[ndgrigorian]

This PR is the first of a series splitting #3147 into smaller pieces. It adds the new framework for device-side random number generation and NumPy's Generator API, with only the base distributions

The new dpnp.libtensor._rng_impl extension samples on the device using the oneMKL device RNG API (header-only, so no oneMKL RNG library is linked). A bit generator keeps its state on the host, and each kernel launch rebuilds its engines from that state, so sampling never waits on the device and the values depend only on the seed and the output size, not on the device

Adds

• dpnp.random.Generator with random, uniform, standard_normal, normal and spawn, and default_rng
• dpnp.random.BitGenerator (with random_raw, spawn, state and lock), SeedSequence (NumPy's) and the Philox4x3210 bit generator on oneMKL's device philox4x32x10
• Strided and blocked sample layouts; the blocked layout generates the words of a work-item in one engine call and is used on GPUs, giving the same values

The legacy RandomState and the module-level functions are untouched until later in the series

Series:

1. Add dpnp.random.Generator on the oneMKL device RNG API #3157 (this PR): framework, Philox4x3210 and the base distributions
2. Add continuous distributions to dpnp.random.Generator #3158: Continuous distributions
3. Add discrete distributions to dpnp.random.Generator #3159: Discrete distributions
4. Add multivariate distributions and permutations to dpnp.random.Generator #3160: Multivariate distributions and permutations
5. Add PCG64DXSM and MRG32k3a bit generators to dpnp.random #3161: PCG64DXSM and MRG32k3a bit generators
6. Reimplement dpnp.random.RandomState on the device RNG #3162: RandomState and the module functions on the device RNG
7. Remove the legacy backend RNG #3163: Removal of the legacy backend RNG

• Have you provided a meaningful PR description?
• Have you added a test, reproducer or referred to an issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
• Have you checked performance impact of proposed changes?
• Have you added documentation for your changes, if necessary?
• Have you added your changes to the changelog?

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/3157/index.html

[github-actions]

Array API standard conformance tests for dpnp=0.22.0dev0=np2py314h8d9cdd5_27 ran successfully.
Passed: 1491
Failed: 0
Skipped: 6

[coveralls]

coverage: 84.209% (-0.2%) from 84.396% — feature/device-rng-generator into master