Lines Matching full:generator

22     # torch.jit.trace does not properly capture the generator manual seed
23     # and thus is non deterministic even if the generator is manually seeded
24     @skipIfLegacyJitExecutor("legacy JIT executor does not support Generator type")
28             generator = torch.Generator()
29             generator.seed()
30             generator.manual_seed(2023)
31             generator.initial_seed()
33             tensor.uniform_(0, 1, generator=generator)
38         # Run this 3 times to ensure that the generator is being manually seeded
45             # Change the seed of the default generator to
46             # check that we're using the generator from the
55             generator = torch.Generator()
56             generator.seed()
57             generator.manual_seed(2023)
58             generator.initial_seed()
60             tensor.normal_(-1.0, 1.0, generator=generator)
65         # Run this 3 times to ensure that the generator is being manually seeded
72             # Change the seed of the default generator to
73             # check that we're using the generator from the
83             # check that calling manual seed for the default generator works
101         def f(generator: torch.Generator):  argument
103             tensor.normal_(-1.0, 1.0, generator=generator)
106         generator = torch.Generator()
107         generator.manual_seed(2023)
109         script_f = torch.jit.script(f, (generator,))
112             generator = torch.Generator()
113             generator.manual_seed(2023 + i)
117             eager_tensor = f(generator)
119             generator = torch.Generator()
120             generator.manual_seed(2023 + i)
124             script_tensor = script_f(generator)
137             def reset_linear(self, module, generator):  argument
139                     module.weight, a=math.sqrt(5), generator=generator
143                 generator = torch.Generator()
144                 generator.manual_seed(1)
145                 self.reset_linear(self.foo, generator)
147                 generator = torch.Generator()
148                 generator.manual_seed(2)
149                 self.reset_linear(self.bar, generator)
155                 generator = torch.Generator()
156                 generator.manual_seed(3)
158                 r.normal_(0.0, 1.0, generator=generator)
175             # Run this 3 times so make sure that the generator seed is being set