o i@sddlZddlZddlmZddlZddlmZdejddfddZdejfdd Z dej jfd d Z dej jfd d Z de fddZdddZde fddZdaej     ddefddZdS)N) Generator)default_generator new_statereturncCst|dS)zSets the random number generator state. .. note:: This function only works for CPU. For CUDA, please use :func:`torch.manual_seed`, which works for both CPU and CUDA. Args: new_state (torch.ByteTensor): The desired state N)r set_state)rrN/sda-disk/www/egybert/egybert_env/lib/python3.10/site-packages/torch/random.py set_rng_state s r cCtS)zReturns the random number generator state as a `torch.ByteTensor`. .. note:: The returned state is for the default generator on CPU only. See also: :func:`torch.random.fork_rng`. )r get_staterrrr get_rng_statesr cCst|S)aSets the seed for generating random numbers on all devices. Returns a `torch.Generator` object. Args: seed (int): The desired seed. Value must be within the inclusive range `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError is raised. Negative inputs are remapped to positive values with the formula `0xffff_ffff_ffff_ffff + seed`. )_manual_seed_impl)seedrrr manual_seed s rcCstt|}ddl}|js|j|ddl}|js"|j|ddl}|j s1|j |t |t |S)Nr) int torch.cudacuda_is_in_bad_forkmanual_seed_all torch.mpsmpsr torch.xpuxpu_seed_custom_devicerrtorchrrrr -s       r cCsnt}ddl}|js|j|ddl}|js"|j|ddl }|j s1|j |t ||S)zSets the seed for generating random numbers to a non-deterministic random number on all devices. Returns a 64 bit number used to seed the RNG. rN) rrrrrrrrrrrrrrrrrCs      rcCst|}tj}tt|rPtt|}d}d}t||r2t||r2t||s0t|||d Sd Sd|d}|d|d|d|d7}tj|td d d Sd S) zSets the seed to generate random numbers for custom device. Args: seed (int): The desired seed. See [Note: support the custom device with privateuse1] rrzSet seed for `z0` device does not take effect, please add API's `z` and `z` to `z` device module. stacklevelN) rr_C_get_privateuse1_backend_namehasattrgetattrwarningswarn UserWarning)rcustom_backend_namecustom_device_mod_bad_fork_name_seed_all_namemessagerrrr\s       rcCr )zReturns the initial seed for generating random numbers as a Python `long`. .. note:: The returned seed is for the default generator on CPU only. )r initial_seedrrrrr,usr,FTfork_rngdevicesrc #s||dkr dVdSt|j}tt|ddur$td|dd|s+dVdS|dur{}|dkrttst|d|d|d |d |d |d |d |d|d|d|d|d}tj |dddat t |}nt |}t }fdd|D}zdVWt |t||D] \} } | | qdSt |t||D] \} } | | qw)az Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in. Args: devices (iterable of Device IDs): devices for which to fork the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressed enabled (bool): if ``False``, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it. device_type (str): device type str, default is `cuda`. As for supported device, see details in :ref:`accelerator` metaNztorch has no module of `z`, you should register z,a module by `torch._register_device_module`.z reports that you have z& available devices, and you have used z_ without explicitly specifying which devices are being used. For safety, we initialize *every* zA device by default, which can be quite slow if you have a lot of z5s. If you know that you are only making use of a few z' devices, set the environment variable z_VISIBLE_DEVICES or the 'z' keyword argument of z with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set zb_VISIBLE_DEVICES=0 or devices=[0]. To initialize all devices and suppress this warning, set the 'z#' keyword argument to `range(torch.z.device_count())`.rTcsg|]}|qSr)r ).0device device_modrr szfork_rng..)rr3typer# RuntimeError device_count_fork_rng_warned_alreadyupperr$r%listranger r zip) r.enabled_caller _devices_kw device_type num_devicesr+ cpu_rng_statedevice_rng_statesr3device_rng_staterr4rr-sp          )rN)NTr-r.r) contextlibr$collections.abcrrtorch._CrTensorr r r rr rrrr,r:contextmanagerr-rrrrs,