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# Owner(s): ["oncall: mobile"]
import io
from itertools import product
from pathlib import Path
import torch
import torch.utils.bundled_inputs
from torch.jit.mobile import _load_for_lite_interpreter
from torch.testing._internal.common_utils import run_tests, TestCase
pytorch_test_dir = Path(__file__).resolve().parents[1]
class TestLiteScriptModule(TestCase):
def _save_load_mobile_module(self, script_module: torch.jit.ScriptModule):
buffer = io.BytesIO(
script_module._save_to_buffer_for_lite_interpreter(
_save_mobile_debug_info=True
)
)
buffer.seek(0)
mobile_module = _load_for_lite_interpreter(buffer)
return mobile_module
def _try_fn(self, fn, *args, **kwargs):
try:
return fn(*args, **kwargs)
except Exception as e:
return e
def test_versioned_div_tensor(self):
def div_tensor_0_3(self, other):
if self.is_floating_point() or other.is_floating_point():
return self.true_divide(other)
return self.divide(other, rounding_mode="trunc")
model_path = (
pytorch_test_dir
/ "cpp"
/ "jit"
/ "upgrader_models"
/ "test_versioned_div_tensor_v2.ptl"
)
mobile_module_v2 = _load_for_lite_interpreter(str(model_path))
jit_module_v2 = torch.jit.load(str(model_path))
current_mobile_module = self._save_load_mobile_module(jit_module_v2)
vals = (2.0, 3.0, 2, 3)
for val_a, val_b in product(vals, vals):
a = torch.tensor((val_a,))
b = torch.tensor((val_b,))
def _helper(m, fn):
m_results = self._try_fn(m, a, b)
fn_result = self._try_fn(fn, a, b)
if isinstance(m_results, Exception):
self.assertTrue(isinstance(fn_result, Exception))
else:
for result in m_results:
print("result: ", result)
print("fn_result: ", fn_result)
print(result == fn_result)
self.assertTrue(result.eq(fn_result))
# self.assertEqual(result, fn_result)
# old operator should produce the same result as applying upgrader of torch.div op
# _helper(mobile_module_v2, div_tensor_0_3)
# latest operator should produce the same result as applying torch.div op
# _helper(current_mobile_module, torch.div)
if __name__ == "__main__":
run_tests()
|
