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# Owner(s): ["oncall: jit"]
import io
import os
import sys
import torch
import zipfile
from torch.testing import FileCheck
from typing import Union
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase
if __name__ == '__main__':
raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
"\tpython test/test_jit.py TESTNAME\n\n"
"instead.")
class TestUpgraders(JitTestCase):
def _load_model_version(self, loaded_model):
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
zipped_model = zipfile.ZipFile(buffer)
# there was a change in how we store version number
# in a package between version 3 and 7.
# So we have to check for both.
try:
version = int(zipped_model.read('archive/version').decode("utf-8"))
return version
except KeyError:
version = int(zipped_model.read('archive/.data/version').decode("utf-8"))
return version
# TODO (tugsuu) We should ideally be generating this test cases.
def test_populated_upgrader_graph(self):
@torch.jit.script
def f():
return 0
buffer = io.BytesIO()
torch.jit.save(f, buffer)
buffer.seek(0)
torch.jit.load(buffer)
upgraders_size = torch._C._get_upgraders_map_size()
upgraders_dump = torch._C._dump_upgraders_map()
# make sure we only populate the upgrader map only once
# so we load it again and make sure the upgrader map has
# same content
buffer.seek(0)
torch.jit.load(buffer)
upgraders_size_second_time = torch._C._get_upgraders_map_size()
upgraders_dump_second_time = torch._C._dump_upgraders_map()
self.assertTrue(upgraders_size == upgraders_size_second_time)
self.assertTrue(upgraders_dump == upgraders_dump_second_time)
def test_add_value_to_version_map(self):
map_before_test = torch._C._get_operator_version_map()
upgrader_bumped_version = 3
upgrader_name = "_test_serialization_subcmul_0_2"
upgrader_schema = "aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=2) -> Tensor"
dummy_entry = torch._C._UpgraderEntry(upgrader_bumped_version, upgrader_name, upgrader_schema)
torch._C._test_only_add_entry_to_op_version_map("aten::_test_serialization_subcmul", dummy_entry)
map_after_test = torch._C._get_operator_version_map()
self.assertTrue("aten::_test_serialization_subcmul" in map_after_test)
self.assertTrue(len(map_after_test) - len(map_before_test) == 1)
torch._C._test_only_remove_entry_to_op_version_map("aten::_test_serialization_subcmul")
map_after_remove_test = torch._C._get_operator_version_map()
self.assertTrue("aten::_test_serialization_subcmul" not in map_after_remove_test)
self.assertEqual(len(map_after_remove_test), len(map_before_test))
def test_populated_test_upgrader_graph(self):
@torch.jit.script
def f():
return 0
buffer = io.BytesIO()
torch.jit.save(f, buffer)
buffer.seek(0)
torch.jit.load(buffer)
# upgrader map should have populated now
upgraders_size = torch._C._get_upgraders_map_size()
test_map = {"a": str(torch._C.Graph()), "c": str(torch._C.Graph())}
torch._C._test_only_populate_upgraders(test_map)
upgraders_size_after_test = torch._C._get_upgraders_map_size()
self.assertEqual(upgraders_size_after_test - upgraders_size, 2)
upgraders_dump = torch._C._dump_upgraders_map()
self.assertTrue("a" in upgraders_dump)
self.assertTrue("c" in upgraders_dump)
torch._C._test_only_remove_upgraders(test_map)
upgraders_size_after_remove_test = torch._C._get_upgraders_map_size()
self.assertTrue(upgraders_size_after_remove_test == upgraders_size)
upgraders_dump_after_remove_test = torch._C._dump_upgraders_map()
self.assertTrue("a" not in upgraders_dump_after_remove_test)
self.assertTrue("c" not in upgraders_dump_after_remove_test)
def test_aten_div_tensor_at_3(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_div_tensor_v3.pt"
loaded_model = torch.jit.load(model_path)
# there are 3 aten::div in this model
# And the upgrader for aten::div uses two
# div's because of if/else branch
FileCheck().check("prim::If").run(loaded_model.graph)
FileCheck().check_count("aten::div", 6).run(loaded_model.graph)
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 4)
loaded_model_twice = torch.jit.load(buffer)
# we check by its code because graph variable names
# can be different every time
self.assertEqual(loaded_model.code, loaded_model_twice.code)
def test_aten_full_other_variants(self):
def test_func():
a = torch.full([4, 5, 6], 4, names=["a", "b", "c"], dtype=torch.int64)
return a
scripted_func = torch.jit.script(test_func)
buffer = io.BytesIO()
torch.jit.save(scripted_func, buffer)
current_flag_value = torch._C._get_version_calculator_flag()
# calculate based on old version
torch._C._calculate_package_version_based_on_upgraders(False)
buffer.seek(0)
loaded_func = torch.jit.load(buffer)
version = self._load_model_version(loaded_func)
self.assertTrue(version == 5)
# calculate based on new version
torch._C._calculate_package_version_based_on_upgraders(True)
buffer.seek(0)
loaded_func = torch.jit.load(buffer)
version = self._load_model_version(loaded_func)
self.assertTrue(version == 5)
# make sure we preserve old behaviou
torch._C._calculate_package_version_based_on_upgraders(current_flag_value)
def test_aten_linspace(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_linspace_v7.ptl"
loaded_model = torch.jit.load(model_path)
sample_inputs = ((3, 10), (-10, 10), (4.0, 6.0), (3 + 4j, 4 + 5j))
for (a, b) in sample_inputs:
output_with_step, output_without_step = loaded_model(a, b)
# when no step is given, should have used 100
self.assertTrue(output_without_step.size(dim=0) == 100)
self.assertTrue(output_with_step.size(dim=0) == 5)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 8)
def test_aten_linspace_out(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_linspace_out_v7.ptl"
loaded_model = torch.jit.load(model_path)
sample_inputs = (
(3, 10, torch.empty((100,), dtype=torch.int64)),
(-10, 10, torch.empty((100,), dtype=torch.int64)),
(4.0, 6.0, torch.empty((100,), dtype=torch.float64)),
(3 + 4j, 4 + 5j, torch.empty((100,), dtype=torch.complex64)),
)
for (a, b, c) in sample_inputs:
output = loaded_model(a, b, c)
# when no step is given, should have used 100
self.assertTrue(output.size(dim=0) == 100)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 8)
def test_aten_logspace(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_logspace_v8.ptl"
loaded_model = torch.jit.load(model_path)
sample_inputs = ((3, 10), (-10, 10), (4.0, 6.0), (3 + 4j, 4 + 5j))
for (a, b) in sample_inputs:
output_with_step, output_without_step = loaded_model(a, b)
# when no step is given, should have used 100
self.assertTrue(output_without_step.size(dim=0) == 100)
self.assertTrue(output_with_step.size(dim=0) == 5)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 9)
def test_aten_logspace_out(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_logspace_out_v8.ptl"
loaded_model = torch.jit.load(model_path)
sample_inputs = (
(3, 10, torch.empty((100,), dtype=torch.int64)),
(-10, 10, torch.empty((100,), dtype=torch.int64)),
(4.0, 6.0, torch.empty((100,), dtype=torch.float64)),
(3 + 4j, 4 + 5j, torch.empty((100,), dtype=torch.complex64)),
)
for (a, b, c) in sample_inputs:
output = loaded_model(a, b, c)
# when no step is given, should have used 100
self.assertTrue(output.size(dim=0) == 100)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 9)
def test_aten_test_serialization(self):
model_path = pytorch_test_dir + "/jit/fixtures/_test_serialization_subcmul_v2.pt"
# add test version entry to the version map
upgrader_bumped_version = 3
upgrader_name = "_test_serialization_subcmul_0_2"
upgrader_schema = "aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=2) -> Tensor"
dummy_entry = torch._C._UpgraderEntry(upgrader_bumped_version, upgrader_name, upgrader_schema)
torch._C._test_only_add_entry_to_op_version_map("aten::_test_serialization_subcmul", dummy_entry)
# add test upgrader in the upgraders map
@torch.jit.script
def _test_serialization_subcmul_0_2(self: torch.Tensor, other: torch.Tensor, alpha: Union[int, float] = 2) -> torch.Tensor:
return other - (self * alpha)
torch._C._test_only_populate_upgraders({"_test_serialization_subcmul_0_2": str(_test_serialization_subcmul_0_2.graph)})
# test if the server is able to find the test upgraders and apply to IR
loaded_model = torch.jit.load(model_path)
FileCheck().check_count("aten::mul", 2).run(loaded_model.graph)
FileCheck().check_count("aten::sub", 2).run(loaded_model.graph)
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 3)
loaded_model_twice = torch.jit.load(buffer)
# we check by its' code because graph variable names
# can be different every time
self.assertEqual(loaded_model.code, loaded_model_twice.code)
torch._C._test_only_remove_entry_to_op_version_map("aten::_test_serialization_subcmul")
torch._C._test_only_remove_upgraders({"_test_serialization_subcmul_0_2": str(_test_serialization_subcmul_0_2.graph)})
def test_aten_div_scalar_at_3(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_div_scalar_float_v3.pt"
loaded_model = torch.jit.load(model_path)
FileCheck().check("prim::If").run(loaded_model.graph)
FileCheck().check_count("aten::div", 2).run(loaded_model.graph)
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
version = self._load_model_version(loaded_model)
self.assertEqual(version, 4)
loaded_model_twice = torch.jit.load(buffer)
self.assertEqual(loaded_model(torch.Tensor([5.0, 3.0]), 2.0),
loaded_model_twice(torch.Tensor([5.0, 3.0]), 2.0))
def test_aten_div_tensor_out_at_3(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_div_tensor_out_v3.pt"
loaded_model = torch.jit.load(model_path)
FileCheck().check("prim::If").run(loaded_model.graph)
FileCheck().check_count("aten::div", 2).run(loaded_model.graph)
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 4)
loaded_model_twice = torch.jit.load(buffer)
# we check by its' code because graph variable names
# can be different every time
self.assertEqual(loaded_model.code, loaded_model_twice.code)
def test_aten_full_at_4(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_full_integer_value_v4.pt"
loaded_model = torch.jit.load(model_path)
FileCheck().check_count("aten::Float", 1).run(loaded_model.graph)
FileCheck().check_count("aten::full", 2).run(loaded_model.graph)
buffer = io.BytesIO()
torch.jit.save(loaded_model, buffer)
buffer.seek(0)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 5)
loaded_model_twice = torch.jit.load(buffer)
# we check by its' code because graph variable names
# can be different every time
self.assertEqual(loaded_model.code, loaded_model_twice.code)
def test_aten_full_out_at_4(self):
model_path = pytorch_test_dir + "/jit/fixtures/test_versioned_full_preserved_v4.pt"
loaded_model = torch.jit.load(model_path)
FileCheck().check_count("aten::full", 5).run(loaded_model.graph)
version = self._load_model_version(loaded_model)
self.assertTrue(version == 5)
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