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Revert D25433268: [PyTorch Mobile] Preserve bundled input related methods when calling optimize_for_mobile

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Test Plan: revert-hammer

Differential Revision:
D25433268 (95233870f2)

Original commit changeset: 0bf9b4afe64b

fbshipit-source-id: bba97e48ce0e72f9d1db5159065bb6495d62666c
This commit is contained in:
Luca Wehrstedt
2020-12-10 04:37:41 -08:00
committed by Facebook GitHub Bot
parent b5a7e25059
commit 2255e68da8
2 changed files with 0 additions and 76 deletions
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64
test/test_mobile_optimizer.py
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@ -8,7 +8,6 @@ from torch.utils.mobile_optimizer import *
from torch.nn import functional as F
from torch._C import MobileOptimizerType
from torch.testing._internal.common_quantized import override_quantized_engine
from torch.nn.modules.module import ModuleAttributeError
FileCheck = torch._C.FileCheck
@ -269,69 +268,6 @@ class TestOptimizer(unittest.TestCase):
bi_module_lint_list = generate_mobile_module_lints(bi_module)
self.assertEqual(len(bi_module_lint_list), 0)
def test_preserve_bundled_inputs_methods(self):
class MyBundledInputModule(torch.nn.Module):
def __init__(self):
super(MyBundledInputModule, self).__init__()
def forward(self, inputs):
return inputs
class MyIncompleteBundledInputModule(torch.nn.Module):
def __init__(self):
super(MyIncompleteBundledInputModule, self).__init__()
def forward(self, inputs):
return inputs
@torch.jit.export
def get_all_bundled_inputs(self):
pass
bi_module = torch.jit.script(MyBundledInputModule())
module_optim_bi_not_preserved = optimize_for_mobile(bi_module)
# Expected to be False since no bundled inputs methods were added
self.assertFalse(
hasattr(module_optim_bi_not_preserved, 'get_all_bundled_inputs') or
hasattr(module_optim_bi_not_preserved, 'get_num_bundled_inputs') or
hasattr(module_optim_bi_not_preserved, 'run_on_bundled_input')
)
# We expect an exception here
with self.assertRaises(ModuleAttributeError):
module_optim_bi_not_preserved.run_on_bundled_input(0)
# Add bundled inputs methods to the module
torch.utils.bundled_inputs.augment_model_with_bundled_inputs(
bi_module, [(torch.tensor([1]),)], [])
# Now they should be preserved
module_optim_bi_preserved = optimize_for_mobile(bi_module)
# All of the bundled inputs methods were preserved
self.assertTrue(
hasattr(module_optim_bi_preserved, 'get_all_bundled_inputs') and
hasattr(module_optim_bi_preserved, 'get_num_bundled_inputs') and
hasattr(module_optim_bi_preserved, 'run_on_bundled_input')
)
# We do not expect an exception here
module_optim_bi_preserved.run_on_bundled_input(0)
bundled_input = module_optim_bi_preserved.get_all_bundled_inputs()[0]
module_optim_bi_preserved(*bundled_input)
# If not all 3 bundled inputs methods are present in the module,
# we will not try to preserve them unless specified by the user.
incomplete_bi_module = torch.jit.script(MyIncompleteBundledInputModule())
incomplete_bi_module_optim = optimize_for_mobile(incomplete_bi_module)
self.assertFalse(hasattr(incomplete_bi_module_optim, 'get_all_bundled_inputs'))
# Specifically preserve get_all_bundled_inputs even if it's the only one
# bundled inputs method available.
incomplete_bi_module_optim = optimize_for_mobile(incomplete_bi_module, preserved_methods=['get_all_bundled_inputs'])
self.assertTrue(hasattr(incomplete_bi_module_optim, 'get_all_bundled_inputs'))
@unittest.skipUnless(torch.backends.xnnpack.enabled,
" XNNPACK must be enabled for these tests."
" Please build with USE_XNNPACK=1.")