def test_op_attributes(attribute: str, shorthand: str, input_id: int):
"""Test the various attributes that can be applied to ops.
Args:
attribute (str): Name of the attribute
shorthand (str): Shorthand of the attribute e.g. VirtualGraphId -> VGraphId
input_id (int): Long int for the id to use for the attribute.
"""
_, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
getter = getattr(op, "get" + attribute)
setter = getattr(op, "set" + attribute)
hasser = getattr(op, "has" + attribute)
get_optional = getattr(op, "getOptional" + shorthand)
assert not hasser()
id_ = getattr(_ir, "Optional" + shorthand)
# Unset optional
setter(id_())
assert get_optional() == id_()
with pytest.raises(popart.popart_exception) as e_info:
getter()
assert (e_info.value.args[0] == f"Cannot return {attribute} for Op")
assert not hasser()
# Set optional == 0
setter(id_(input_id))
assert getter() == input_id
assert hasser()
assert get_optional() == id_(input_id)
def test_shapes(shape1: List[int], shape2: List[int], expected: List[int],
dtype: str):
"""Test the shapes and np broadcasting. Don't really need to test the
broadcasting as that is tested at C++ level. But try a few cases to be sure
binding works correctly.
Args:
shape1 (List[int]): First tensor shape
shape2 (List[int]): Second Tensor Shape
expected (List[int]): Expected shape
dtype (Str): Popart data type to use
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
shape = op.prettyNpOut(shape1, shape2)
assert shape == list(expected)
t1 = _ir.TensorInfo(dtype, shape1)
t2 = _ir.TensorInfo(dtype, shape2)
shape = op.prettyNpOut(t1, t2)
assert shape == _ir.TensorInfo(dtype, expected)
def test_multi_graph():
"""Test adding ops to multiple graphs.
"""
ir, graphs = create_ir(["A", "B"])
g = graphs[0]
h = graphs[1]
settings_g = _ir.Settings(g, "settings_g")
settings_h = _ir.Settings(h, "settings_h")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op1 = _ir.Op(opid, settings_g)
op2 = _ir.Op(opid, settings_h)
assert op1.id == 100 # default Id
assert op2.id == 101 # default Id + 1
assert op1.opid == opid == op2.opid
assert op1.getGraph() == g
assert op2.getGraph() == h
def test_graph_in_outs():
"""Test default behaviour for no inputs or outputs.
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
assert op.hasInput(0) == False
assert op.hasOutput(0) == False
assert op.optionalInputs() == set()
assert op.getInBatchAxis(0) == 0
assert op.getOutBatchAxis(0) == 0
def test_op_creation():
"""Test simple op creation.
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
assert op.id == 100 # default Id
assert op.opid == opid
assert op.opid.domain == "ai.onnx"
assert op.opid.type == "Identity"
assert op.opid.version == 1
assert op.opid.numOutputs == 1
def test_op_clone():
"""Op::Clone is pure virtual, this should throw an error. Derived classes should
be able to call without issue.
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
with pytest.raises(RuntimeError) as e_info:
op2 = op.clone()
assert (
e_info.value.args[0] ==
"RuntimeError: Tried to call pure virtual function \"Op::clone\"")
def create_dummy_op(op_domain: str, op_type: str, op_version: int,
num_inputs: int, num_outputs: int) -> _ir.Op:
"""Create an op with the provided properties.
Args:
op_domain (str): Op domain
op_type (str): Op type name
op_version (int): Op version
num_inputs (int): Max = min number of outputs
num_outputs (int): Number of outputs
Returns:
_ir.Op: The op in question.
"""
ir, graphs = create_ir(["graph_123"])
graph = graphs[0]
settings = _ir.Settings(graph, "new_settings")
num_inputs_obj = _ir.NumInputs(num_inputs, num_inputs)
opid = _ir.OperatorIdentifier(op_domain, op_type, op_version,
num_inputs_obj, num_outputs)
return _ir.Op(opid, settings), ir, graph
def test_bools():
"""Test default behaviour of bool returns.
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier("ai.onnx", "Identity", 1, num_inputs, 1)
op = _ir.Op(opid, settings)
assert op.isInplaceViewChange() == False
assert op.isOutplaceViewChange() == False
assert op.isLossOp() == False
assert op.isIpuCopyOp() == False
assert op.isOptimizerOp() == False
assert op.requiresRandomSeed() == False
assert op.isOutlineable()
assert op.hasSideEffect() == False
assert op.isNorm() == False
assert op.canBeReplacedByIdentity() == False
assert op.copiesOptimizerTensors() == False
assert op.inputsUnmodifiable() == False
assert op.isElementWiseUnary() == False
def test_string_methods(op_name: str, domain: str, op_type: str, op_num: int,
op_version: int):
"""Test various string methods (name, id etc)
Args:
op_name (str): Name for the op
domain (str): Domain e.g. ai.onnx
op_type (str): Op type
op_num (int): Op number to test against (default 100)
op_version (int): Op version
"""
ir, graphs = create_ir(["A"])
g = graphs[0]
settings = _ir.Settings(g, "new_settings")
num_inputs = _ir.NumInputs(1, 1)
opid = _ir.OperatorIdentifier(domain, op_type, op_version, num_inputs, 1)
op = _ir.Op(opid, settings)
op.setName(op_name)
assert op.getName() == op_name
assert op.name() == op_name
assert op.str() == f"{op_num} ({domain}.{op_type}:{op_version})"
assert op.debugName(
) == f"Op({op_name} ({domain}.{op_type}:{op_version}), inputs=[], outputs=[])"