| import unittest |
| import os |
| import sys |
| from typing import List, Dict, Optional, Tuple |
| |
| import torch |
| import torch.nn as nn |
| from torch import Tensor |
| from torch.testing import FileCheck |
| from collections import OrderedDict |
| |
| # 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, _tmp_donotuse_dont_inline_everything |
| |
| 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 TestRecursiveScript(JitTestCase): |
| def test_inferred_nonetype(self): |
| class M(nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.x = None |
| |
| def forward(self): |
| assert self.x is None |
| |
| m = torch.jit.script(M()) |
| self.checkModule(M(), ()) |
| |
| def test_script_function_attribute(self): |
| @torch.jit.script |
| def fn1(x): |
| return x + x |
| |
| @torch.jit.script |
| def fn2(x): |
| return x - x |
| |
| class M(torch.nn.Module): |
| def __init__(self, fn): |
| super(M, self).__init__() |
| self.fn = fn |
| |
| def forward(self, x): |
| return self.fn(x) |
| |
| fn1_mod = M(fn1) |
| fn2_mod = M(fn2) |
| |
| self.checkModule(fn1_mod, (torch.randn(2, 2),)) |
| self.checkModule(fn2_mod, (torch.randn(2, 2),)) |
| |
| def test_python_function_attribute(self): |
| class M(torch.nn.Module): |
| def __init__(self, fn): |
| super(M, self).__init__() |
| self.fn = fn |
| |
| def forward(self, x): |
| return self.fn(x) |
| |
| mod = M(torch.sigmoid) |
| |
| self.checkModule(mod, (torch.randn(2, 2),)) |
| |
| def test_failed_function_compilation(self): |
| def fn(x): |
| return i_dont_exist |
| |
| class M(torch.nn.Module): |
| def __init__(self, fn): |
| super(M, self).__init__() |
| self.fn = fn |
| |
| def forward(self, x): |
| return self.fn(x) |
| |
| m = M(fn) |
| with self.assertRaisesRegex(RuntimeError, "failed to compile"): |
| torch.jit.script(m) |
| |
| def test_init_error(self): |
| class M(nn.Module): |
| def __init__(self): |
| self.x = 2 |
| |
| def forward(self): |
| pass |
| |
| with self.assertRaisesRegex(RuntimeError, "has not been initialized"): |
| torch.jit.script(M()) |
| |
| def test_script_after_eval(self): |
| class M(nn.Module): |
| def forward(self): |
| if self.training: |
| return 2 |
| else: |
| return 0 |
| |
| m = M() |
| sm1 = torch.jit.script(m) |
| m.eval() |
| sm2 = torch.jit.script(m) |
| |
| # m is in eval mode, training should be False |
| self.assertFalse(m.training) |
| |
| # sm1 was created while m had training = True |
| self.assertTrue(sm1.training) |
| self.assertEqual(sm1.training, sm1._c.getattr('training')) |
| self.assertEqual(sm1(), 2) |
| |
| # sm2 was created after m was eval'ed |
| self.assertFalse(sm2.training) |
| self.assertEqual(sm2.training, sm2._c.getattr('training')) |
| self.assertEqual(sm2(), 0) |
| |
| def test_module_name(self): |
| class MyModule(torch.nn.Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| self.x = 2 |
| |
| def forward(self, t): |
| return t + self.x |
| |
| m = torch.jit.script(MyModule()) |
| FileCheck().check("MyModule").run(m.graph) |
| |
| def test_repeated_error_stack(self): |
| def d(x): |
| return "a" - 2 |
| |
| def c(x): |
| return d(x) |
| |
| def b(x): |
| return c(x) |
| |
| def a(x): |
| return b(x) |
| |
| try: |
| torch.jit.script(a) |
| except Exception as e: |
| FileCheck().check_count("is being compiled", 2).run(str(e)) |
| |
| try: |
| torch.jit.script(a) |
| except Exception as e: |
| # Make sure that no entries are left over from the previous failure |
| FileCheck().check_count("is being compiled", 2).run(str(e)) |
| |
| @unittest.skipIf(True, "Class annotations are a thing in > 3.5, need to fix for < 3.7") |
| def test_constants_with_final(self): |
| class M(torch.nn.Module): |
| # TODO: Use this (see below) |
| # x : torch.jit.Final[int] |
| |
| def __init__(self): |
| super(M, self).__init__() |
| self.x = 2 |
| |
| def forward(self, t): |
| return t + self.x |
| |
| |
| # TODO: Fix this test so that we can actually define the class like |
| # class M(torch.nn.Module): |
| # x : torch.jit.Final[int] |
| M.__annotations__ = {'x': torch.jit.Final[int]} |
| |
| m = M() |
| |
| self.checkModule(M(), (torch.randn(2, 2),)) |
| |
| def test_ignore_class(self): |
| @torch.jit.ignore |
| class MyScriptClass(object): |
| def unscriptable(self): |
| return "a" + 200 |
| |
| |
| class TestModule(torch.nn.Module): |
| def __init__(self): |
| super(TestModule, self).__init__() |
| |
| def forward(self, x): |
| return MyScriptClass() |
| |
| with self.assertRaisesRegex(torch.jit.frontend.FrontendError, "Cannot instantiate class"): |
| t = torch.jit.script(TestModule()) |
| |
| def test_method_call(self): |
| class M(nn.Module): |
| def test(self, x): |
| return x |
| |
| def forward(self, z): |
| y = self.test(z) |
| return z + 20 + y |
| |
| self.checkModule(M(), (torch.randn(2, 2),)) |
| |
| def test_module_repr(self): |
| class Submodule(nn.Module): |
| def forward(self, x): |
| return x |
| |
| class MyModule(nn.Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| self.conv = nn.Conv2d(10, 10, 3) |
| self.lin = nn.Linear(10, 10) |
| self.sub = Submodule() |
| |
| def forward(self, x): |
| return self.lin(x) + self.sub(x) + self.conv(x) |
| |
| m = torch.jit.script(MyModule()) |
| |
| with self.capture_stdout() as out: |
| print(m) |
| |
| f = FileCheck() |
| f.check('MyModule') |
| f.check('Conv2d') |
| f.check('Linear') |
| f.check('Submodule') |
| f.run(out[0]) |
| |
| self.assertEqual(m.original_name, 'MyModule') |
| |
| def test_dir(self): |
| def test_module_dir(mod): |
| dir_set = dir(mod) |
| scripted_mod = torch.jit.script(mod) |
| dir_scripted = set(dir(scripted_mod)) |
| # set not currently copied over |
| ignore_set = ["training", "__delitem__", "__setitem__", "clear", "items", |
| "keys", "pop", "update", "values"] |
| for attr in dir_set: |
| if attr in ignore_set: |
| continue |
| self.assertTrue(attr in dir_scripted, attr) |
| |
| class MyModule(nn.Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| self.conv = nn.Conv2d(10, 10, 3) |
| self.lin = nn.Linear(10, 10) |
| |
| def forward(self, x): |
| return self.lin(x) + self.conv(x) |
| |
| test_module_dir(MyModule()) |
| |
| # test custom __dir__ for containers |
| conv = nn.Conv2d(10, 10, 3) |
| linear = nn.Linear(10, 10) |
| |
| test_module_dir(nn.Sequential(conv, linear)) |
| test_module_dir(nn.ModuleDict(OrderedDict([("conv", conv), ("linear", linear)]))) |
| |
| def test_class_compile(self): |
| def other_fn(a, b): |
| # type: (int, Tensor) -> Tensor |
| return a * b |
| |
| class B(object): |
| def __init__(self, x): |
| self.x = 2 |
| |
| def helper(self, a): |
| return self.x + a + other_fn(self.x, a) |
| |
| |
| class N(torch.nn.Module): |
| def __init__(self): |
| super(N, self).__init__() |
| |
| def forward(self, x): |
| b = B(x) |
| return b.helper(x) |
| |
| self.checkModule(N(), (torch.randn(2, 2),)) |
| |
| def test_error_stack(self): |
| def d(x): |
| # type: (int) -> int |
| return x + 10 |
| |
| def c(x): |
| return d("hello") + d(x) |
| |
| def b(x): |
| return c(x) |
| |
| def a(x): |
| return b(x) |
| |
| try: |
| scripted = torch.jit.script(a) |
| except RuntimeError as e: |
| checker = FileCheck() |
| checker.check("Expected a value of type 'int'") |
| checker.check("def c(x)") |
| checker.check("def b(x)") |
| checker.check("def a(x)") |
| checker.run(str(e)) |
| |
| def test_error_stack_module(self): |
| def d(x): |
| # type: (int) -> int |
| return x + 10 |
| |
| def c(x): |
| return d("hello") + d(x) |
| |
| def b(x): |
| return c(x) |
| |
| class Submodule(torch.nn.Module): |
| def __init__(self): |
| super(Submodule, self).__init__() |
| |
| def forward(self, x): |
| return b(x) |
| |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.submodule = Submodule() |
| |
| def some_method(self, y): |
| return y + self.submodule(y) |
| |
| def forward(self, x): |
| return self.some_method(x) |
| |
| try: |
| scripted = torch.jit.script(M()) |
| except RuntimeError as e: |
| checker = FileCheck() |
| checker.check("Expected a value of type 'int'") |
| checker.check("'c' is being compiled since it was called from 'b'") |
| checker.check("'b' is being compiled since it was called from") |
| checker.run(str(e)) |
| |
| @_tmp_donotuse_dont_inline_everything |
| def test_script_basic(self): |
| def a_python_fn(a, b, c): |
| return a + b + c |
| |
| @torch.jit.script |
| def a_script_fn(d, e, f): |
| return a_python_fn(d, e, f) |
| |
| graph = str(a_script_fn.graph) |
| FileCheck().check("prim::CallFunction").run(graph) |
| FileCheck().check_not("^a_python_fn").run(graph) |
| t = torch.ones(2, 2) |
| self.assertEqual(a_script_fn(t, t, t), t + t + t) |
| |
| def test_error_stack_class(self): |
| class X(object): |
| def bad_fn(self): |
| import pdb # noqa |
| |
| def fn(x) -> X: |
| return X(10) |
| |
| try: |
| torch.jit.script(fn) |
| except Exception as e: |
| checker = FileCheck() |
| checker.check("import statements") |
| checker.check("is being compiled since it was called from") |
| checker.run(str(e)) |
| |
| def test_error_stack_annotation(self): |
| class X(object): |
| def bad_fn(self): |
| import pdb # noqa |
| |
| def fn(x) -> X: |
| return X(10) |
| |
| try: |
| torch.jit.script(fn) |
| except Exception as e: |
| checker = FileCheck() |
| checker.check("import statements") |
| checker.check("is being compiled since it was called from") |
| checker.check("-> X") |
| checker.run(str(e)) |
| |
| def test_module_basic(self): |
| class Other(torch.nn.Module): |
| __constants__ = ['x'] |
| |
| def __init__(self, x): |
| super(Other, self).__init__() |
| self.x = x |
| self.param = torch.nn.Parameter(torch.ones(2, 2)) |
| |
| def some_unscriptable_method(self): |
| a = 2 |
| a = [2] |
| return a |
| |
| def forward(self, t): |
| return t + self.x + self.param |
| |
| |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.other = Other(200) |
| |
| def forward(self, t): |
| return self.other(t) * 2 |
| |
| self.checkModule(M(), (torch.ones(2, 2),)) |
| |
| def test_module_function_export(self): |
| class Other(torch.nn.Module): |
| __constants__ = ['x'] |
| |
| def __init__(self, x): |
| super(Other, self).__init__() |
| self.x = x |
| self.param = torch.nn.Parameter(torch.ones(2, 2)) |
| |
| @torch.jit.export |
| def some_entry_point(self, y): |
| return y + 20 |
| |
| def forward(self, t): |
| return t + self.x + self.param |
| |
| |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.other = Other(200) |
| |
| def forward(self, t): |
| return self.other(t) * 2 |
| |
| self.checkModule(M(), (torch.ones(2, 2),)) |
| |
| def test_iterable_modules(self): |
| class Inner(torch.nn.Module): |
| def forward(self, x): |
| return x + 10 |
| |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.sequential = nn.Sequential( |
| Inner(), |
| Inner(), |
| nn.Sequential(Inner(), Inner()) |
| ) |
| self.module_list = nn.ModuleList([Inner(), Inner()]) |
| |
| def forward(self, x): |
| for mod in self.module_list: |
| x += mod(x) |
| x += self.sequential(x) |
| return x |
| |
| self.checkModule(M(), (torch.randn(5, 5),)) |
| |
| def test_attributes(self): |
| @torch.jit.script |
| class Inner2(object): |
| def __init__(self): |
| self.b = "a string" |
| |
| @torch.jit.script |
| class Foo(object): |
| def __init__(self): |
| self.a = 4 |
| self.inner = Inner2() |
| |
| @torch.jit.script |
| class SFoo(object): |
| def __init__(self): |
| self.a = 4 |
| self.inner = Inner2() |
| |
| def __setstate__(self, obj): |
| # type: (Tuple[int, Inner2]) -> None |
| a, inner = obj |
| self.a = a |
| self.inner = inner |
| |
| def __getstate__(self): |
| return (self.a, self.inner) |
| |
| |
| untyped_values = ( |
| ('my_dict', {"I": "am", "a test": "test"}), |
| ('my_float', 2.3), |
| ('my_int', 99), |
| ('my_bool', False), |
| ('my_tuple', (1, 2, 3, 4)), |
| ('my_list', [(1, 2), (3, 4)]), |
| # ('my_tensor', torch.randn(2, 2)), |
| ('my_int_list', [1, 2, 3, 4]), |
| # ('my_tensor_list', [torch.ones(2, 2) + i for i in range(4)]), |
| ('my_bool_list', [True, True, False, True]), |
| ('my_float_list', [1., 2., 3., 4.]), |
| ('my_str_list', ['hello', 'bye']), |
| ) |
| typed_values = ( |
| ('my_empty_list', []), |
| ('my_empty_dict', {}), |
| ('my_none', None), |
| ('my_object', Foo()), |
| ('my_object2', SFoo()), |
| ) |
| |
| class M(torch.nn.Module): |
| # TODO: re-enable this once this test is in a Python 3-only syntax |
| # file |
| # my_empty_list : List[int] |
| # my_empty_dict : Dict[str, int] |
| # my_none : Optional[int] |
| |
| def __init__(self): |
| super(M, self).__init__() |
| |
| def forward(self, x): |
| return ( |
| self.my_dict, |
| self.my_float, |
| self.my_int, |
| self.my_bool, |
| # self.my_tensor, |
| self.my_int_list, |
| # self.my_tensor_list, |
| self.my_bool_list, |
| self.my_float_list, |
| self.my_str_list, |
| self.my_empty_list, |
| self.my_empty_dict, |
| self.my_none, |
| self.my_object.a, |
| self.my_object.inner.b, |
| self.my_object.a, |
| self.my_object2.inner.b, |
| ) |
| |
| # TODO: as a followup, fix this test |
| # We can't define class attributes like we should be doing: |
| # class M(torch.nn.Module): |
| # my_empty_list : List[int] |
| # my_empty_dict : Dict[str, int] |
| # my_none : Optional[int] |
| # my_out_of_line_attribute: List[int] = [1, 2, 3] |
| # since there's no string frontend for Python classes (so the `define`) |
| # trick doesn't work. |
| M.__annotations__ = { |
| 'my_empty_list': List[int], |
| 'my_empty_dict': Dict[str, int], |
| 'my_none': Optional[int], |
| 'my_object': Foo, |
| 'my_object2': SFoo, |
| } |
| |
| m = M() |
| for name, value in untyped_values + typed_values: |
| setattr(m, name, value) |
| |
| self.checkModule(m, (torch.randn(5, 5),)) |
| |
| def test_function_attribute_in_submodule(self): |
| class N(nn.Module): |
| def __init__(self, norm): |
| super(N, self).__init__() |
| self.activation = torch.nn.functional.relu |
| self.norm = norm |
| |
| def forward(self, src): |
| output = src |
| output = self.norm(output) |
| return output |
| |
| class M(nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| encoder_norm = nn.ReLU() |
| self.encoder = N(encoder_norm) |
| |
| def forward(self, x): |
| return self.encoder(x) |
| |
| m = M() |
| self.checkModule(m, (torch.randn(5, 5), )) |
| |
| def test_property(self): |
| class M(nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.x = 0 |
| |
| @property |
| def x_and_1(self): |
| return self.x + 1 |
| |
| def forward(self, new_x): |
| # type: (int) -> int |
| self.x = new_x |
| return self.x_and_1 |
| |
| with self.assertRaisesRegex(RuntimeError, "property"): |
| torch.jit.script(M()) |
| |
| def test_inner_traced_module(self): |
| class Dummy(nn.Module): |
| def forward(self, x): |
| return x |
| |
| class Model(nn.Module): |
| def __init__(self, dummies): |
| super(Model, self).__init__() |
| self._dummies = dummies |
| |
| def forward(self, x): |
| out = [] |
| for dummy in self._dummies: |
| out.append(dummy(x)) |
| return out |
| |
| dummy = torch.jit.trace(Dummy(), torch.randn(1, 2)) |
| dummies = nn.ModuleList([dummy]) |
| model = Model(dummies) |
| self.checkModule(model, (torch.rand(5, 5), )) |
| |
| def test_script_loaded_module(self): |
| """ |
| Test that we can hold a loaded ScriptModule as a submodule. |
| """ |
| class Dummy(nn.Module): |
| def forward(self, x): |
| return x |
| |
| dummy = torch.jit.script(Dummy()) |
| dummy = self.getExportImportCopy(dummy) |
| |
| class ContainsLoaded(torch.nn.Module): |
| def __init__(self): |
| super(ContainsLoaded, self).__init__() |
| self.encoder = dummy |
| |
| def forward(self, input): |
| return self.encoder(input) |
| |
| self.checkModule(ContainsLoaded(), (torch.rand(2, 3), )) |
| |
| def test_optional_module(self): |
| class Dummy(nn.Module): |
| def __init__(self): |
| super(Dummy, self).__init__() |
| self.foo = nn.Linear(2, 2) |
| |
| def forward(self, x): |
| if self.foo is not None: |
| return self.foo(x) |
| return x |
| |
| mod = Dummy() |
| self.checkModule(mod, (torch.rand(2, 2),)) |
| mod.foo = None |
| self.checkModule(mod, (torch.rand(2, 2),)) |
