xref: /aosp_15_r20/external/pytorch/torch/random.py (revision da0073e96a02ea20f0ac840b70461e3646d07c45) 
1# mypy: allow-untyped-defs
2import contextlib
3import warnings
4from typing import Generator
5
6import torch
7from torch._C import default_generator
8
9
10def set_rng_state(new_state: torch.Tensor) -> None:
11    r"""Sets the random number generator state.
12
13    .. note:: This function only works for CPU. For CUDA, please use
14        :func:`torch.manual_seed`, which works for both CPU and CUDA.
15
16    Args:
17        new_state (torch.ByteTensor): The desired state
18    """
19    default_generator.set_state(new_state)
20
21
22def get_rng_state() -> torch.Tensor:
23    r"""Returns the random number generator state as a `torch.ByteTensor`.
24
25    .. note:: The returned state is for the default generator on CPU only.
26
27    See also: :func:`torch.random.fork_rng`.
28    """
29    return default_generator.get_state()
30
31
32def manual_seed(seed) -> torch._C.Generator:
33    r"""Sets the seed for generating random numbers on all devices. Returns a
34    `torch.Generator` object.
35
36    Args:
37        seed (int): The desired seed. Value must be within the inclusive range
38            `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError
39            is raised. Negative inputs are remapped to positive values with the formula
40            `0xffff_ffff_ffff_ffff + seed`.
41    """
42    seed = int(seed)
43    import torch.cuda
44
45    if not torch.cuda._is_in_bad_fork():
46        torch.cuda.manual_seed_all(seed)
47
48    import torch.mps
49
50    if not torch.mps._is_in_bad_fork():
51        torch.mps.manual_seed(seed)
52
53    import torch.xpu
54
55    if not torch.xpu._is_in_bad_fork():
56        torch.xpu.manual_seed_all(seed)
57
58    _seed_custom_device(seed)
59
60    return default_generator.manual_seed(seed)
61
62
63def seed() -> int:
64    r"""Sets the seed for generating random numbers to a non-deterministic
65    random number on all devices. Returns a 64 bit number used to seed the RNG.
66    """
67    seed = default_generator.seed()
68    import torch.cuda
69
70    if not torch.cuda._is_in_bad_fork():
71        torch.cuda.manual_seed_all(seed)
72
73    import torch.mps
74
75    if not torch.mps._is_in_bad_fork():
76        torch.mps.manual_seed(seed)
77
78    import torch.xpu
79
80    if not torch.xpu._is_in_bad_fork():
81        torch.xpu.manual_seed_all(seed)
82
83    _seed_custom_device(seed)
84
85    return seed
86
87
88def _seed_custom_device(seed) -> None:
89    r"""Sets the seed to generate random numbers for custom device.
90
91    Args:
92        seed (int): The desired seed.
93
94    See [Note: support the custom device with privateuse1]
95    """
96    seed = int(seed)
97    custom_backend_name = torch._C._get_privateuse1_backend_name()
98    if hasattr(torch, custom_backend_name):
99        custom_device_mod = getattr(torch, custom_backend_name)
100        _bad_fork_name = "_is_in_bad_fork"
101        _seed_all_name = "manual_seed_all"
102        if hasattr(custom_device_mod, _bad_fork_name) and hasattr(
103            custom_device_mod, _seed_all_name
104        ):
105            if not getattr(custom_device_mod, _bad_fork_name)():
106                getattr(custom_device_mod, _seed_all_name)(seed)
107        else:
108            message = f"Set seed for `{custom_backend_name}` device does not take effect, please add API's "
109            message += f"`{_bad_fork_name}` and `{_seed_all_name}` to `{custom_backend_name}` device module."
110            warnings.warn(message, UserWarning, stacklevel=3)
111
112
113def initial_seed() -> int:
114    r"""Returns the initial seed for generating random numbers as a
115    Python `long`.
116
117    .. note:: The returned seed is for the default generator on CPU only.
118    """
119    return default_generator.initial_seed()
120
121
122_fork_rng_warned_already = False
123
124
125@contextlib.contextmanager
126def fork_rng(
127    devices=None,
128    enabled=True,
129    _caller="fork_rng",
130    _devices_kw="devices",
131    device_type="cuda",
132) -> Generator:
133    """
134    Forks the RNG, so that when you return, the RNG is reset
135    to the state that it was previously in.
136
137    Args:
138        devices (iterable of Device IDs): devices for which to fork
139            the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates
140            on all devices, but will emit a warning if your machine has a lot
141            of devices, since this function will run very slowly in that case.
142            If you explicitly specify devices, this warning will be suppressed
143        enabled (bool): if ``False``, the RNG is not forked.  This is a convenience
144            argument for easily disabling the context manager without having
145            to delete it and unindent your Python code under it.
146        device_type (str): device type str, default is `cuda`. As for custom device,
147            see details in [Note: support the custom device with privateuse1]
148    """
149
150    device_type = torch.device(device_type).type
151    device_mod = getattr(torch, device_type, None)
152    if device_mod is None:
153        raise RuntimeError(
154            f"torch has no module of `{device_type}`, you should register "
155            + "a module by `torch._register_device_module`."
156        )
157    global _fork_rng_warned_already
158
159    # Internal arguments:
160    #   _caller: the function which called fork_rng, which the user used
161    #   _devices_kw: the devices keyword of _caller
162
163    if not enabled:
164        yield
165        return
166
167    if devices is None:
168        num_devices = device_mod.device_count()
169        if num_devices > 1 and not _fork_rng_warned_already:
170            message = (
171                f"{device_type.upper()} reports that you have {num_devices} available devices, and "
172                f"you have used {_caller} without explicitly specifying which devices are being used. "
173                f"For safety, we initialize *every* {device_type.upper()} device by default, which can "
174                f"be quite slow if you have a lot of {device_type.upper()}s. If you know that you are only"
175                f" making use of a few {device_type.upper()} devices, set the environment variable "
176                f"{device_type.upper()}_VISIBLE_DEVICES or the '{_devices_kw}' keyword argument of {_caller} "
177                "with the set of devices you are actually using. For example, if you are using CPU only, "
178                "set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, "
179                f"set {device_type.upper()}_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices "
180                f"and suppress this warning, set the '{_devices_kw}' keyword argument to "
181                f"`range(torch.{device_type}.device_count())`."
182            )
183            warnings.warn(message)
184            _fork_rng_warned_already = True
185        devices = list(range(num_devices))
186    else:
187        # Protect against user passing us a generator; we need to traverse this
188        # multiple times but a generator will be exhausted upon first traversal
189        devices = list(devices)
190
191    cpu_rng_state = torch.get_rng_state()
192    device_rng_states = [device_mod.get_rng_state(device) for device in devices]
193
194    try:
195        yield
196    finally:
197        torch.set_rng_state(cpu_rng_state)
198        for device, device_rng_state in zip(devices, device_rng_states):
199            device_mod.set_rng_state(device_rng_state, device)
200