def test_generated_functional_tests(self):
with enable_profiling_mode():
stats = [("Name", "Ifs/Loops", "non-tensor ops")]
for test in nn_functional_tests:
test_name = test[0]
fn, inputs = get_nn_functional_compiled_fn_and_inputs(*test)
for _ in range(6):
fn(*inputs)
g = torch.jit.last_executed_optimized_graph()
stats.append((test_name, num_ifs_loops(g), num_non_tensor_nodes(g)))
for line in stats:
print(line)
def test_nn_module_tests(self):
with enable_profiling_mode():
stats = [("Name", "Ifs/Loops", "non-tensor ops")]
for test in get_all_nn_module_tests():
out = try_get_nn_module_compiled_mod_and_inputs(**test)
if not out:
continue
mod, inputs = out
test_name = get_nn_mod_test_name(**test)
for _ in range(6):
mod(*inputs)
g = torch.jit.last_executed_optimized_graph()
stats.append((test_name, num_ifs_loops(g), num_non_tensor_nodes(g)))
for line in stats:
print(line)