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warp.rand_init#

warp.rand_init(seed: int32) → uint32#

  • Kernel

  • Python

Initialize a random number generator (RNG) state from a seed.

Warp’s RNG is a stateless PCG hash (Jarzynski & Olano, 2020): rand_init turns a seed into a 32-bit state that is passed to the rand* and sample_* built-ins. In a kernel, these built-ins advance state in place, so repeated calls on the same local variable return different values. When called directly from the Python scope, they do not modify state: calling wp.randf(state) twice with the same state returns the same value both times. Results are deterministic and reproducible for a given seed and call order on every device.

state is just a local value, not a persistent generator object: it lives only for the duration of the kernel invocation and does not carry over between launches. Re-launching with the same seed and per-thread offsets reproduces the same sequences. To draw different sequences across launches, change the seed or include a launch-specific value in the rand_init(seed, offset) offset. See Avoiding Correlated Sequences for examples.

For parallel kernels, prefer the rand_init(seed, offset) overload with a unique per-thread offset so each thread draws a distinct sequence. See the Random Number Generation user guide section for more details.

Parameters:

seed – Seed value used to derive the initial state.

Returns:

A 32-bit unsigned integer holding the initial RNG state.

warp.rand_init(seed: int32, offset: int32) → uint32

  • Kernel

  • Python

Initialize a random number generator (RNG) state from a seed and an offset.

Both seed and offset are hashed into the returned state. This is the recommended constructor for parallel kernels: share seed across the launch and pass a unique per-thread offset (typically wp.tid()) so each thread starts from a distinct RNG state and avoids sharing the same sequence:

@wp.kernel
def sample_kernel(seed: int, out: wp.array[float]):
    tid = wp.tid()
    rng = wp.rand_init(seed, tid)
    out[tid] = wp.randf(rng)

See the Random Number Generation user guide section for the RNG model and guidance on avoiding correlated sequences.

Parameters:

  • seed – Seed shared across a kernel launch.

  • offset – Per-thread offset selecting a distinct sequence.

Returns:

A 32-bit unsigned integer holding the initial RNG state.