def test_custom_op_rel_infer_exception():
"""Tests infer type for custom_op"""
def custom_log1_rel(arg_types, attrs):
assert len(arg_types) == 2, "type relation arg number mismatch!"
return None
op_name = "custom_log2"
_op.register(op_name, r"code(cal log of a tensor.)code")
_op.get(op_name).set_num_inputs(1)
_op.get(op_name).add_argument("data_0", "Tensor", "The input data tensor.")
_op.get(op_name).set_attrs_type_key("DictAttrs")
# call customized relation functions
_op.get(op_name).add_type_rel("custom_log2", custom_log1_rel)
_op.get(op_name).set_support_level(1)
_op.register_pattern(op_name, _op.OpPattern.ELEMWISE)
_op.register_stateful(op_name, False)
def clog(x):
return relay.Call(_op.get(op_name), [x])
tp = relay.TensorType((10, 10), "float32")
x = relay.var("x", tp)
sb = relay.ScopeBuilder()
t1 = sb.let("t1", clog(x))
t2 = sb.let("t2", relay.add(t1, x))
sb.ret(t2)
f = relay.Function([x], sb.get())
with pytest.raises(tvm.error.TVMError) as cm:
fchecked = infer_expr(f)
assert "type relation arg number mismatch" in str(cm.execption)
def test_custom_op_rel_infer():
"""Tests infer type for custom_op"""
def custom_log1_rel(arg_types, attrs):
assert len(arg_types) == 1, "type relation arg number mismatch!"
if attrs:
assert isinstance(attrs, DictAttrs)
inputa_type = arg_types[0]
return relay.TensorType(inputa_type.shape, inputa_type.dtype)
op_name = "custom_log1"
_op.register(op_name, r"code(cal log of a tensor.)code")
_op.get(op_name).set_num_inputs(1)
_op.get(op_name).add_argument("data_0", "Tensor", "The input data tensor.")
_op.get(op_name).set_attrs_type_key("DictAttrs")
# call customized relation functions
_op.get(op_name).add_type_rel("custom_log1", custom_log1_rel)
_op.get(op_name).set_support_level(1)
_op.register_pattern(op_name, _op.OpPattern.ELEMWISE)
_op.register_stateful(op_name, False)
def clog(x):
return relay.Call(_op.get(op_name), [x])
tp = relay.TensorType((10, 10), "float32")
x = relay.var("x", tp)
sb = relay.ScopeBuilder()
t1 = sb.let("t1", clog(x))
t2 = sb.let("t2", relay.add(t1, x))
sb.ret(t2)
f = relay.Function([x], sb.get())
fchecked = infer_expr(f)
assert fchecked.checked_type == relay.FuncType([tp], tp)
def test_op_register():
"""Tests register_op functionality."""
op_name = "custom_op"
_op.register(op_name, r"code(Add two tensor with inner broadcasting.)code")
_op.get(op_name).set_num_inputs(2)
_op.get(op_name).add_argument("data_0", "Tensor", "The input data tensor.")
_op.get(op_name).add_argument("data_1", "Tensor", "The input data tensor.")
# call default relation functions
_op.get(op_name).add_type_rel("Identity")
_op.get(op_name).set_support_level(1)
_op.register_pattern(op_name, _op.OpPattern.ELEMWISE)
_op.register_stateful(op_name, False)
assert _op.get(op_name).name == op_name
assert _op.get(op_name).num_inputs == 2
assert _op.get(op_name).get_attr("TOpPattern") == _op.OpPattern.ELEMWISE
assert _op.get(op_name).get_attr("TOpIsStateful") == False
def test_custom_add_broadcast_op():
"""Tests infer type for broadcast custom_op"""
op_name = "custom_broadcast_add"
_op.register(op_name, r"code(Add two tensor with inner broadcasting.)code")
_op.get(op_name).set_num_inputs(2)
_op.get(op_name).add_argument("data_0", "Tensor", "The input data tensor.")
_op.get(op_name).add_argument("data_1", "Tensor", "The input data tensor.")
# call default relation functions
_op.get(op_name).add_type_rel("Broadcast")
_op.get(op_name).set_support_level(1)
_op.register_stateful(op_name, False)
def broadcast_add(x, y):
return relay.Call(_op.get(op_name), [x, y])
x = relay.var("x", shape=(10, 4))
y = relay.var("y", shape=(5, 10, 1))
z = broadcast_add(x, y)
func = relay.Function([x, y], z)
t1 = relay.TensorType((10, 4), "float32")
t2 = relay.TensorType((5, 10, 1), "float32")
t3 = relay.TensorType((5, 10, 4), "float32")
expected_ty = relay.FuncType([t1, t2], t3)
assert_has_type(func, expected_ty)
def test_custom_op_infer():
"""Tests infer type for custom_op"""
op_name = "custom_log"
_op.register(op_name, r"code(cal log of a tensor.)code")
_op.get(op_name).set_num_inputs(1)
_op.get(op_name).add_argument("data_0", "Tensor", "The input data tensor.")
# call default relation functions
_op.get(op_name).add_type_rel("Identity")
_op.get(op_name).set_support_level(1)
_op.register_pattern(op_name, _op.OpPattern.ELEMWISE)
_op.register_stateful(op_name, False)
def clog(x):
return relay.Call(_op.get(op_name), [x])
tp = relay.TensorType((10, 10), "float32")
x = relay.var("x", tp)
sb = relay.ScopeBuilder()
t1 = sb.let("t1", clog(x))
t2 = sb.let("t2", relay.add(t1, x))
sb.ret(t2)
f = relay.Function([x], sb.get())
fchecked = infer_expr(f)
assert fchecked.checked_type == relay.FuncType([tp], tp)
def register_qnn_legalize(op_name, legal_op=None, level=10):
"""Register legal transformation function for a QNN op
Parameters
----------
op_name : str
The name of the operator
legal_op: function (attrs: Attrs, inputs: List[Expr]) -> new_expr: Expr
The function for transforming an expr to another expr.
level : int
The priority level
"""
return register(op_name, "FTVMQnnLegalize", legal_op, level)
def test_repeat_register():
op_name = "custom_log3"
_op.register(op_name, r"code(cal log of a tensor.)code")
with pytest.raises(tvm.error.TVMError) as cm:
_op.register(op_name)
assert "Operator custom_log3 is registered before" in str(cm.execption)
