migraphx::op::broadcast{axis, l10->get_shape().lens()}, l9);
+    auto l12 = p.add_instruction(migraphx::op::add{}, l10, l11);
+    auto l13 = p.add_instruction(migraphx::op::relu{}, l12);
+    p.add_instruction(migraphx::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l13);
+
+    auto prog = migraphx::parse_onnx("conv_relu_maxpool_x2_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(min_test)
+TEST_CASE(cos_test)
 {
     migraphx::program p;
-    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
-    auto l0     = p.add_instruction(migraphx::op::min{}, input0, input1);
-    p.add_instruction(migraphx::op::min{}, l0, input2);
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::cos{}, input);
 
-    migraphx::parse_onnx("min_test.onnx");
+    auto prog = migraphx::parse_onnx("cos_test.onnx");
+    EXPECT(p == prog);
 }
 
-TEST_CASE(atan_test)
+TEST_CASE(cosh_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1}});
+    p.add_instruction(migraphx::op::cosh{}, input);
+
+    auto prog = migraphx::parse_onnx("cosh_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(dropout_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 2, 2}});
+    p.add_instruction(migraphx::op::identity{}, input);
+
+    auto prog = migraphx::parse_onnx("dropout_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(elu_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
+    p.add_instruction(migraphx::op::elu{0.01}, input);
+
+    auto prog = migraphx::parse_onnx("elu_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(erf_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10, 15}});
+    p.add_instruction(migraphx::op::erf{}, input);
+
+    auto prog = migraphx::parse_onnx("erf_test.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(exp_test)
 {
     migraphx::program p;
     auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
-    p.add_instruction(migraphx::op::atan{}, input);
+    p.add_instruction(migraphx::op::exp{}, input);
 
-    auto prog = migraphx::parse_onnx("atan_test.onnx");
+    auto prog = migraphx::parse_onnx("exp_test.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(expand_test)
+{
+    migraphx::program p;
+    migraphx::shape s(migraphx::shape::float_type, {3, 1, 1});
+    auto param = p.add_parameter("x", s);
+    migraphx::shape ss(migraphx::shape::int32_type, {4});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4, 5}));
+    p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, param);
 
+    auto prog = migraphx::parse_onnx("expand_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(add_bcast_test)
+TEST_CASE(flatten_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
-    auto l2 = p.add_instruction(migraphx::op::broadcast{1, l0->get_shape().lens()}, l1);
-    p.add_instruction(migraphx::op::add{}, l0, l2);
+    p.add_instruction(migraphx::op::flatten{2}, l0);
+    p.add_instruction(migraphx::op::flatten{1}, l0);
+    auto prog = migraphx::parse_onnx("flatten_test.onnx");
 
-    auto prog = migraphx::parse_onnx("add_bcast_test.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(gather_test)
+{
+    migraphx::program p;
+    auto l0  = p.add_parameter("data", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto l1  = p.add_parameter("indices", migraphx::shape{migraphx::shape::int32_type, {2, 3}});
+    int axis = 1;
+    p.add_instruction(migraphx::op::gather{axis}, l0, l1);
+    auto prog = migraphx::parse_onnx("gather_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(gemm_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {5, 7}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {11, 5}});
+    p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {}});
+    auto t0    = p.add_instruction(migraphx::op::transpose{{1, 0}}, l0);
+    auto t1    = p.add_instruction(migraphx::op::transpose{{1, 0}}, l1);
+    auto alpha = 2.f;
+    auto beta  = 2.0f;
+    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, t1);
+    auto prog = migraphx::parse_onnx("gemm_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(gemm_ex_test)
+{
+    migraphx::program p;
+    auto l0    = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 6}});
+    auto l1    = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 7}});
+    auto l2    = p.add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 7}});
+    auto t0    = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l0);
+    auto alpha = 0.5f;
+    auto beta  = 0.8f;
+    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, l1, l2);
+    auto prog = migraphx::parse_onnx("gemm_ex_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(gemm_ex_brcst_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 6}});
+    auto l1 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 7}});
+    auto l2 = p.add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 1}});
+    auto t0 = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l0);
+    std::vector<std::size_t> out_lens{1, 1, 6, 7};
+    auto t2    = p.add_instruction(migraphx::op::multibroadcast{out_lens}, l2);
+    auto alpha = 0.5f;
+    auto beta  = 0.8f;
+    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, l1, t2);
+    auto prog = migraphx::parse_onnx("gemm_ex_brcst_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(globalavgpool_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    auto op    = migraphx::op::pooling{"average"};
+    auto lens  = input->get_shape().lens();
+    op.lengths = {lens[2], lens[3]};
+    p.add_instruction(op, input);
+
+    auto prog = migraphx::parse_onnx("globalavgpool_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(globalmaxpool_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    auto op    = migraphx::op::pooling{"max"};
+    auto lens  = input->get_shape().lens();
+    op.lengths = {lens[2], lens[3]};
+    p.add_instruction(op, input);
+
+    auto prog = migraphx::parse_onnx("globalmaxpool_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(group_conv_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 4, 16, 16}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 1, 3, 3}});
+    migraphx::op::convolution op;
+    op.group = 4;
+    p.add_instruction(op, l0, l1);
+    auto prog = migraphx::parse_onnx("group_conv_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(imagescaler_test)
+{
+    migraphx::program p;
+    migraphx::shape s{migraphx::shape::float_type, {1, 3, 16, 16}};
+    auto l0        = p.add_parameter("0", s);
+    auto scale_val = p.add_literal(0.5f);
+    auto bias_vals = p.add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {3}}, {0.01, 0.02, 0.03}});
+    auto scaled_tensor = p.add_instruction(migraphx::op::scalar{s.lens()}, scale_val);
+    auto img_scaled    = p.add_instruction(migraphx::op::mul{}, l0, scaled_tensor);
+    auto bias_bcast    = p.add_instruction(migraphx::op::broadcast{1, s.lens()}, bias_vals);
+    p.add_instruction(migraphx::op::add{}, img_scaled, bias_bcast);
+
+    auto prog = migraphx::parse_onnx("imagescaler_test.onnx");
 
     EXPECT(p == prog);
 }
@@ -350,25 +525,26 @@ TEST_CASE(implicit_add_bcast_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4, 1}});
     auto l2 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
     auto l3 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
     p.add_instruction(migraphx::op::add{}, l2, l3);
 
-    auto prog = migraphx::parse_onnx("implicit_bcast_test.onnx");
+    auto prog = migraphx::parse_onnx("implicit_add_bcast_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(sub_bcast_test)
+TEST_CASE(implicit_pow_bcast_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
-    auto l2 = p.add_instruction(migraphx::op::broadcast{1, l0->get_shape().lens()}, l1);
-    p.add_instruction(migraphx::op::sub{}, l0, l2);
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4, 1}});
+    auto l2 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
+    auto l3 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
+    p.add_instruction(migraphx::op::pow{}, l2, l3);
 
-    auto prog = migraphx::parse_onnx("sub_bcast_test.onnx");
+    auto prog = migraphx::parse_onnx("implicit_pow_bcast_test.onnx");
 
     EXPECT(p == prog);
 }
@@ -377,7 +553,7 @@ TEST_CASE(implicit_sub_bcast_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 5}});
     auto l2 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
     auto l3 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
     p.add_instruction(migraphx::op::sub{}, l2, l3);
@@ -387,323 +563,405 @@ TEST_CASE(implicit_sub_bcast_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(unknown_test)
+TEST_CASE(leaky_relu_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
-    auto l2 = p.add_instruction(migraphx::op::unknown{"Unknown"}, l0, l1);
-    p.add_instruction(migraphx::op::unknown{"Unknown"}, l2);
-    auto prog = migraphx::parse_onnx("unknown_test.onnx");
+    float alpha = 0.01f;
+    auto l0     = p.add_parameter("0", {migraphx::shape::float_type, {3}});
+    p.add_instruction(migraphx::op::leaky_relu{alpha}, l0);
+
+    auto prog = migraphx::parse_onnx("leaky_relu_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(softmax_test)
+TEST_CASE(log_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3}});
-    auto r  = p.add_instruction(migraphx::op::reshape{{1, 3, 1, 1}}, l0);
-    auto s  = p.add_instruction(migraphx::op::softmax{}, r);
-    p.add_instruction(migraphx::op::reshape{{1, 3}}, s);
-    auto prog = migraphx::parse_onnx("softmax_test.onnx");
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::log{}, input);
 
+    auto prog = migraphx::parse_onnx("log_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(reshape_test)
+TEST_CASE(logsoftmax_test)
 {
     migraphx::program p;
-    migraphx::op::reshape op;
-    std::vector<int64_t> reshape_dims{3, 8};
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {4, 2, 3}});
-    p.add_literal(
-        migraphx::literal{migraphx::shape{migraphx::shape::int64_type, {2}}, reshape_dims});
-    op.dims = reshape_dims;
-    p.add_instruction(op, l0);
+    auto l0  = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    int axis = 1;
+    p.add_instruction(migraphx::op::logsoftmax{axis}, l0);
+    auto prog = migraphx::parse_onnx("logsoftmax_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(lrn_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 28, 24, 24}});
+    migraphx::op::lrn op;
+    op.size  = 5;
+    op.alpha = 0.0001;
+    op.beta  = 0.75;
+    op.bias  = 1.0;
     p.add_instruction(op, l0);
-    auto prog = migraphx::parse_onnx("reshape_test.onnx");
+    auto prog = migraphx::parse_onnx("lrn_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_bmbm_test)
+{
+    migraphx::program p;
+    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 6, 7}});
+    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {5, 2, 1, 7, 8}});
+    auto bl0 = p.add_instruction(migraphx::op::multibroadcast{{5, 2, 3, 6, 7}}, l0);
+    auto bl1 = p.add_instruction(migraphx::op::multibroadcast{{5, 2, 3, 7, 8}}, l1);
+    p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, bl0, bl1);
+
+    auto prog = migraphx::parse_onnx("matmul_bmbm_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_bmv_test)
+{
+    migraphx::program p;
+    auto l0   = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 6, 7}});
+    auto l1   = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto sl1  = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
+    auto bsl1 = p.add_instruction(migraphx::op::multibroadcast{{3, 7, 1}}, sl1);
+    auto res  = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, l0, bsl1);
+    p.add_instruction(migraphx::op::squeeze{{2}}, res);
+
+    auto prog = migraphx::parse_onnx("matmul_bmv_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_mv_test)
+{
+    migraphx::program p;
+    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {6, 7}});
+    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto sl1 = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
+    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, l0, sl1);
+    p.add_instruction(migraphx::op::squeeze{{1}}, res);
+
+    auto prog = migraphx::parse_onnx("matmul_mv_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_vbm_test)
+{
+    migraphx::program p;
+    auto l0   = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto l1   = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {5, 7, 8}});
+    auto sl0  = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
+    auto bsl0 = p.add_instruction(migraphx::op::multibroadcast{{5, 1, 7}}, sl0);
+    std::cout << "ONNX_TEST" << std::endl;
+    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, bsl0, l1);
+    std::cout << "After Dot" << std::endl;
+    p.add_instruction(migraphx::op::squeeze{{1}}, res);
+
+    auto prog = migraphx::parse_onnx("matmul_vbm_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_vm_test)
+{
+    migraphx::program p;
+    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7, 8}});
+    auto sl0 = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
+    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, sl0, l1);
+    p.add_instruction(migraphx::op::squeeze{{0}}, res);
+
+    auto prog = migraphx::parse_onnx("matmul_vm_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_vv_test)
+{
+    migraphx::program p;
+    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto sl0 = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
+    auto sl1 = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
+    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, sl0, sl1);
+    auto sr0 = p.add_instruction(migraphx::op::squeeze{{0}}, res);
+    p.add_instruction(migraphx::op::squeeze{{0}}, sr0);
+
+    auto prog = migraphx::parse_onnx("matmul_vv_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(max_test)
+{
+    migraphx::program p;
+    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto l0     = p.add_instruction(migraphx::op::max{}, input0, input1);
+    p.add_instruction(migraphx::op::max{}, l0, input2);
+
+    migraphx::parse_onnx("max_test.onnx");
+}
+
+TEST_CASE(min_test)
+{
+    migraphx::program p;
+    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto l0     = p.add_instruction(migraphx::op::min{}, input0, input1);
+    p.add_instruction(migraphx::op::min{}, l0, input2);
+
+    migraphx::parse_onnx("min_test.onnx");
+}
+
+TEST_CASE(no_pad_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 2}});
+    p.add_instruction(migraphx::op::identity{}, l0);
+    auto prog = migraphx::parse_onnx("no_pad_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(shape_test)
+TEST_CASE(pad_test)
 {
     migraphx::program p;
-    migraphx::shape s{migraphx::shape::float_type, {3, 4, 5, 6}};
-    auto l0 = p.add_parameter("x", s);
-    migraphx::shape s_shape{migraphx::shape::int64_type, {4}};
-    p.add_literal(s_shape, l0->get_shape().lens());
-    auto prog = migraphx::parse_onnx("shape_test.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 2}});
+    p.add_instruction(migraphx::op::pad{{1, 1, 1, 1}}, l0);
+    auto prog = migraphx::parse_onnx("pad_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(gather_test)
+TEST_CASE(pow_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("data", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
-    auto l1  = p.add_parameter("indices", migraphx::shape{migraphx::shape::int32_type, {2, 3}});
-    int axis = 1;
-    p.add_instruction(migraphx::op::gather{axis}, l0, l1);
-    auto prog = migraphx::parse_onnx("gather_test.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
+    p.add_instruction(migraphx::op::pow{}, l0, l1);
+
+    auto prog = migraphx::parse_onnx("pow_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(shape_gather_test)
+TEST_CASE(reducemean_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {7, 3, 10}});
-    auto l1 =
-        p.add_literal(migraphx::shape{migraphx::shape::int64_type, {3}}, l0->get_shape().lens());
-    migraphx::shape const_shape{migraphx::shape::int32_type, {1}};
-    auto l2  = p.add_literal(migraphx::literal{const_shape, {1}});
-    int axis = 0;
-    p.add_instruction(migraphx::op::gather{axis}, l1, l2);
-    auto prog = migraphx::parse_onnx("shape_gather.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto l1 = p.add_instruction(migraphx::op::reduce_mean{{2, 3}}, l0);
+    p.add_instruction(migraphx::op::squeeze{{2, 3}}, l1);
+    auto prog = migraphx::parse_onnx("reducemean_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(flatten_test)
+TEST_CASE(reducemean_keepdims_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    p.add_instruction(migraphx::op::flatten{2}, l0);
-    p.add_instruction(migraphx::op::flatten{1}, l0);
-    auto prog = migraphx::parse_onnx("flatten_test.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    p.add_instruction(migraphx::op::reduce_mean{{2}}, l0);
+    auto prog = migraphx::parse_onnx("reducemean_keepdims_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(squeeze_unsqueeze_test)
+TEST_CASE(reducesum_test)
 {
     migraphx::program p;
-    std::vector<int64_t> squeeze_axes{0, 2, 3, 5};
-    std::vector<int64_t> unsqueeze_axes{0, 1, 3, 5};
-    auto l0 =
-        p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 1, 1, 2, 1}});
-    auto l1 = p.add_instruction(migraphx::op::squeeze{squeeze_axes}, l0);
-    p.add_instruction(migraphx::op::unsqueeze{unsqueeze_axes}, l1);
-    auto prog = migraphx::parse_onnx("squeeze_unsqueeze_test.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto l1 = p.add_instruction(migraphx::op::reduce_sum{{2}}, l0);
+    p.add_instruction(migraphx::op::squeeze{{2}}, l1);
+    auto prog = migraphx::parse_onnx("reducesum_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(concat_test)
+TEST_CASE(reducesum_multiaxis_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 4, 3}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7, 4, 3}});
-    p.add_instruction(migraphx::op::concat{0}, l0, l1);
-    auto prog = migraphx::parse_onnx("concat_test.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    auto l1 = p.add_instruction(migraphx::op::reduce_sum{{2, 3}}, l0);
+    p.add_instruction(migraphx::op::squeeze{{2, 3}}, l1);
+    auto prog = migraphx::parse_onnx("reducesum_multiaxis_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(slice_test)
+TEST_CASE(reducesum_keepdims_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3, 2}});
-    p.add_instruction(migraphx::op::slice{{0, 1}, {1, 0}, {2, 2}}, l0);
-    auto prog = migraphx::parse_onnx("slice_test.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
+    p.add_instruction(migraphx::op::reduce_sum{{2, 3}}, l0);
+    auto prog = migraphx::parse_onnx("reducesum_keepdims_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(constant_test)
+TEST_CASE(reshape_test)
 {
     migraphx::program p;
-    p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type, {3}}, {0, 1, 2}});
-    auto prog = migraphx::parse_onnx("constant_test.onnx");
+    migraphx::op::reshape op;
+    std::vector<int64_t> reshape_dims{3, 8};
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {4, 2, 3}});
+    p.add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::int64_type, {2}}, reshape_dims});
+    op.dims = reshape_dims;
+    p.add_instruction(op, l0);
+    p.add_instruction(op, l0);
+    auto prog = migraphx::parse_onnx("reshape_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(constant_test_scalar)
+TEST_CASE(reshape_non_standard_test)
 {
     migraphx::program p;
-    p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::int32_type, {1}}, {1}});
-    auto prog = migraphx::parse_onnx("constant_scalar.onnx");
+    migraphx::op::reshape op;
+    std::vector<int64_t> reshape_dims{4, 3, 2};
+    migraphx::shape s{migraphx::shape::float_type, {2, 3, 4}};
+    auto x      = p.add_parameter("x", s);
+    auto tran_x = p.add_instruction(migraphx::op::transpose{{0, 2, 1}}, x);
+    auto cont_x = p.add_instruction(migraphx::op::contiguous{}, tran_x);
+    p.add_instruction(migraphx::op::reshape{{4, 3, 2}}, cont_x);
+    auto prog = migraphx::parse_onnx("reshape_non_standard_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(constant_fill_test)
+TEST_CASE(round_test)
 {
-    {
-        migraphx::program p;
-        auto l0 = p.add_literal(migraphx::literal{{migraphx::shape::int32_type, {2}}, {2, 3}});
-        std::vector<std::size_t> dims(l0->get_shape().elements());
-        migraphx::literal ls = l0->get_literal();
-        ls.visit([&](auto s) { dims.assign(s.begin(), s.end()); });
-        migraphx::shape s{migraphx::shape::float_type, dims};
-        std::vector<float> value(s.elements(), 1.0);
-        p.add_literal(migraphx::literal{s, value});
-        auto prog = migraphx::parse_onnx("const_fill1.onnx");
-
-        EXPECT(p == prog);
-    }
-
-    {
-        migraphx::program p;
-        migraphx::shape s{migraphx::shape::float_type, {2, 3}};
-        std::vector<float> value(s.elements(), 1.0);
-        p.add_literal(migraphx::literal{s, value});
-        auto prog = migraphx::parse_onnx("const_fill2.onnx");
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::double_type, {10, 5}});
+    p.add_instruction(migraphx::op::round{}, input);
 
-        EXPECT(p == prog);
-    }
+    auto prog = migraphx::parse_onnx("round_test.onnx");
+    EXPECT(p == prog);
 }
 
-TEST_CASE(gemm_test)
+TEST_CASE(shape_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {5, 7}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {11, 5}});
-    p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {}});
-    auto t0    = p.add_instruction(migraphx::op::transpose{{1, 0}}, l0);
-    auto t1    = p.add_instruction(migraphx::op::transpose{{1, 0}}, l1);
-    auto alpha = 2.f;
-    auto beta  = 2.0f;
-    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, t1);
-    auto prog = migraphx::parse_onnx("gemm_test.onnx");
+    migraphx::shape s{migraphx::shape::float_type, {3, 4, 5, 6}};
+    auto l0 = p.add_parameter("x", s);
+    migraphx::shape s_shape{migraphx::shape::int64_type, {4}};
+    p.add_literal(s_shape, l0->get_shape().lens());
+    auto prog = migraphx::parse_onnx("shape_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(gemm_ex)
+TEST_CASE(shape_gather_test)
 {
     migraphx::program p;
-    auto l0    = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 6}});
-    auto l1    = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 7}});
-    auto l2    = p.add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 7}});
-    auto t0    = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l0);
-    auto alpha = 0.5f;
-    auto beta  = 0.8f;
-    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, l1, l2);
-    auto prog = migraphx::parse_onnx("gemm_test_ex.onnx");
+    auto l0 = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {7, 3, 10}});
+    auto l1 =
+        p.add_literal(migraphx::shape{migraphx::shape::int64_type, {3}}, l0->get_shape().lens());
+    migraphx::shape const_shape{migraphx::shape::int32_type, {1}};
+    auto l2  = p.add_literal(migraphx::literal{const_shape, {1}});
+    int axis = 0;
+    p.add_instruction(migraphx::op::gather{axis}, l1, l2);
+    auto prog = migraphx::parse_onnx("shape_gather_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(gemm_ex_brcst)
+TEST_CASE(sign_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 6}});
-    auto l1 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 7}});
-    auto l2 = p.add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 1}});
-    auto t0 = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l0);
-    std::vector<std::size_t> out_lens{1, 1, 6, 7};
-    auto t2    = p.add_instruction(migraphx::op::multibroadcast{out_lens}, l2);
-    auto alpha = 0.5f;
-    auto beta  = 0.8f;
-    p.add_instruction(migraphx::op::dot{alpha, beta}, t0, l1, t2);
-    auto prog = migraphx::parse_onnx("gemm_test_ex1.onnx");
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::double_type, {10, 5}});
+    p.add_instruction(migraphx::op::sign{}, input);
 
+    auto prog = migraphx::parse_onnx("sign_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_vv)
+TEST_CASE(sin_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto sl0 = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
-    auto sl1 = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
-    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, sl0, sl1);
-    auto sr0 = p.add_instruction(migraphx::op::squeeze{{0}}, res);
-    p.add_instruction(migraphx::op::squeeze{{0}}, sr0);
-
-    auto prog = migraphx::parse_onnx("matmul_vv.onnx");
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::sin{}, input);
 
+    auto prog = migraphx::parse_onnx("sin_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_vm)
+TEST_CASE(sinh_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7, 8}});
-    auto sl0 = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
-    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, sl0, l1);
-    p.add_instruction(migraphx::op::squeeze{{0}}, res);
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::sinh{}, input);
 
-    auto prog = migraphx::parse_onnx("matmul_vm.onnx");
+    auto prog = migraphx::parse_onnx("sinh_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_vbm)
+TEST_CASE(slice_test)
 {
     migraphx::program p;
-    auto l0   = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto l1   = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {5, 7, 8}});
-    auto sl0  = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
-    auto bsl0 = p.add_instruction(migraphx::op::multibroadcast{{5, 1, 7}}, sl0);
-    std::cout << "ONNX_TEST" << std::endl;
-    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, bsl0, l1);
-    std::cout << "After Dot" << std::endl;
-    p.add_instruction(migraphx::op::squeeze{{1}}, res);
-
-    auto prog = migraphx::parse_onnx("matmul_vbm.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3, 2}});
+    p.add_instruction(migraphx::op::slice{{0, 1}, {1, 0}, {2, 2}}, l0);
+    auto prog = migraphx::parse_onnx("slice_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_mv)
+TEST_CASE(softmax_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {6, 7}});
-    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto sl1 = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
-    auto res = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, l0, sl1);
-    p.add_instruction(migraphx::op::squeeze{{1}}, res);
-
-    auto prog = migraphx::parse_onnx("matmul_mv.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3}});
+    p.add_instruction(migraphx::op::softmax{1}, l0);
+    auto prog = migraphx::parse_onnx("softmax_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_bmv)
+TEST_CASE(sqrt_test)
 {
     migraphx::program p;
-    auto l0   = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 6, 7}});
-    auto l1   = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
-    auto sl1  = p.add_instruction(migraphx::op::unsqueeze{{1}}, l1);
-    auto bsl1 = p.add_instruction(migraphx::op::multibroadcast{{3, 7, 1}}, sl1);
-    auto res  = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, l0, bsl1);
-    p.add_instruction(migraphx::op::squeeze{{2}}, res);
-
-    auto prog = migraphx::parse_onnx("matmul_bmv.onnx");
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10, 15}});
+    p.add_instruction(migraphx::op::sqrt{}, input);
 
+    auto prog = migraphx::parse_onnx("sqrt_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(matmul_bmbm)
+TEST_CASE(squeeze_unsqueeze_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 6, 7}});
-    auto l1  = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {5, 2, 1, 7, 8}});
-    auto bl0 = p.add_instruction(migraphx::op::multibroadcast{{5, 2, 3, 6, 7}}, l0);
-    auto bl1 = p.add_instruction(migraphx::op::multibroadcast{{5, 2, 3, 7, 8}}, l1);
-    p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, bl0, bl1);
-
-    auto prog = migraphx::parse_onnx("matmul_bmbm.onnx");
+    std::vector<int64_t> squeeze_axes{0, 2, 3, 5};
+    std::vector<int64_t> unsqueeze_axes{0, 1, 3, 5};
+    auto l0 =
+        p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 1, 1, 2, 1}});
+    auto l1 = p.add_instruction(migraphx::op::squeeze{squeeze_axes}, l0);
+    p.add_instruction(migraphx::op::unsqueeze{unsqueeze_axes}, l1);
+    auto prog = migraphx::parse_onnx("squeeze_unsqueeze_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(add_scalar_test)
+TEST_CASE(sub_bcast_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 = p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type}, {1}});
-    auto m0 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
-    auto m1 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
-    p.add_instruction(migraphx::op::add{}, m0, m1);
-    auto prog = migraphx::parse_onnx("add_scalar_test.onnx");
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
+    auto l2 = p.add_instruction(migraphx::op::broadcast{1, l0->get_shape().lens()}, l1);
+    p.add_instruction(migraphx::op::sub{}, l0, l2);
+
+    auto prog = migraphx::parse_onnx("sub_bcast_test.onnx");
 
     EXPECT(p == prog);
 }
@@ -722,84 +980,86 @@ TEST_CASE(sub_scalar_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(group_conv_test)
+TEST_CASE(sum_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 4, 16, 16}});
-    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 1, 3, 3}});
-    migraphx::op::convolution op;
-    op.group = 4;
-    p.add_instruction(op, l0, l1);
-    auto prog = migraphx::parse_onnx("group_conv_test.onnx");
+    auto input0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto input2 = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {3}});
+    auto l0     = p.add_instruction(migraphx::op::add{}, input0, input1);
+    p.add_instruction(migraphx::op::add{}, l0, input2);
 
+    auto prog = migraphx::parse_onnx("sum_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(pad_test)
+TEST_CASE(tan_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 2}});
-    p.add_instruction(migraphx::op::pad{{1, 1, 1, 1}}, l0);
-    auto prog = migraphx::parse_onnx("pad_test.onnx");
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::tan{}, input);
 
+    auto prog = migraphx::parse_onnx("tan_test.onnx");
     EXPECT(p == prog);
 }
 
-TEST_CASE(lrn_test)
+TEST_CASE(tanh_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 28, 24, 24}});
-    migraphx::op::lrn op;
-    op.size  = 5;
-    op.alpha = 0.0001;
-    op.beta  = 0.75;
-    op.bias  = 1.0;
-    p.add_instruction(op, l0);
-    auto prog = migraphx::parse_onnx("lrn_test.onnx");
-
-    EXPECT(p == prog);
-}
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1}});
+    p.add_instruction(migraphx::op::tanh{}, input);
 
-TEST_CASE(add_fp16_test)
-{
-    migraphx::program p;
-    auto l0 =
-        p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type, {1}}, {1.5}});
-    auto l1 =
-        p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type, {1}}, {2.5}});
-    p.add_instruction(migraphx::op::add{}, l0, l1);
-    auto prog = migraphx::parse_onnx("add_fp16_test.onnx");
+    auto prog = migraphx::parse_onnx("tanh_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(logsoftmax)
+TEST_CASE(transpose_test)
 {
     migraphx::program p;
-    auto l0  = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
-    int axis = 1;
-    p.add_instruction(migraphx::op::logsoftmax{axis}, l0);
-    auto prog = migraphx::parse_onnx("logsoftmax_test.onnx");
+    auto input = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 2, 3}});
+    std::vector<int64_t> perm{0, 3, 1, 2};
+    p.add_instruction(migraphx::op::transpose{perm}, input);
+
+    auto prog = migraphx::parse_onnx("transpose_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(no_pad_test)
+TEST_CASE(transpose_gather_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 2}});
-    p.add_instruction(migraphx::op::identity{}, l0);
-    auto prog = migraphx::parse_onnx("no_pad_test.onnx");
+    auto make_contiguous = [&p](migraphx::instruction_ref ins) {
+        if(ins->get_shape().standard())
+        {
+            return ins;
+        }
+
+        return p.add_instruction(migraphx::op::contiguous{}, ins);
+    };
+
+    auto data = p.add_parameter("data", migraphx::shape{migraphx::shape::float_type, {3, 5, 4, 6}});
+    auto ind =
+        p.add_parameter("indices", migraphx::shape{migraphx::shape::int32_type, {2, 4, 3, 5}});
+    auto tr_data = p.add_instruction(migraphx::op::transpose{{0, 2, 1, 3}}, data);
+    auto tr_ind  = p.add_instruction(migraphx::op::transpose{{0, 2, 1, 3}}, ind);
+    int axis     = 1;
+    p.add_instruction(
+        migraphx::op::gather{axis}, make_contiguous(tr_data), make_contiguous(tr_ind));
+
+    auto prog = migraphx::parse_onnx("transpose_gather_test.onnx");
 
     EXPECT(p == prog);
 }
 
-TEST_CASE(clip_test)
+TEST_CASE(unknown_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3}});
-    p.add_instruction(migraphx::op::clip{6.0, 0.0}, l0);
-    auto prog = migraphx::parse_onnx("clip_test.onnx");
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 4}});
+    auto l2 = p.add_instruction(migraphx::op::unknown{"Unknown"}, l0, l1);
+    p.add_instruction(migraphx::op::unknown{"Unknown"}, l2);
+    auto prog = migraphx::parse_onnx("unknown_test.onnx");
 
     EXPECT(p == prog);
 }

test/onnx/pow_bcast_test.onnx

+pow2:q
+
+
0
+
1out"Powpow_testZ
+
0
+

+
+
+
+Z
+
1
+

+
+
+
b
+out
+

+
+
+
+B

test/onnx/pow_bcast_test1.onnx

+pow2:u
+
+
0
+
1out"Powpow_testZ
+
0
+

+
+
+
+Z
+
1
+

+
+
+
+b
+out
+

+
+
+
+B