def testProductTypeInferenceNumpy(self):
inputs = np.array([4, 5], dtype=np.int64)
outputs = np.array(([6, 7], [7, 8]), dtype=np.int64)
prog = test_programs.synthetic_pattern_variable_program(
include_types=False)
typed = type_inference.infer_types(prog, [inputs], NP_BACKEND)
expected_prog = test_programs.synthetic_pattern_variable_program()
self.assertSameTypes(expected_prog, typed)
alloc = allocation_strategy.optimize(typed)
lowered = lowering.lower_function_calls(alloc)
self.assertAllEqual(outputs, _execute(lowered, inputs, 15, NP_BACKEND))
def _product_type_execute(inputs, backend):
with tf.compat.v2.name_scope('product_types_program'):
return vm.execute(test_programs.synthetic_pattern_variable_program(),
[inputs],
max_stack_depth=4,
backend=backend)
def interpret(n):
return instructions.interpret(
test_programs.synthetic_pattern_variable_program(), n)
