blob: 2db53825a786dc897688ab4530d6d4816b8f90c4 [file] [log] [blame]
import torch
import torch.nn as nn
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing import FileCheck
import io
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 TestFreezing(JitTestCase):
def test_freeze_module(self):
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
self.a = 1 # folded
self.b = 1.2 # folded
self.c = "hello" # folded
self.d = [1, 1] # folded
self.e = [1.0, 1.1] # folded
self.f = ["hello", "world"] # folded
self.g = ([1, 2], 3.2, "4.4", torch.tensor([5.5], requires_grad=True)) # folded
self.h = {"layer" : [torch.tensor([7.7], requires_grad=True)]}
self.t = torch.tensor([1.2, 2.4], requires_grad=True) # folded
self.ts = [torch.tensor([1.0, 2.0], requires_grad=True), torch.tensor([3.0, 4.0], requires_grad=True)] # folded
self.tt = [[torch.tensor([3.3, 2.3], requires_grad=True), None]]
def forward(self, x):
return str(self.a) + str(self.b) + self.c + str(self.d) + \
str(self.e) + str(self.f) + str(self.g) + str(self.h['layer']) + str(self.t) + str(self.ts) + str(self.tt)
m = torch.jit.script(M())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
m._c = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# tt = ...
# }
# ...
# }
self.assertFalse(m._c.hasattr('a'))
self.assertFalse(m._c.hasattr('b'))
self.assertFalse(m._c.hasattr('c'))
self.assertFalse(m._c.hasattr('d'))
self.assertFalse(m._c.hasattr('e'))
self.assertFalse(m._c.hasattr('f'))
self.assertFalse(m._c.hasattr('g'))
self.assertFalse(m._c.hasattr('h'))
self.assertFalse(m._c.hasattr('t'))
self.assertFalse(m._c.hasattr('ts'))
self.assertFalse(m._c.hasattr('tt'))
output_f = m.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_submodule(self):
class SubModule(nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.a = 11
self.b = 2
def forward(self, x):
return self.a + self.b
class SubModule2(nn.Module):
def __init__(self):
super(SubModule2, self).__init__()
self.a = 12
self.b = 2
def forward(self, x):
self.b = 30
return self.a + self.b
class TestModule(nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub1 = SubModule()
self.sub2 = SubModule2()
self.a = 3
self.b = 4
def forward(self, x):
self.b = 20
return self.sub1(x) + self.a + self.b + self.sub2(x)
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# sub2 = ...
# b =
# }
# ...
# submodule {
# module m {
# attributes {
# sub2 = ...
# b =
# }
# ...
# }
# }
# }
self.assertFalse(mf.hasattr('sub1'))
self.assertFalse(mf.hasattr('a'))
self.assertTrue(mf.hasattr('b'))
self.assertTrue(mf.hasattr('sub2'))
self.assertTrue(mf.sub2.hasattr('b')) # verify b is preserved in sub2
self.assertFalse(mf.sub2.hasattr('a')) # verify a is removed in sub2
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_sharedclasstype(self):
class SubModule(nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] += 10
return self. b
@torch.jit.export
def modify_b(self, x):
self.b[0] += 20
return self.a
class SubModule2(nn.Module):
def __init__(self):
super(SubModule2, self).__init__()
self.sub = SubModule()
self.b = torch.tensor([3.3])
def forward(self, x):
y = self.sub.modify_b(x)
return y + self.b
class TestModule(nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub1 = SubModule() # sub1 and sub2.sub shared same class type.
self.sub2 = SubModule2()
self.a = torch.tensor([4.4])
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z + self.a
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Checking if Frozen module looks as below
# module mf {
# attributes {
# sub1 = ...
# sub2 = ...
# }
# ...
# submodules {
# module sub1 {
# attributes {
# a = ...
# b = ...
# }
# ...
# }
# module sub2 {
# attributes {
# sub = ...
# }
# ...
# submodule {
# module sub {
# attributes {
# a = ...
# b = ...
# }
# ...
# }
# }
# }
# }
# }
self.assertTrue(mf.hasattr('sub1'))
self.assertTrue(mf.sub1.hasattr('a'))
self.assertTrue(mf.sub1.hasattr('b'))
self.assertFalse(mf.hasattr('a'))
self.assertTrue(mf.hasattr('sub2'))
self.assertTrue(mf.sub2.hasattr('sub'))
self.assertFalse(mf.sub2.hasattr('b'))
self.assertTrue(mf.sub2.sub.hasattr('a'))
self.assertTrue(mf.sub2.sub.hasattr('b'))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_nestedaliasing(self):
class SubModule(nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] = 10
return self. b
@torch.jit.export
def modify_b(self, x):
self.b[0] = 20
return self.a
Sub = SubModule()
class SubModule2(nn.Module):
def __init__(self):
super(SubModule2, self).__init__()
self.sub = Sub # aliasing
def forward(self, x):
return self.sub.a
class TestModule(nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub1 = Sub # aliasing
self.sub2 = SubModule2()
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z
m = torch.jit.script(TestModule())
m.eval()
mf = torch._C._freeze_module(m._c)
self.assertTrue(mf.hasattr('sub1'))
self.assertTrue(mf.sub1.hasattr('a'))
self.assertFalse(mf.sub1.hasattr('b'))
self.assertTrue(mf.hasattr('sub2'))
self.assertTrue(mf.sub2.hasattr('sub'))
self.assertTrue(mf.sub2.sub.hasattr('a')) # Freezing detects that self.sub2.sub.a and self.sub1.a are alias
self.assertFalse(mf.sub2.sub.hasattr('b'))
input = torch.randn(2, 2)
output_s = m.forward(input)
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
# FIXME: JIT is not honoring aliasing. 'Sub' module is copied. As a result
# Eager and Script modules produce different output.
def test_freeze_module_with_nestedaliasingscalar(self):
class SubModule(nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.a = 1.1
self.b = 2.2
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a = 10.0
return self. b
@torch.jit.export
def modify_b(self, x):
self.b = 20.0
return self.a
Sub = SubModule()
class SubModule2(nn.Module):
def __init__(self):
super(SubModule2, self).__init__()
self.sub = Sub # aliasing
def forward(self, x):
return self.sub.a
class TestModule(nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub1 = Sub # aliasing
self.sub2 = SubModule2()
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z
m = TestModule()
ms = torch.jit.script(m)
ms.eval()
mf = torch._C._freeze_module(ms._c)
self.assertTrue(mf.hasattr('sub1'))
self.assertTrue(mf.sub1.hasattr('a'))
self.assertFalse(mf.sub1.hasattr('b'))
# sub2 is fully folded becasue self.sub1 and self.sub2.sub are not alias (Scripting bug)
self.assertFalse(mf.hasattr('sub2'))
input = torch.randn(2, 2)
output = m.forward(input)
output_s = ms.forward(input)
output_f = mf.forward(input)
print(output, " ", output_s, " ", output_f)
# Should be equal
self.assertNotEqual(output, output_s)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_helperfunction(self):
class SubModule(nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.a = 11
self.b = 2
def forward(self, x):
return self.a + self.b
class TestModule(nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub = SubModule()
self.a = 3
self.b = 4
def forward(self, x):
self.b = 20
return self._forward(x) + self.a + self.b
def _forward(self, x):
return self.sub(x)
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
mf = torch._C._freeze_module(m._c)
self.assertFalse(mf.hasattr('sub'))
self.assertFalse(mf.hasattr('a'))
self.assertTrue(mf.hasattr('b'))
with self.assertRaisesRegex(RuntimeError, "TestModule does not have a field with name '_forward'"):
mf._forward(x)
def test_freeze_module_with_inplace_mutable(self):
class FreezeMe(torch.jit.ScriptModule):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [11, 22]
@torch.jit.script_method
def forward(self, x):
for i in range(3):
self.a.append(i)
return self.a
m = FreezeMe()
m.eval()
m_f = torch._C._freeze_module(m._c)
self.assertTrue(m_f.hasattr('a'))
m.forward(torch.tensor([3]))
out = m_f.forward(torch.tensor([5]))
expected = [11, 22, 0, 1, 2, 0, 1, 2]
self.assertEqual(out, expected)
# Mutable attributes
def test_freeze_module_with_mutable_list(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [1, 2]
def forward(self, x):
return self.a
m = FreezeMe()
m.eval()
m.a.append(3)
m_s = torch.jit.script(m)
v = m_s.a
v.append(4)
m_s.a = v
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# Post-freezing mutating m_s.a does not affect m_f (m_f has its own copy).
v = m_s.a
v.append(5)
m_s.a = v
self.assertFalse(m_f.hasattr('a'))
out = m_f.forward(torch.tensor([5]))
expected = [1, 2, 3, 4]
self.assertEqual(out, expected)
def test_freeze_module_with_mutable_dict(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = {"layer" : "4"}
def forward(self, x):
return self.a
@torch.jit.export
def modify_a(self, x):
self.a["layer"] = self.a["layer"] + "1"
return self.a
m = FreezeMe()
m.eval()
m.a["layer2"] = "3"
m_s = torch.jit.script(m)
t = torch.tensor(5)
m_s.modify_a(t)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
m.a["layer2"] += "2"
m_s.modify_a(t)
self.assertFalse(m_f.hasattr('a'))
out = m_f.forward(t)
expected = {"layer" : "411", "layer2" : "3"}
self.assertEqual(out, expected)
def test_freeze_module_with_mutable_tensor(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1., 2., 3.])
def forward(self, x):
return self.a
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.a[1] += 3.0
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# Post-freezing tensor attribute mutations affect m_f.
# FIXME: deep copy all folded attributes so that m_f has full ownership.
m_s.a[0] += 5.0
self.assertFalse(m_f.hasattr('a'))
out = m_f.forward(torch.tensor([5]))
expected = [6., 5., 3.]
self.assertEqual(out, expected)
def test_freeze_module_with_tuple(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = (torch.tensor([1, 2, 3, 4, 5, 6]), "hi")
def forward(self, x):
if (x[0] == 2.0):
self.a[0][0] = 10
return self.a[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([2.0])
expected = m_s.forward(inp)
m_s.a[0][0] = 1
m_f = torch._C._freeze_module(m_s._c)
self.assertFalse(m_f.hasattr('a'))
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_tensor(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
def forward(self, x):
x = self.a.view(2, 3)
x[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('a'))
m_f.a[0] -= 10
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_list(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [torch.tensor([1, 2, 3, 4, 5, 6])]
def forward(self, x):
self.a[0][1] += 10
return self.a[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_s.a[0][1] -= 10
m_f = torch._C._freeze_module(m_s._c)
self.assertFalse(m_f.hasattr('a'))
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = self.a.view(2, 3)
def forward(self, x):
self.b[1] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('a'))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = torch.tensor(51) # 1+2+3+14+15+16
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr2(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = {"layer" : ([self.a.view(2, 3), torch.tensor([10])], 20)}
self.c = ([self.a.view(2, 3), torch.tensor([10])], 20)
self.d = (self.a.view(2, 3), 20)
def forward(self, x):
self.d[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
with self.assertRaisesRegex(RuntimeError, "module contains attributes values that overlaps"):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_aliased_tensor_attr3(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = [self.a, torch.tensor([10])]
def forward(self, x):
self.a[1] += 10
return self.b[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('a'))
self.assertTrue(m_f.hasattr('b'))
out = m_f.forward(inp)
expected += 10 # account for self.a += 10.
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr4(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = [self.a, torch.tensor([10])]
def forward(self, x):
self.b[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_s.a[0] -= 10
with self.assertRaisesRegex(RuntimeError, "module contains attributes values that overlaps"):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_overlapping_attrs(self):
a = torch.tensor([1, 2, 3, 4, 5, 6])
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.b = [a.view(3, 2), torch.tensor([10])]
self.c = (20, a.view(2, 3))
def forward(self, x):
self.b[0][0] += 10
return self.c[1].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
a[0] -= 10
with self.assertRaisesRegex(RuntimeError, "module contains attributes values that overlaps"):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_aliased_attr(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = self.a
self.c = (self.a, 10)
def forward(self, x):
self.b[1] += 10
return str(self.a) + str(self.c)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# FIXME: It should be assertTrue. Currently scripting is making a copy for setting self.b (see #33034)
self.assertFalse(m_f.hasattr('a'))
self.assertFalse(m_f.hasattr('c'))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m_s.forward(inp)
self.assertEqual(out, expected)
# Check attribute a is preserved. Alias analysis detects that 'a' has output writers.
# In this example, 'a' is not mutated. However, we do not track which sub
# values of a composite ivalue is mutated.
def test_freeze_module_with_aliased_attr2(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = ([11], [10])
def forward(self, x):
v = self.a
self.b = (v, [12])
v2 = self.b[1]
v2.append(7)
return str(v) + str(v2)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('a'))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_attr3(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = ([11], [10])
def forward(self, x):
v = self.a
v2 = (v, [12])
v3 = v2[0]
v3.append(7)
return str(self.a)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('a'))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_return_self(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.a = torch.tensor([1., 2., 3.])
def forward(self, x):
return self
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
with self.assertRaisesRegex(RuntimeError, "attempted to freeze a module that return itself"):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_return_sub_module(self):
class FreezeMe(nn.Module):
def __init__(self):
super(FreezeMe, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
def forward(self, x):
return self.conv1
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr('conv1'))
def test_freeze_module_in_training_mode(self):
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.functional.log_softmax(x, dim=1)
return output
model = torch.jit.script(Net())
model.train()
with self.assertRaisesRegex(RuntimeError, 'Freezing module in training mode is not yet supported'):
mTrain_freezed = torch._C._freeze_module(model._c)
model.eval()
mEval_freezed = torch._C._freeze_module(model._c)
self.assertFalse(mEval_freezed.hasattr('conv1'))
self.assertFalse(mEval_freezed.hasattr('conv2'))
self.assertFalse(mEval_freezed.hasattr('dropout1'))
self.assertFalse(mEval_freezed.hasattr('training'))
self.assertFalse(mEval_freezed.hasattr('fc1'))
self.assertFalse(mEval_freezed.hasattr('dropout2'))
self.assertFalse(mEval_freezed.hasattr('fc2'))
with self.assertRaisesRegex(RuntimeError, "does not have a field with name 'state_dict'"):
print(mEval_freezed.state_dict())
buffer = io.BytesIO()
torch.jit.save(mEval_freezed, buffer)
buffer.seek(0)
m = torch.jit.load(buffer)
FileCheck().check_not('GetAttr[name=') \
.run(m._c._get_method('forward').graph)
def test_freeze_module_detach_gradient(self):
mod = nn.Conv2d(8, 3, 4, 2, 1)
self.assertTrue(mod.weight.requires_grad)
smod = torch.jit.script(mod)
smod.eval()
fmod = torch._C._freeze_module(smod._c)
self.assertTrue(mod.weight.requires_grad)
self.assertTrue(smod.weight.requires_grad)
self.assertFalse(fmod.hasattr('weight'))
inp = torch.ones(1, 8, 32, 32)
out1 = fmod.forward(inp)
# FIXME: frozen module mutated from outside (original module).
smod.weight[0, 0, 0, 0] += 100.0
out2 = fmod.forward(inp)
out3 = smod(inp)
self.assertNotEqual(out1, out2)
self.assertEqual(out2, out3)