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# Owner(s): ["oncall: mobile"]
import fnmatch
import io
import shutil
import tempfile
import torch
import torch.utils.show_pickle
# from torch.utils.mobile_optimizer import optimize_for_mobile
from torch.jit.mobile import (
_load_for_lite_interpreter,
_get_mobile_model_contained_types,
_get_model_bytecode_version,
_get_model_ops_and_info,
_backport_for_mobile_to_buffer,
_backport_for_mobile)
from torch.testing._internal.common_utils import TestCase, run_tests
from pathlib import Path
pytorch_test_dir = Path(__file__).resolve().parents[1]
# script_module_v4.ptl and script_module_v5.ptl source code
# class TestModule(torch.nn.Module):
# def __init__(self, v):
# super().__init__()
# self.x = v
# def forward(self, y: int):
# increment = torch.ones([2, 4], dtype=torch.float64)
# return self.x + y + increment
# output_model_path = Path(tmpdirname, "script_module_v5.ptl")
# script_module = torch.jit.script(TestModule(1))
# optimized_scripted_module = optimize_for_mobile(script_module)
# exported_optimized_scripted_module = optimized_scripted_module._save_for_lite_interpreter(
# str(output_model_path))
SCRIPT_MODULE_V4_BYTECODE_PKL = '''
(4,
('__torch__.*.TestModule.forward',
(('instructions',
(('STOREN', 1, 2),
('DROPR', 1, 0),
('LOADC', 0, 0),
('LOADC', 1, 0),
('MOVE', 2, 0),
('OP', 0, 0),
('LOADC', 1, 0),
('OP', 1, 0),
('RET', 0, 0))),
('operators', (('aten::add', 'int'), ('aten::add', 'Scalar'))),
('constants',
(torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, '0', 'cpu', 8),),
0,
(2, 4),
(4, 1),
False,
collections.OrderedDict()),
1)),
('types', ()),
('register_size', 2)),
(('arguments',
((('name', 'self'),
('type', '__torch__.*.TestModule'),
('default_value', None)),
(('name', 'y'), ('type', 'int'), ('default_value', None)))),
('returns',
((('name', ''), ('type', 'Tensor'), ('default_value', None)),)))))
'''
SCRIPT_MODULE_V5_BYTECODE_PKL = '''
(5,
('__torch__.*.TestModule.forward',
(('instructions',
(('STOREN', 1, 2),
('DROPR', 1, 0),
('LOADC', 0, 0),
('LOADC', 1, 0),
('MOVE', 2, 0),
('OP', 0, 0),
('LOADC', 1, 0),
('OP', 1, 0),
('RET', 0, 0))),
('operators', (('aten::add', 'int'), ('aten::add', 'Scalar'))),
('constants',
(torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, 'constants/0', 'cpu', 8),),
0,
(2, 4),
(4, 1),
False,
collections.OrderedDict()),
1)),
('types', ()),
('register_size', 2)),
(('arguments',
((('name', 'self'),
('type', '__torch__.*.TestModule'),
('default_value', None)),
(('name', 'y'), ('type', 'int'), ('default_value', None)))),
('returns',
((('name', ''), ('type', 'Tensor'), ('default_value', None)),)))))
'''
SCRIPT_MODULE_V6_BYTECODE_PKL = '''
(6,
('__torch__.*.TestModule.forward',
(('instructions',
(('STOREN', 1, 2),
('DROPR', 1, 0),
('LOADC', 0, 0),
('LOADC', 1, 0),
('MOVE', 2, 0),
('OP', 0, 0),
('OP', 1, 0),
('RET', 0, 0))),
('operators', (('aten::add', 'int', 2), ('aten::add', 'Scalar', 2))),
('constants',
(torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, '0', 'cpu', 8),),
0,
(2, 4),
(4, 1),
False,
collections.OrderedDict()),
1)),
('types', ()),
('register_size', 2)),
(('arguments',
((('name', 'self'),
('type', '__torch__.*.TestModule'),
('default_value', None)),
(('name', 'y'), ('type', 'int'), ('default_value', None)))),
('returns',
((('name', ''), ('type', 'Tensor'), ('default_value', None)),)))))
'''
SCRIPT_MODULE_BYTECODE_PKL = {
4: {
"bytecode_pkl": SCRIPT_MODULE_V4_BYTECODE_PKL,
"model_name": "script_module_v4.ptl"},
}
# The minimum version a model can be backported to
# Need to be updated when a bytecode version is completely retired
MINIMUM_TO_VERSION = 4
class testVariousModelVersions(TestCase):
def test_get_model_bytecode_version(self):
def check_model_version(model_path, expect_version):
actual_version = _get_model_bytecode_version(model_path)
assert(actual_version == expect_version)
for version, model_info in SCRIPT_MODULE_BYTECODE_PKL.items():
model_path = pytorch_test_dir / "cpp" / "jit" / model_info["model_name"]
check_model_version(model_path, version)
def test_bytecode_values_for_all_backport_functions(self):
# Find the maximum version of the checked in models, start backporting to the minimum support version,
# and comparing the bytecode pkl content.
# It can't be merged to the test `test_all_backport_functions`, because optimization is dynamic and
# the content might change when optimize function changes. This test focuses
# on bytecode.pkl content validation. For the content validation, it is not byte to byte check, but
# regular expression matching. The wildcard can be used to skip some specific content comparison.
maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys())
current_from_version = maximum_checked_in_model_version
with tempfile.TemporaryDirectory() as tmpdirname:
while current_from_version > MINIMUM_TO_VERSION:
# Load model v5 and run forward method
model_name = SCRIPT_MODULE_BYTECODE_PKL[current_from_version]["model_name"]
input_model_path = pytorch_test_dir / "cpp" / "jit" / model_name
# A temporary model file will be export to this path, and run through bytecode.pkl
# content check.
tmp_output_model_path_backport = Path(tmpdirname, "tmp_script_module_backport.ptl")
current_to_version = current_from_version - 1
backport_success = _backport_for_mobile(input_model_path, tmp_output_model_path_backport, current_to_version)
assert(backport_success)
expect_bytecode_pkl = SCRIPT_MODULE_BYTECODE_PKL[current_to_version]["bytecode_pkl"]
buf = io.StringIO()
torch.utils.show_pickle.main(
["", tmpdirname + "/" + tmp_output_model_path_backport.name + "@*/bytecode.pkl"],
output_stream=buf)
output = buf.getvalue()
acutal_result_clean = "".join(output.split())
expect_result_clean = "".join(expect_bytecode_pkl.split())
isMatch = fnmatch.fnmatch(acutal_result_clean, expect_result_clean)
assert(isMatch)
current_from_version -= 1
shutil.rmtree(tmpdirname)
# Please run this test manually when working on backport.
# This test passes in OSS, but fails internally, likely due to missing step in build
# def test_all_backport_functions(self):
# # Backport from the latest bytecode version to the minimum support version
# # Load, run the backport model, and check version
# class TestModule(torch.nn.Module):
# def __init__(self, v):
# super().__init__()
# self.x = v
# def forward(self, y: int):
# increment = torch.ones([2, 4], dtype=torch.float64)
# return self.x + y + increment
# module_input = 1
# expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64)
# # temporary input model file and output model file will be exported in the temporary folder
# with tempfile.TemporaryDirectory() as tmpdirname:
# tmp_input_model_path = Path(tmpdirname, "tmp_script_module.ptl")
# script_module = torch.jit.script(TestModule(1))
# optimized_scripted_module = optimize_for_mobile(script_module)
# exported_optimized_scripted_module = optimized_scripted_module._save_for_lite_interpreter(str(tmp_input_model_path))
# current_from_version = _get_model_bytecode_version(tmp_input_model_path)
# current_to_version = current_from_version - 1
# tmp_output_model_path = Path(tmpdirname, "tmp_script_module_backport.ptl")
# while current_to_version >= MINIMUM_TO_VERSION:
# # Backport the latest model to `to_version` to a tmp file "tmp_script_module_backport"
# backport_success = _backport_for_mobile(tmp_input_model_path, tmp_output_model_path, current_to_version)
# assert(backport_success)
# backport_version = _get_model_bytecode_version(tmp_output_model_path)
# assert(backport_version == current_to_version)
# # Load model and run forward method
# mobile_module = _load_for_lite_interpreter(str(tmp_input_model_path))
# mobile_module_result = mobile_module(module_input)
# torch.testing.assert_close(mobile_module_result, expected_mobile_module_result)
# current_to_version -= 1
# # Check backport failure case
# backport_success = _backport_for_mobile(tmp_input_model_path, tmp_output_model_path, MINIMUM_TO_VERSION - 1)
# assert(not backport_success)
# # need to clean the folder before it closes, otherwise will run into git not clean error
# shutil.rmtree(tmpdirname)
# Check just the test_backport_bytecode_from_file_to_file mechanism but not the function implementations
def test_backport_bytecode_from_file_to_file(self):
maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys())
script_module_v5_path = pytorch_test_dir / "cpp" / "jit" / SCRIPT_MODULE_BYTECODE_PKL[
maximum_checked_in_model_version]["model_name"]
if (maximum_checked_in_model_version > MINIMUM_TO_VERSION):
with tempfile.TemporaryDirectory() as tmpdirname:
tmp_backport_model_path = Path(tmpdirname, "tmp_script_module_v5_backported_to_v4.ptl")
# backport from file
success = _backport_for_mobile(
script_module_v5_path,
tmp_backport_model_path,
maximum_checked_in_model_version - 1)
assert(success)
buf = io.StringIO()
torch.utils.show_pickle.main(
["", tmpdirname + "/" + tmp_backport_model_path.name + "@*/bytecode.pkl"],
output_stream=buf)
output = buf.getvalue()
expected_result = SCRIPT_MODULE_V4_BYTECODE_PKL
acutal_result_clean = "".join(output.split())
expect_result_clean = "".join(expected_result.split())
isMatch = fnmatch.fnmatch(acutal_result_clean, expect_result_clean)
assert(isMatch)
# Load model v4 and run forward method
mobile_module = _load_for_lite_interpreter(str(tmp_backport_model_path))
module_input = 1
mobile_module_result = mobile_module(module_input)
expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64)
torch.testing.assert_close(mobile_module_result, expected_mobile_module_result)
shutil.rmtree(tmpdirname)
# Check just the _backport_for_mobile_to_buffer mechanism but not the function implementations
def test_backport_bytecode_from_file_to_buffer(self):
maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys())
script_module_v5_path = pytorch_test_dir / "cpp" / "jit" / SCRIPT_MODULE_BYTECODE_PKL[
maximum_checked_in_model_version]["model_name"]
if (maximum_checked_in_model_version > MINIMUM_TO_VERSION):
# Backport model to v4
script_module_v4_buffer = _backport_for_mobile_to_buffer(
script_module_v5_path, maximum_checked_in_model_version - 1)
buf = io.StringIO()
# Check version of the model v4 from backport
bytesio = io.BytesIO(script_module_v4_buffer)
backport_version = _get_model_bytecode_version(bytesio)
assert(backport_version == maximum_checked_in_model_version - 1)
# Load model v4 from backport and run forward method
bytesio = io.BytesIO(script_module_v4_buffer)
mobile_module = _load_for_lite_interpreter(bytesio)
module_input = 1
mobile_module_result = mobile_module(module_input)
expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64)
torch.testing.assert_close(mobile_module_result, expected_mobile_module_result)
def test_get_model_ops_and_info(self):
# TODO update this to be more in the style of the above tests after a backport from 6 -> 5 exists
script_module_v6 = pytorch_test_dir / "cpp" / "jit" / "script_module_v6.ptl"
ops_v6 = _get_model_ops_and_info(script_module_v6)
assert(ops_v6["aten::add.int"].num_schema_args == 2)
assert(ops_v6["aten::add.Scalar"].num_schema_args == 2)
def test_get_mobile_model_contained_types(self):
class MyTestModule(torch.nn.Module):
def __init__(self):
super(MyTestModule, self).__init__()
def forward(self, x):
return x + 10
sample_input = torch.tensor([1])
script_module = torch.jit.script(MyTestModule())
script_module_result = script_module(sample_input)
buffer = io.BytesIO(script_module._save_to_buffer_for_lite_interpreter())
buffer.seek(0)
type_list = _get_mobile_model_contained_types(buffer)
assert(len(type_list) >= 0)
if __name__ == '__main__':
run_tests()
|
