diff --git a/CHANGELOG.md b/CHANGELOG.md index d7df4c2ab55..911f08be516 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 * Added the multivariate distributions, `permutation`, `shuffle`, `permuted`, `choice` and `bytes` to `dpnp.random.Generator` [#3160](https://github.com/IntelPython/dpnp/pull/3160) * Added the `dpnp.random.PCG64DXSM` bit generator, which matches `numpy.random.PCG64DXSM` for raw values, `random`, `uniform` and `bytes` [#3161](https://github.com/IntelPython/dpnp/pull/3161) * Added the `dpnp.random.MRG32k3a` bit generator [#3161](https://github.com/IntelPython/dpnp/pull/3161) +* Added `dpnp.random.get_state`, `dpnp.random.set_state`, `dpnp.random.get_bit_generator` and `dpnp.random.set_bit_generator`, and support for a bit generator as the seed of `dpnp.random.RandomState` [#3162](https://github.com/IntelPython/dpnp/pull/3162) ### Changed @@ -24,6 +25,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 * Merged the `ufunc` and `indexing` extensions into `dpnp.libtensor._tensor_elementwise_impl` and `dpnp.libtensor._tensor_impl`, while `statistics` and `window` extensions became part of libtensor [#3131](https://github.com/IntelPython/dpnp/pull/3131) * Moved `blas`, `fft`, `lapack`, `sparse` and `vm` extensions into `dpnp/libtensor/extensions` [#3131](https://github.com/IntelPython/dpnp/pull/3131) * Moved the libtensor headers shipped with dpnp from `dpnp/tensor/libtensor/include` to `dpnp/libtensor/include`, and changed `dpnp.get_include` to return that directory [#3131](https://github.com/IntelPython/dpnp/pull/3131) +* Reimplemented `dpnp.random.RandomState` and the `dpnp.random` functions on device kernels, giving the same values on every device. `device`, `usm_type` and `sycl_queue` are now keyword-only, `randint(dtype=int)` returns `int64` and `shuffle` requires a `dpnp.ndarray` [#3162](https://github.com/IntelPython/dpnp/pull/3162) ### Deprecated diff --git a/doc/reference/random.rst b/doc/reference/random.rst index 810a5ff7f5b..d528920f526 100644 --- a/doc/reference/random.rst +++ b/doc/reference/random.rst @@ -8,10 +8,9 @@ Random sampling (:mod:`dpnp.random`) DPNP draws random numbers on the SYCL device, by default with the counter-based Philox4x32x10 generator of the oneMKL device RNG API, or with NumPy's PCG64DXSM generator or the MRG32k3a generator of the oneMKL device -RNG API. It provides the -:class:`Generator` API of NumPy, recommended for new code. The legacy -:class:`RandomState` API and the module-level functions still use the -host-side oneMKL RNG. +RNG API. It provides both NumPy random APIs: the :class:`Generator` API recommended for new code, +and the legacy :class:`RandomState` API with the module-level functions +built on it. .. code-block:: python @@ -41,8 +40,8 @@ Differences from NumPy which defaults to the default floating-point type of the device. All methods take a ``usm_type`` keyword. * Parameters on the host (Python scalars, lists and NumPy arrays) are - checked as in NumPy. Parameters in :class:`dpnp.ndarray` are - not, since that would wait for the device. + checked as in NumPy. In :class:`Generator`, parameters in + :class:`dpnp.ndarray` are not, since that would wait for the device. Invalid ones give undefined values, such as NaN or the smallest ``int64``, but the kernels handle them safely: they never loop without end or access memory out of bounds. Floating-point arrays given for integer parameters @@ -58,9 +57,15 @@ Differences from NumPy * ``zipf`` follows the Zipf distribution truncated to the largest ``int64``. NumPy's rejection test rounds to accepting every sample above ``2**53``, which makes its tail too heavy for ``a`` close to 1. -* ``hypergeometric`` requires ``ngood`` and ``nbad`` below ``10**9``. -* :meth:`Generator.shuffle` only accepts a :class:`dpnp.ndarray`, as in - CuPy. +* :meth:`RandomState.randint` and :func:`randint` return ``int64`` values by + default, as do :meth:`RandomState.random_integers`, + :func:`random_integers` and :meth:`RandomState.tomaxint`, whose values + reach the largest ``int64``. NumPy returns a C ``long`` instead, which is + ``int32`` on Windows. +* ``hypergeometric`` requires ``ngood`` and ``nbad`` below ``10**9``, also in + :class:`RandomState`. +* :meth:`Generator.shuffle` and :func:`shuffle` only accept a + :class:`dpnp.ndarray`, as in CuPy. Random Generator ---------------- @@ -157,6 +162,24 @@ Legacy random generation .. hint:: `NumPy API Reference: Legacy random generation `_ +:class:`RandomState` keeps the parameter checks of NumPy's legacy one, also +for parameters in :class:`dpnp.ndarray`, as CuPy's does, so that a call with +such parameters waits for the device to check them. The module-level +functions use one :class:`RandomState` per SYCL queue: the +queue given by the ``device`` or ``sycl_queue`` keyword, or else the queue of +a :class:`dpnp.ndarray` parameter, or else the default queue. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + RandomState + seed + get_state + set_state + get_bit_generator + set_bit_generator + Simple random data ^^^^^^^^^^^^^^^^^^ @@ -227,15 +250,3 @@ Distributions wald weibull zipf - -Random generator -^^^^^^^^^^^^^^^^ - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - RandomState - seed - get_random_state - set_random_state diff --git a/dpnp/AGENTS.md b/dpnp/AGENTS.md index 8d2a8d977af..0aedf8e654a 100644 --- a/dpnp/AGENTS.md +++ b/dpnp/AGENTS.md @@ -6,7 +6,7 @@ - `dpnp_algo/dpnp_elementwise_common.py`: ufunc wrapper classes (`DPNPUnaryFunc`, `DPNPBinaryFunc`, two-output and specialised variants) - `dpnp_utils/`: shared helpers (`get_usm_allocations`, `map_dtype_to_device`, reduction wrappers) - `linalg/`, `fft/`, `random/`, `scipy/`, `lib/`: subpackages, each exporting through its own `__init__.py` -- `random/`: `_bit_generator.py` (`BitGenerator`, `Philox4x3210`, `PCG64DXSM`, `MRG32k3a`; `SeedSequence` is NumPy's) and `_generator.py` (`Generator`, which implements every distribution). The kernels are in `dpnp.libtensor._rng_impl`. +- `random/`: `_bit_generator.py` (`BitGenerator`, `Philox4x3210`, `PCG64DXSM`, `MRG32k3a`; `SeedSequence` is NumPy's), `_generator.py` (`Generator`, which implements every distribution), `dpnp_random_state.py` (`RandomState`, the same samplers with NumPy's legacy checks) and `dpnp_iface_random.py` (module functions, one `RandomState` per queue). The kernels are in `dpnp.libtensor._rng_impl`. - Legacy, don't extend: the Cython `dpnp_algo` path and `dpnp_backend_c` (still used by `dpnp.partition` and `dpnp.random`), and the NumPy fallback `call_origin`. New kernels go into `dpnp/libtensor` (see `dpnp/libtensor/AGENTS.md`). ## Adding a Public Function @@ -21,7 +21,7 @@ - Extension calls are ordered with `_manager = dpctl.utils.SequentialOrderManager[exec_q]`: pass `depends=_manager.submitted_events`, then `_manager.add_event_pair(ht_ev, ev)`. Don't add blocking waits. - Use `dpnp.default_float_type(...)` or `map_dtype_to_device(...)` for the default float type. - Deprecated ufuncs use `DPNPDeprecatedUnaryFunc(..., deprecated_msg=...)`. -- A `dpnp.random` distribution is a `Generator` method: it calls `self._check_queues(...)` on its array parameters (raises `ExecutionPlacementError`), checks host parameters as NumPy does, and draws through `_cont`, `_float_sample` or `_discrete_sample` into `BitGenerator._fill`. Parameters on the device are not checked, as the check waits for the device. Temporaries take the output's `usm_type`. +- A `dpnp.random` distribution is a `Generator` method: it calls `self._check_queues(...)` on its array parameters (raises `ExecutionPlacementError`), checks host parameters as NumPy does, and draws through `_cont`, `_float_sample` or `_discrete_sample` into `BitGenerator._fill`. `RandomState` and the module function delegate to it. Parameters on the device are checked only by `RandomState` (`_check_device`), as the check waits for the device. Temporaries take the output's `usm_type`. - Unsupported NumPy keywords raise `NotImplementedError` (`dpnp.check_limitations`) and are listed under `Limitations` in the docstring. - The Array API spec version is set in three places that change together: `__array_api_version__` in `dpnp/tensor/_array_api.py`, `ARRAY_API_TESTS_VERSION` in `.github/workflows/conda-package.yml` and `doc/reference/array_api.rst` (plus the version test in `dpnp/tests/test_ndarray.py`). Inspection utilities come from `__array_namespace_info__` in `dpnp_array_api_info.py`. - `dpnp.tensor` replaces `dpctl.tensor`, which no longer exists here; `SyclQueue`, `SyclDevice`, `dpctl.memory` and `SequentialOrderManager` still come from `dpctl`. diff --git a/dpnp/random/__init__.py b/dpnp/random/__init__.py index dbf86094885..90170ba616a 100644 --- a/dpnp/random/__init__.py +++ b/dpnp/random/__init__.py @@ -45,6 +45,8 @@ f, gamma, geometric, + get_bit_generator, + get_state, gumbel, hypergeometric, laplace, @@ -71,6 +73,8 @@ rayleigh, sample, seed, + set_bit_generator, + set_state, shuffle, standard_cauchy, standard_exponential, @@ -104,6 +108,8 @@ "f", "gamma", "geometric", + "get_bit_generator", + "get_state", "gumbel", "hypergeometric", "laplace", @@ -131,6 +137,8 @@ "sample", "shuffle", "seed", + "set_bit_generator", + "set_state", "standard_cauchy", "standard_exponential", "standard_gamma", diff --git a/dpnp/random/_generator.py b/dpnp/random/_generator.py index 4978d5ad53a..d96ddfd74e1 100644 --- a/dpnp/random/_generator.py +++ b/dpnp/random/_generator.py @@ -84,6 +84,7 @@ _CONS_BOUNDED_LT_0_1 = 6 _CONS_GT_1 = 7 _CONS_POISSON = 8 +_CONS_GTE_1 = 9 _POISSON_LAM_MAX = _INT64_MAX - numpy.sqrt(_INT64_MAX) * 10 _HYPERGEOM_MAX = 10**9 @@ -175,79 +176,125 @@ def _check_real(value): raise TypeError(f"must be real number, not {t.__name__}") -def _uniform_range(low, high, overflow_msg): +def _uniform_range(low, high, overflow_msg, device=False): """ ``high - low``, raising OverflowError with `overflow_msg` unless it is - finite, or ``None`` when either param is on the device. + finite, or ``None`` when either param is on the device, unless `device` + is true. """ - xp, (flow, fhigh) = _check_arrays((low, high), numpy.float64) + xp, (flow, fhigh) = _check_arrays((low, high), device, numpy.float64) if xp is None: return None - with numpy.errstate(over="ignore", invalid="ignore"): - rng = numpy.subtract(fhigh, flow) - if _is_scalar_path(low, high): - _check_real(low) - _check_real(high) - if not numpy.all(numpy.isfinite(rng)): + if xp is dpnp: + rng = dpnp.subtract(fhigh, flow) + else: + with numpy.errstate(over="ignore", invalid="ignore"): + rng = numpy.subtract(fhigh, flow) + if _is_scalar_path(low, high): + _check_real(low) + _check_real(high) + if not xp.all(xp.isfinite(rng)): raise OverflowError(overflow_msg) return rng -def _check_arrays(params, dtype=None): +def _on_device(value): + """ + `value` for a check on the device, as a floating-point array unless it + already is one. + + """ + + value = dpnp.asarray(value) + if dpnp.issubdtype(value.dtype, dpnp.inexact): + return value + return value.astype(dpnp.default_float_type(sycl_queue=value.sycl_queue)) + + +def _check_arrays(params, device, dtype=None): """ - `params` as arrays of `dtype` to check, with the module to check them - by, which is ``None`` when some are on the device, as the check would - wait for the device. + `params` as arrays to check, with the module to check them by, which is + ``None`` when some are on the device and `device` is false. + + Host parameters are converted to `dtype`, and moved to the device when + others are on the device. """ - if any(dpnp.is_supported_array_type(p) for p in params): + queues = [p.sycl_queue for p in params if dpnp.is_supported_array_type(p)] + host = [ + None if dpnp.is_supported_array_type(p) else numpy.asarray(p, dtype) + for p in params + ] + if not queues: + return numpy, host + if not device: return None, [None] * len(params) - return numpy, [numpy.asarray(p, dtype) for p in params] + + def _to_device(p, h): + if h is None: + return dpnp.asarray(p) + if ( + h.dtype == numpy.float64 + and not queues[0].sycl_device.has_aspect_fp64 + ): + h = h.astype(numpy.float32) + return dpnp.asarray(h, sycl_queue=queues[0]) + + return dpnp, [_to_device(p, h) for p, h in zip(params, host)] # pylint: disable=too-many-branches -def _check_constraint(value, name, cons, scalar): +def _check_constraint(value, name, cons, scalar, device=False): """ Raise ValueError, as NumPy does, when the parameter `value` violates the constraint `cons`. - Parameters on the device are not checked, as the check waits for the - device. `scalar` selects the messages of NumPy's scalar path. + Parameters on the device are checked only with `device`, as the check + waits for the device. `scalar` selects the messages of NumPy's scalar + path. """ if dpnp.is_supported_array_type(value): - return - val = numpy.asarray(value, dtype=numpy.float64) - if scalar: - _check_real(value) + if not device: + return + xp = dpnp + val = _on_device(value) + else: + xp = numpy + val = numpy.asarray(value, dtype=numpy.float64) + if scalar: + _check_real(value) if cons == _CONS_NONE: return nan_msg = "is NaN" if scalar else "contains NaNs" if cons == _CONS_NON_NEGATIVE: - if numpy.any(~numpy.isnan(val) & numpy.signbit(val)): + if xp.any(~xp.isnan(val) & xp.signbit(val)): raise ValueError(f"{name} < 0") elif cons in (_CONS_POSITIVE, _CONS_POSITIVE_NOT_NAN): - if cons == _CONS_POSITIVE_NOT_NAN and numpy.any(numpy.isnan(val)): + if cons == _CONS_POSITIVE_NOT_NAN and xp.any(xp.isnan(val)): raise ValueError(f"{name} must not be NaN") - if numpy.any(val <= 0): + if xp.any(val <= 0): raise ValueError(f"{name} <= 0") elif cons == _CONS_BOUNDED_0_1: - if not numpy.all((val >= 0) & (val <= 1)): + if not xp.all((val >= 0) & (val <= 1)): raise ValueError(f"{name} < 0, {name} > 1 or {name} {nan_msg}") elif cons == _CONS_BOUNDED_GT_0_1: - if not numpy.all((val > 0) & (val <= 1)): + if not xp.all((val > 0) & (val <= 1)): raise ValueError(f"{name} <= 0, {name} > 1 or {name} contains NaNs") elif cons == _CONS_BOUNDED_LT_0_1: - if not numpy.all((val >= 0) & (val < 1)): + if not xp.all((val >= 0) & (val < 1)): raise ValueError(f"{name} < 0, {name} >= 1 or {name} {nan_msg}") elif cons == _CONS_GT_1: - if not numpy.all(val > 1): + if not xp.all(val > 1): raise ValueError(f"{name} <= 1 or {name} {nan_msg}") + elif cons == _CONS_GTE_1: + if not xp.all(val >= 1): + raise ValueError(f"{name} < 1 or {name} {nan_msg}") elif cons == _CONS_POISSON: # the array path checks the upper bound first, so NaN fails it if scalar: @@ -256,9 +303,9 @@ def _check_constraint(value, name, cons, scalar): if val > _POISSON_LAM_MAX: raise ValueError(f"{name} value too large") else: - if not numpy.all(val <= _POISSON_LAM_MAX): + if not xp.all(val <= _POISSON_LAM_MAX): raise ValueError(f"{name} value too large") - if not numpy.all(val >= 0): + if not xp.all(val >= 0): raise ValueError(f"{name} < 0 or {name} contains NaNs") @@ -325,6 +372,9 @@ class Generator: """ + # Whether parameters on the device are checked, waiting for the device + _check_device = False + def __init__(self, bit_generator): if not isinstance(bit_generator, BitGenerator): raise TypeError( @@ -546,7 +596,9 @@ def _cont(self, kernel, name, size, dtype, usm_type, *params, out=None): self._check_queues(*values, out) scalar = _is_scalar_path(*values) for value, pname, cons in params: - _check_constraint(value, pname, cons, scalar) + _check_constraint( + value, pname, cons, scalar, device=self._check_device + ) return self._float_sample( kernel, values, size, dtype, usm_type, name, out=out ) @@ -652,6 +704,7 @@ def uniform( if scalar else "Range exceeds valid bounds" ), + self._check_device, ) if rng is not None: _check_constraint( @@ -659,6 +712,7 @@ def uniform( "high - low", _CONS_NON_NEGATIVE, scalar, + device=self._check_device, ) return self._float_sample( ri._uniform, (low, high), size, dtype, usm_type, "uniform" @@ -997,7 +1051,9 @@ def triangular( params = (left, mode, right) self._check_queues(*params) - xp, (fleft, fmode, fright) = _check_arrays(params, numpy.float64) + xp, (fleft, fmode, fright) = _check_arrays( + params, self._check_device, numpy.float64 + ) if xp is not None: if xp is numpy and _is_scalar_path(*params): for p in params: @@ -1055,6 +1111,7 @@ def standard_gamma( "shape", _CONS_NON_NEGATIVE, _is_scalar_path(shape), + device=self._check_device, ) return self._float_sample( ri._gamma, @@ -1242,6 +1299,7 @@ def poisson(self, lam=1.0, size=None, *, usm_type="device"): "lam", _CONS_POISSON, _is_scalar_path(lam), + device=self._check_device, ) return self._discrete_sample( ri._poisson, (lam,), size, usm_type, f32_bounds=(_F32_LT_2_63,) @@ -1264,8 +1322,12 @@ def binomial(self, n, p, size=None, *, usm_type="device"): scalar = _is_scalar_path(n, p) # NumPy converts both parameters before checking either n = _as_int64(n) - _check_constraint(p, "p", _CONS_BOUNDED_0_1, scalar) - _check_constraint(n, "n", _CONS_NON_NEGATIVE, scalar) + _check_constraint( + p, "p", _CONS_BOUNDED_0_1, scalar, device=self._check_device + ) + _check_constraint( + n, "n", _CONS_NON_NEGATIVE, scalar, device=self._check_device + ) return self._discrete_sample( ri._binomial, (n, p), size, usm_type, param_types=(_int64, None) ) @@ -1285,8 +1347,12 @@ def negative_binomial(self, n, p, size=None, *, usm_type="device"): self._check_queues(n, p) # NumPy takes scalar parameters as arrays here, so None gives NaN - _check_constraint(n, "n", _CONS_POSITIVE_NOT_NAN, False) - _check_constraint(p, "p", _CONS_BOUNDED_GT_0_1, False) + _check_constraint( + n, "n", _CONS_POSITIVE_NOT_NAN, False, device=self._check_device + ) + _check_constraint( + p, "p", _CONS_BOUNDED_GT_0_1, False, device=self._check_device + ) if not ( dpnp.is_supported_array_type(n) or dpnp.is_supported_array_type(p) ): @@ -1322,6 +1388,7 @@ def geometric(self, p, size=None, *, usm_type="device"): "p", _CONS_BOUNDED_GT_0_1, _is_scalar_path(p), + device=self._check_device, ) return self._discrete_sample( ri._geometric, (p,), size, usm_type, f32_bounds=(_F32_GT_0,) @@ -1346,6 +1413,7 @@ def logseries(self, p, size=None, *, usm_type="device"): "p", _CONS_BOUNDED_LT_0_1, _is_scalar_path(p), + device=self._check_device, ) return self._discrete_sample( ri._logseries, (p,), size, usm_type, f32_bounds=(_F32_LT_1,) @@ -1367,7 +1435,9 @@ def zipf(self, a, size=None, *, usm_type="device"): """ self._check_queues(a) - _check_constraint(a, "a", _CONS_GT_1, _is_scalar_path(a)) + _check_constraint( + a, "a", _CONS_GT_1, _is_scalar_path(a), device=self._check_device + ) return self._discrete_sample( ri._zipf, (a,), size, usm_type, f32_bounds=(_F32_GT_1,) ) @@ -1393,16 +1463,18 @@ def hypergeometric( scalar = _is_scalar_path(*params) for x in params[:2]: - xp, (xa,) = _check_arrays((x,)) + xp, (xa,) = _check_arrays((x,), self._check_device) if xp is not None and xp.any(xa >= _HYPERGEOM_MAX): raise ValueError( "both ngood and nbad must be less than " f"{_HYPERGEOM_MAX}" ) - xp, (good, bad, sample) = _check_arrays(params) + xp, (good, bad, sample) = _check_arrays(params, self._check_device) if xp is not None and xp.any(good + bad < sample): raise ValueError("ngood + nbad < nsample") for x, name in zip(params, names): - _check_constraint(x, name, _CONS_NON_NEGATIVE, scalar) + _check_constraint( + x, name, _CONS_NON_NEGATIVE, scalar, device=self._check_device + ) return self._discrete_sample( ri._hypergeometric, @@ -1521,9 +1593,12 @@ def multinomial(self, n, pvals, size=None, *, usm_type="device"): "pvals must have at least 1 dimension and the last dimension " "of pvals must be greater than 0." ) - if on_host: - _check_constraint(pvals, "pvals", _CONS_BOUNDED_0_1, False) - if numpy.any(pvals[..., :-1].sum(axis=-1) > 1.0 + 1e-12): + if on_host or self._check_device: + xp = numpy if on_host else dpnp + _check_constraint( + pvals, "pvals", _CONS_BOUNDED_0_1, False, device=True + ) + if xp.any(pvals[..., :-1].sum(axis=-1) > 1.0 + 1e-12): raise ValueError( "sum(pvals[:-1]) > 1.0" if pvals.ndim == 1 @@ -1536,6 +1611,7 @@ def multinomial(self, n, pvals, size=None, *, usm_type="device"): "n", _CONS_NON_NEGATIVE, numpy.ndim(n) == 0, + device=self._check_device, ) d = pvals.shape[-1] @@ -1994,7 +2070,13 @@ def _choice(self, a, size, replace, p, axis, usm_type, msgs): raise ValueError(msgs["p_1d"]) if p_dev.size != pop_size: raise ValueError(msgs["p_size"]) - p_check = p_host + if p_host is None and self._check_device: + p_check = p_dev + atol = numpy.sqrt(numpy.finfo(numpy.float64).eps) + if dpnp.issubdtype(p_dev.dtype, dpnp.floating): + atol = max(atol, numpy.sqrt(numpy.finfo(p_dev.dtype).eps)) + else: + p_check = p_host if p_check is not None: p_sum = float(p_check.sum()) if numpy.isnan(p_sum): @@ -2127,8 +2209,11 @@ def _check_bounds(low, high): params = (low, high) def _on_host(x): - # bounds on the device are not checked - return None if dpnp.is_supported_array_type(x) else x + if not dpnp.is_supported_array_type(x): + return x + # NumPy's checks of mixed integer types and Python integers + # are exact, so bounds on the device are checked on the host + return dpnp.asnumpy(x) if self._check_device else None _check_bounds( *( diff --git a/dpnp/random/dpnp_iface_random.py b/dpnp/random/dpnp_iface_random.py index ca1e3d1a463..0ec4ff1c7bc 100644 --- a/dpnp/random/dpnp_iface_random.py +++ b/dpnp/random/dpnp_iface_random.py @@ -1,4 +1,3 @@ -# cython: language_level=3 # ***************************************************************************** # Copyright (c) 2016, Intel Corporation # All rights reserved. @@ -28,1156 +27,985 @@ # ***************************************************************************** """ -Module Intel NumPy Random +The functions of :mod:`dpnp.random` of NumPy's legacy API. -Set of functions to implement NumPy random module API +Every function draws from a :class:`dpnp.random.RandomState` of the SYCL +queue given by `device` or `sycl_queue`. Without either, the common queue of +the parameters that are :class:`dpnp.ndarray` is used, or else the default +queue, and parameters on incompatible queues raise +:class:`dpnp.exceptions.ExecutionPlacementError`. There is one state per +queue, which :func:`seed` reseeds. .. seealso:: :obj:`numpy.random` """ -import operator -import threading +# pylint: disable=redefined-builtin +# pylint: disable=too-many-arguments -import numpy +import threading import dpnp -from dpnp.dpnp_algo import * -from dpnp.dpnp_utils import * +import dpnp.tensor as dpt +from dpnp.exceptions import ExecutionPlacementError + +from ._bit_generator import BitGenerator, Philox4x3210 +from .dpnp_random_state import RandomState, _reseed_in_place + +# The states of the queues. The lock guarantees that concurrent callers +# asking for the state of the same queue get the same instance back rather +# than each building one of their own. +_dpnp_random_states = {} +_random_states_lock = threading.Lock() + +_QUEUE_PARAMS = """\ + device : {None, string, SyclDevice, SyclQueue, Device}, optional + An array API concept of device where the output array is created. + `device` can be ``None``, a oneAPI filter selector string, an + instance of :class:`dpctl.SyclDevice` corresponding to a + non-partitioned SYCL device, an instance of :class:`dpctl.SyclQueue`, + or a :class:`dpnp.tensor.Device` object returned by + :attr:`dpnp.ndarray.device`. + + Default: ``None``. + sycl_queue : {None, SyclQueue}, optional + A SYCL queue to use for output array allocation and copying. + + Default: ``None``.""" + +_USM_PARAM = """\ + usm_type : {"device", "shared", "host"}, optional + The type of SYCL USM allocation for the output array. -from .dpnp_algo_random import * -from .dpnp_random_state import RandomState + Default: ``"device"``.""" -# lock the RNG calls for concurrent access -_legacy_rng_lock = threading.RLock() +_DTYPE_PARAM = """\ + dtype : {None, float32, float64}, optional + The floating-point type of the output. + Default: ``None``, the default floating-point type of the device.""" -def _legacy_rng(func, *args, **kwargs): - """Call a legacy ``dpnp_rng_*`` generator holding `_legacy_rng_lock`.""" - with _legacy_rng_lock: - return func(*args, **kwargs) +def _params_doc(func): + """Fill the placeholders of the parameters common to the functions.""" + + func.__doc__ = ( + func.__doc__.replace(" %(queue)s", _QUEUE_PARAMS) + .replace(" %(usm)s", _USM_PARAM) + .replace(" %(dtype)s", _DTYPE_PARAM) + ) + return func -# Guards the `_dpnp_random_states` cache, so that concurrent callers asking -# for the state of the same queue are guaranteed to get the same instance -# back rather than each building one of their own. -_random_states_lock = threading.RLock() +def _get_random_state(device=None, sycl_queue=None, arrays=()): + """ + The state of the queue of `device` or `sycl_queue`, or else of the common + queue of the arrays of `arrays`, or else of the default queue. -def _get_random_state(device=None, sycl_queue=None): - global _dpnp_random_states + """ + if device is None and sycl_queue is None: + queues = [ + x.sycl_queue for x in arrays if dpnp.is_supported_array_type(x) + ] + if queues: + sycl_queue = dpt.get_execution_queue(queues) + if sycl_queue is None: + raise ExecutionPlacementError( + "Input arrays have incompatible allocation queues" + ) sycl_queue = dpnp.get_normalized_queue_device( device=device, sycl_queue=sycl_queue ) with _random_states_lock: - if not isinstance(_dpnp_random_states, dict): - _dpnp_random_states = {} - if sycl_queue not in _dpnp_random_states: - rs = RandomState(device=device, sycl_queue=sycl_queue) - if sycl_queue == rs.get_sycl_queue(): - _dpnp_random_states[sycl_queue] = rs - else: - raise RuntimeError( - f"Normalized SYCL queue {sycl_queue} mismatched with one " - f"returned by RandmoState {rs.get_sycl_queue()}" - ) - return _dpnp_random_states[sycl_queue] + rs = _dpnp_random_states.get(sycl_queue) + if rs is None: + rs = RandomState(sycl_queue=sycl_queue) + _dpnp_random_states[sycl_queue] = rs + return rs -def _is_type_supported(obj_type): - """Return True if type is supported by dpnp.random""" +@_params_doc +def seed(seed=None, *, device=None, sycl_queue=None): + # pylint: disable=redefined-outer-name + """ + Reseed the state of a queue. + + For full documentation refer to :obj:`numpy.random.seed`. - if obj_type in (dpnp.float64, dpnp.float32, dpnp.int64, dpnp.int32): - return True - return False + Parameters + ---------- + %(queue)s + Notes + ----- + A bit generator set by :func:`dpnp.random.set_bit_generator` other than + :class:`dpnp.random.Philox4x3210` is reseeded in place with the state a + new one of its type would have for `seed`, as in NumPy. -def beta(a, b, size=None): """ - Draw samples from a Beta distribution. + # pylint: disable=protected-access + rs = _get_random_state(device, sycl_queue) + bit_generator = rs._generator.bit_generator + if type(bit_generator) is Philox4x3210: + rs.seed(seed) + else: + _reseed_in_place( + bit_generator, + type(bit_generator)(seed, sycl_queue=bit_generator.sycl_queue), + ) - For full documentation refer to :obj:`numpy.random.beta`. + +@_params_doc +def get_state(legacy=True, *, device=None, sycl_queue=None): + """ + Return the state of a queue. + + For full documentation refer to :obj:`numpy.random.get_state`. + + Parameters + ---------- + %(queue)s Limitations ----------- - Parameters `a` and `b` are supported as scalar. - Otherwise, :obj:`numpy.random.beta(a, b, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + See :meth:`dpnp.random.RandomState.get_state`. - Examples - -------- - Draw samples from the distribution: + """ + return _get_random_state(device, sycl_queue).get_state(legacy) - >>> a, b = .4, .5 # alpha, beta - >>> s = dpnp.random.beta(a, b, 1000) +@_params_doc +def set_state(state, *, device=None, sycl_queue=None): """ + Set the state of a queue. + + For full documentation refer to :obj:`numpy.random.set_state`. - if not use_origin_backend(a): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + Parameters + ---------- + %(queue)s - # TODO: - # array_like of floats for `a`, `b` - if not dpnp.isscalar(a): - pass - elif not dpnp.isscalar(b): - pass - elif a <= 0: - pass - elif b <= 0: - pass - else: - return _legacy_rng(dpnp_rng_beta, a, b, size).get_pyobj() + Limitations + ----------- + See :meth:`dpnp.random.RandomState.set_state`. - return call_origin(numpy.random.beta, a, b, size) + """ + _get_random_state(device, sycl_queue).set_state(state) -def binomial(n, p, size=None): +@_params_doc +def get_bit_generator(*, device=None, sycl_queue=None): """ - Draw samples from a binomial distribution. + Return the bit generator of the state of a queue, which the + :mod:`dpnp.random` functions draw from on that queue. - For full documentation refer to :obj:`numpy.random.binomial`. + For full documentation refer to :obj:`numpy.random.get_bit_generator`. - Limitations - ----------- - Output array data type is :obj:`dpnp.int32`. - Parameters `n` and `p` are supported as scalar. - Otherwise, :obj:`numpy.random.binomial(n, p, size)` samples are drawn. + Parameters + ---------- + %(queue)s + + Returns + ------- + out : BitGenerator + The bit generator, a :class:`dpnp.random.Philox4x3210` unless set + by :func:`dpnp.random.set_bit_generator`. + + See Also + -------- + :obj:`dpnp.random.set_bit_generator` : Set the bit generator of a queue. + + """ + # pylint: disable=protected-access + return _get_random_state(device, sycl_queue)._generator.bit_generator + + +def set_bit_generator(bitgen): + """ + Set the bit generator of the state of the queue of `bitgen`, which the + :mod:`dpnp.random` functions then draw from on that queue. + + For full documentation refer to :obj:`numpy.random.set_bit_generator`. + + Parameters + ---------- + bitgen : BitGenerator + The bit generator. It is used itself, not a copy, so the + :mod:`dpnp.random` functions and a :class:`dpnp.random.Generator` + of `bitgen` share its stream. + + See Also + -------- + :obj:`dpnp.random.get_bit_generator` : Return the bit generator of a + queue. Examples -------- - Draw samples from the distribution: + >>> import dpnp as np + >>> bg = np.random.PCG64DXSM(42) + >>> np.random.set_bit_generator(bg) + >>> np.random.get_bit_generator() is bg + True + + """ + if isinstance(bitgen, type) and issubclass(bitgen, BitGenerator): + raise ValueError( + "Invalid bit generator. The bit generator must be instantiated." + ) + if not isinstance(bitgen, BitGenerator): + raise TypeError( + "bitgen must be an instance of dpnp.random.BitGenerator" + ) + # pylint: disable=protected-access + _get_random_state(sycl_queue=bitgen.sycl_queue)._set_bit_generator(bitgen) + + +@_params_doc +def beta( + a, + b, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): + """ + Draw samples from a Beta distribution. + + For full documentation refer to :obj:`numpy.random.beta`. + + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s + + See Also + -------- + :obj:`dpnp.random.RandomState.beta` + + """ + rs = _get_random_state(device, sycl_queue, (a, b)) + return rs.beta(a, b, size, dtype, usm_type=usm_type) - >>> n, p = 10, .5 # number of trials, probability of each trial - >>> s = dpnp.random.binomial(n, p, 1000) - # result of flipping a coin 10 times, tested 1000 times. - A real world example. A company drills 9 wild-cat oil exploration - wells, each with an estimated probability of success of 0.1. All nine - wells fail. What is the probability of that happening? - Let's do 20,000 trials of the model, and count the number that - generate zero positive results. - >>> sum(dpnp.random.binomial(9, 0.1, 20000) == 0)/20000. - # answer = 0.38885, or 38%. +@_params_doc +def binomial( + n, p, size=None, *, device=None, usm_type="device", sycl_queue=None +): """ + Draw samples from a binomial distribution. + + For full documentation refer to :obj:`numpy.random.binomial`. - if not use_origin_backend(n): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + Parameters + ---------- + %(queue)s + %(usm)s - # TODO: - # array_like of floats for `p` param - if not dpnp.isscalar(n): - pass - elif not dpnp.isscalar(p): - pass - elif p > 1 or p < 0: - pass - elif n < 0: - pass - else: - return _legacy_rng(dpnp_rng_binomial, int(n), p, size).get_pyobj() + See Also + -------- + :obj:`dpnp.random.RandomState.binomial` - return call_origin(numpy.random.binomial, n, p, size) + """ + rs = _get_random_state(device, sycl_queue, (n, p)) + return rs.binomial(n, p, size, usm_type=usm_type) -def bytes(length): +@_params_doc +def bytes(length, *, device=None, sycl_queue=None): """ Return random bytes. For full documentation refer to :obj:`numpy.random.bytes`. - Notes - ----- - The function uses `numpy.random.bytes` on the backend and will be - executed on fallback backend. + Parameters + ---------- + %(queue)s """ + return _get_random_state(device, sycl_queue).bytes(length) - return call_origin(numpy.random.bytes, length) - -def chisquare(df, size=None): +@_params_doc +def chisquare( + df, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a chi-square distribution. For full documentation refer to :obj:`numpy.random.chisquare`. - Limitations - ----------- - Parameter `df` is supported as a scalar. - Otherwise, :obj:`numpy.random.chisquare(df, size)` samples are drawn. - Output array data type is default float type. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - >>> dpnp.random.chisquare(2, 4) - array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272]) # random + :obj:`dpnp.random.RandomState.chisquare` """ + rs = _get_random_state(device, sycl_queue, (df,)) + return rs.chisquare(df, size, dtype, usm_type=usm_type) - if not use_origin_backend(df): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - # TODO: - # array_like of floats for `df` - if not dpnp.isscalar(df): - pass - elif df <= 0: - pass - else: - # TODO: - # float to int, safe - return _legacy_rng(dpnp_rng_chisquare, int(df), size).get_pyobj() - - return call_origin(numpy.random.chisquare, df, size) - - -def choice(a, size=None, replace=True, p=None): +@_params_doc +def choice( + a, + size=None, + replace=True, + p=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Generates a random sample from a given 1-D array. + Generate a random sample from a given 1-D array. For full documentation refer to :obj:`numpy.random.choice`. - Notes - ----- - The function uses `numpy.random.choice` on the backend and will be - executed on fallback backend. + Parameters + ---------- + %(queue)s + %(usm)s - """ + See Also + -------- + :obj:`dpnp.random.RandomState.choice` - return call_origin(numpy.random.choice, a, size, replace, p) + """ + rs = _get_random_state(device, sycl_queue, (a, p)) + return rs.choice(a, size, replace, p, usm_type=usm_type) -def dirichlet(alpha, size=None): +@_params_doc +def dirichlet( + alpha, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from the Dirichlet distribution. For full documentation refer to :obj:`numpy.random.dirichlet`. - Notes - ----- - The function uses `numpy.random.dirichlet` on the backend and will be - executed on fallback backend. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - """ + See Also + -------- + :obj:`dpnp.random.RandomState.dirichlet` - return call_origin(numpy.random.dirichlet, alpha, size) + """ + rs = _get_random_state(device, sycl_queue, (alpha,)) + return rs.dirichlet(alpha, size, dtype, usm_type=usm_type) -def exponential(scale=1.0, size=None): +@_params_doc +def exponential( + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from an exponential distribution. For full documentation refer to :obj:`numpy.random.exponential`. - Limitations - ----------- - Parameter `scale` is supported as a scalar. - Otherwise, :obj:`numpy.random.exponential(scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> scale = .5 # alpha - >>> s = dpnp.random.exponential(scale, 1000) + :obj:`dpnp.random.RandomState.exponential` """ + rs = _get_random_state(device, sycl_queue, (scale,)) + return rs.exponential(scale, size, dtype, usm_type=usm_type) - if not use_origin_backend(scale): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `scale` - if not dpnp.isscalar(scale): - pass - elif scale < 0: - pass - else: - return _legacy_rng(dpnp_rng_exponential, scale, size).get_pyobj() - - return call_origin(numpy.random.exponential, scale, size) - -def f(dfnum, dfden, size=None): +@_params_doc +def f( + dfnum, + dfden, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from an F distribution. For full documentation refer to :obj:`numpy.random.f`. - Limitations - ----------- - Parameters `dfnum` and `dfden` are supported as scalar. - Otherwise, :obj:`numpy.random.f(dfnum, dfden, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - >>> dfnum, dfden = 3., 2. - >>> s = dpnp.random.f(dfnum, dfden, size) + :obj:`dpnp.random.RandomState.f` """ + rs = _get_random_state(device, sycl_queue, (dfnum, dfden)) + return rs.f(dfnum, dfden, size, dtype, usm_type=usm_type) - if not use_origin_backend(dfnum): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - # TODO: - # array_like of floats for `dfnum` and `dfden` - if not dpnp.isscalar(dfnum): - pass - elif not dpnp.isscalar(dfden): - pass - elif dfnum <= 0: - pass - elif dfden <= 0: - pass - else: - return _legacy_rng(dpnp_rng_f, dfnum, dfden, size).get_pyobj() - - return call_origin(numpy.random.f, dfnum, dfden, size) - - -def gamma(shape, scale=1.0, size=None): +@_params_doc +def gamma( + shape, + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a Gamma distribution. For full documentation refer to :obj:`numpy.random.gamma`. - Limitations - ----------- - Parameters `shape` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.gamma(shape, scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> shape, scale = 0, 0.1 # shape and scale - >>> s = dpnp.random.gamma(shape, scale, 1000) + :obj:`dpnp.random.RandomState.gamma` """ + rs = _get_random_state(device, sycl_queue, (shape, scale)) + return rs.gamma(shape, scale, size, dtype, usm_type=usm_type) - if not use_origin_backend(scale): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - # TODO: - # array_like of floats for `scale` and `shape` - if not dpnp.isscalar(scale): - pass - elif not dpnp.isscalar(shape): - pass - elif scale < 0: - pass - elif shape < 0: - pass - else: - return _legacy_rng(dpnp_rng_gamma, shape, scale, size).get_pyobj() - - return call_origin(numpy.random.gamma, shape, scale, size) - - -def geometric(p, size=None): +@_params_doc +def geometric(p, size=None, *, device=None, usm_type="device", sycl_queue=None): """ Draw samples from the geometric distribution. For full documentation refer to :obj:`numpy.random.geometric`. - Limitations - ----------- - Parameter `p` is supported as a scalar. - Otherwise, :obj:`numpy.random.geometric(p, size)` samples are drawn. - Output array data type is :obj:`dpnp.int32`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Draw ten thousand values from the geometric distribution, - with the probability of an individual success equal to 0.35: - - >>> z = dpnp.random.geometric(p=0.35, size=10000) + :obj:`dpnp.random.RandomState.geometric` """ + rs = _get_random_state(device, sycl_queue, (p,)) + return rs.geometric(p, size, usm_type=usm_type) - if not use_origin_backend(p): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `p` param - if not dpnp.isscalar(p): - pass - elif p > 1 or p <= 0: - pass - else: - return _legacy_rng(dpnp_rng_geometric, p, size).get_pyobj() - - return call_origin(numpy.random.geometric, p, size) - -def gumbel(loc=0.0, scale=1.0, size=None): +@_params_doc +def gumbel( + loc=0.0, + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a Gumbel distribution. For full documentation refer to :obj:`numpy.random.gumbel`. - Limitations - ----------- - Parameters `loc` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.gumbel(loc, scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> mu, beta = 0, 0.1 # location and scale - >>> s = dpnp.random.gumbel(mu, beta, 1000) + :obj:`dpnp.random.RandomState.gumbel` """ + rs = _get_random_state(device, sycl_queue, (loc, scale)) + return rs.gumbel(loc, scale, size, dtype, usm_type=usm_type) - if not use_origin_backend(loc): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `loc` and `scale` params - if not dpnp.isscalar(scale): - pass - elif not dpnp.isscalar(loc): - pass - elif scale < 0: - pass - else: - return _legacy_rng(dpnp_rng_gumbel, loc, scale, size).get_pyobj() - return call_origin(numpy.random.gumbel, loc, scale, size) - - -def hypergeometric(ngood, nbad, nsample, size=None): +@_params_doc +def hypergeometric( + ngood, + nbad, + nsample, + size=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from a Hypergeometric distribution. + Draw samples from a hypergeometric distribution. For full documentation refer to :obj:`numpy.random.hypergeometric`. - Limitations - ----------- - Parameters `ngood`, `nbad` and `nsample` are supported as scalar. - Otherwise, :obj:`numpy.random.hypergeometric(shape, scale, size)` samples - are drawn. - Output array data type is :obj:`dpnp.int32`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> ngood, nbad, nsamp = 100, 2, 10 - # number of good, number of bad, and number of samples - >>> s = dpnp.random.hypergeometric(ngood, nbad, nsamp, 1000) - - """ - - if not use_origin_backend(ngood): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of ints for `ngood`, `nbad`, `nsample` param - if not dpnp.isscalar(ngood): - pass - elif not dpnp.isscalar(nbad): - pass - elif not dpnp.isscalar(nsample): - pass - elif ngood < 0: - pass - elif nbad < 0: - pass - elif nsample < 0: - pass - elif ngood + nbad < nsample: - pass - elif nsample < 1: - pass - else: - _m = int(ngood) - _l = int(ngood) + int(nbad) - _s = int(nsample) - return _legacy_rng( - dpnp_rng_hypergeometric, _l, _s, _m, size - ).get_pyobj() - - return call_origin(numpy.random.hypergeometric, ngood, nbad, nsample, size) - - -def laplace(loc=0.0, scale=1.0, size=None): - """ - Draw samples from the Laplace or double exponential distribution with - specified location (or mean) and scale (decay). - - For full documentation refer to :obj:`numpy.random.laplace`. + :obj:`dpnp.random.RandomState.hypergeometric` - Limitations - ----------- - Parameters `loc` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.laplace(loc, scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + """ + rs = _get_random_state(device, sycl_queue, (ngood, nbad, nsample)) + return rs.hypergeometric(ngood, nbad, nsample, size, usm_type=usm_type) - Examples - -------- - >>> loc, scale = 0., 1. - >>> s = dpnp.random.laplace(loc, scale, 1000) +@_params_doc +def laplace( + loc=0.0, + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ + Draw samples from the Laplace or double exponential distribution. - if not use_origin_backend(loc): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `loc` and `scale` - if not dpnp.isscalar(loc): - pass - elif not dpnp.isscalar(scale): - pass - elif scale < 0: - pass - else: - return _legacy_rng(dpnp_rng_laplace, loc, scale, size).get_pyobj() + For full documentation refer to :obj:`numpy.random.laplace`. - return call_origin(numpy.random.laplace, loc, scale, size) + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s + See Also + -------- + :obj:`dpnp.random.RandomState.laplace` -def logistic(loc=0.0, scale=1.0, size=None): """ - Draw samples from a logistic distribution. + rs = _get_random_state(device, sycl_queue, (loc, scale)) + return rs.laplace(loc, scale, size, dtype, usm_type=usm_type) - For full documentation refer to :obj:`numpy.random.logistic`. - - Limitations - ----------- - Parameters `loc` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.logistic(loc, scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. - - Examples - -------- - >>> loc, scale = 0., 1. - >>> s = dpnp.random.logistic(loc, scale, 1000) +@_params_doc +def logistic( + loc=0.0, + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ + Draw samples from a logistic distribution. + + For full documentation refer to :obj:`numpy.random.logistic`. - if not use_origin_backend(loc): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - # TODO: - # array_like of floats for `loc` and `scale` - if not dpnp.isscalar(loc): - pass - elif not dpnp.isscalar(scale): - pass - elif scale < 0: - pass - else: - result = _legacy_rng( - dpnp_rng_logistic, loc, scale, size - ).get_pyobj() - if size is None or size == 1: - return result[0] - else: - return result + See Also + -------- + :obj:`dpnp.random.RandomState.logistic` - return call_origin(numpy.random.logistic, loc, scale, size) + """ + rs = _get_random_state(device, sycl_queue, (loc, scale)) + return rs.logistic(loc, scale, size, dtype, usm_type=usm_type) -def lognormal(mean=0.0, sigma=1.0, size=None): +@_params_doc +def lognormal( + mean=0.0, + sigma=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a log-normal distribution. For full documentation refer to :obj:`numpy.random.lognormal`. - Limitations - ----------- - Parameters `mean` and `sigma` are supported as scalar. - Otherwise, :obj:`numpy.random.lognormal(mean, sigma, size)` samples - are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> mu, sigma = 3., 1. # mean and standard deviation - >>> s = dpnp.random.lognormal(mu, sigma, 1000) + :obj:`dpnp.random.RandomState.lognormal` """ + rs = _get_random_state(device, sycl_queue, (mean, sigma)) + return rs.lognormal(mean, sigma, size, dtype, usm_type=usm_type) - if not use_origin_backend(mean): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `mean` and `sigma` params - if not dpnp.isscalar(mean): - pass - elif not dpnp.isscalar(sigma): - pass - elif sigma < 0: - pass - else: - return _legacy_rng( - dpnp_rng_lognormal, mean, sigma, size - ).get_pyobj() - return call_origin(numpy.random.lognormal, mean, sigma, size) - - -def logseries(p, size=None): +@_params_doc +def logseries(p, size=None, *, device=None, usm_type="device", sycl_queue=None): """ Draw samples from a logarithmic series distribution. For full documentation refer to :obj:`numpy.random.logseries`. - Notes - ----- - The function uses `numpy.random.logseries` on the backend and will be - executed on fallback backend. + Parameters + ---------- + %(queue)s + %(usm)s - """ + See Also + -------- + :obj:`dpnp.random.RandomState.logseries` - return call_origin(numpy.random.logseries, p, size) + """ + rs = _get_random_state(device, sycl_queue, (p,)) + return rs.logseries(p, size, usm_type=usm_type) -def multinomial(n, pvals, size=None): +@_params_doc +def multinomial( + n, pvals, size=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from a multinomial distribution. For full documentation refer to :obj:`numpy.random.multinomial`. - Limitations - ----------- - Parameter `n` limited with int32 max. See, `numpy.iinfo(numpy.int32).max`. - Sum of ``pvals``, `sum(pvals)` should be between (0, 1). - Otherwise, :obj:`numpy.random.multinomial(n, pvals, size)` - samples are drawn. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Throw a dice 20 times: - - >>> s = dpnp.random.multinomial(20, [1/6.]*6, size=1) - >>> s.shape - (1, 6) + :obj:`dpnp.random.RandomState.multinomial` """ + rs = _get_random_state(device, sycl_queue, (n, pvals)) + return rs.multinomial(n, pvals, size, usm_type=usm_type) - if not use_origin_backend(n): - pvals_sum = sum(pvals) - pvals_desc = dpnp.get_dpnp_descriptor(dpnp.array(pvals)) - d = len(pvals) - if dpnp.is_cuda_backend(pvals_desc.get_array()): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - if n < 0: - pass - elif n > dpnp.iinfo(dpnp.int32).max: - pass - elif pvals_sum > 1.0: - pass - elif pvals_sum < 0.0: - pass - else: - if size is None: - shape = (d,) - else: - try: - shape = (operator.index(size), d) - except Exception: - shape = tuple(size) + (d,) - - return _legacy_rng( - dpnp_rng_multinomial, int(n), pvals_desc, shape - ).get_pyobj() - return call_origin(numpy.random.multinomial, n, pvals, size) - - -def multivariate_normal(mean, cov, size=None, check_valid="warn", tol=1e-8): +@_params_doc +def multivariate_normal( + mean, + cov, + size=None, + check_valid="warn", + tol=1e-8, + *, + dtype=None, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw random samples from a multivariate normal distribution. For full documentation refer to :obj:`numpy.random.multivariate_normal`. - Limitations - ----------- - Parameters `check_valid` and `tol` are not supported. - Otherwise, :obj:`numpy.random.multivariate_normal(mean, cov, size, check_valid, tol)` - samples are drawn. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - >>> mean = (1, 2) - >>> cov = [[1, 0], [0, 1]] - >>> x = dpnp.random.multivariate_normal(mean, cov, (3, 3)) - >>> x.shape - (3, 3, 2) - - """ - - if not use_origin_backend(mean): - mean_ = dpnp.get_dpnp_descriptor(dpnp.array(mean, dtype=dpnp.float64)) - cov_ = dpnp.get_dpnp_descriptor(dpnp.array(cov, dtype=dpnp.float64)) - if dpnp.is_cuda_backend(mean_.get_array()) or dpnp.is_cuda_backend( - cov_.get_array() - ): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - if size is None: - shape = [] - elif isinstance(size, (int, dpnp.integer)): - shape = [size] - else: - shape = size - if len(mean_.shape) != 1: - pass - elif (len(cov_.shape) != 2) or (cov_.shape[0] != cov_.shape[1]): - pass - elif mean_.shape[0] != cov_.shape[0]: - pass - else: - final_shape = list(shape[:]) - final_shape.append(mean_.shape[0]) - return _legacy_rng( - dpnp_rng_multivariate_normal, mean_, cov_, final_shape - ).get_pyobj() - - return call_origin( - numpy.random.multivariate_normal, mean, cov, size, check_valid, tol + :obj:`dpnp.random.RandomState.multivariate_normal` + + """ + # pylint: disable=protected-access + rs = _get_random_state(device, sycl_queue, (mean, cov)) + return rs._multivariate_normal( + mean, cov, size, check_valid, tol, dtype, usm_type, stacklevel=3 ) -def negative_binomial(n, p, size=None): +@_params_doc +def negative_binomial( + n, p, size=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from a negative binomial distribution. For full documentation refer to :obj:`numpy.random.negative_binomial`. - Limitations - ----------- - Parameters `n` and `p` are supported as scalar. - Otherwise, :obj:`numpy.random.negative_binomial(n, p, size)` samples - are drawn. - Output array data type is :obj:`dpnp.int32`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - A real world example. A company drills wild-cat oil - exploration wells, each with an estimated probability of - success of 0.1. What is the probability of having one success - for each successive well, that is what is the probability of a - single success after drilling 5 wells, after 6 wells, etc.? - - >>> s = dpnp.random.negative_binomial(1, 0.1, 100000) - >>> for i in range(1, 11): - ... probability = sum(s 1 or p < 0: - pass - elif n <= 0: - pass - else: - return _legacy_rng( - dpnp_rng_negative_binomial, n, p, size - ).get_pyobj() - - return call_origin(numpy.random.negative_binomial, n, p, size) + :obj:`dpnp.random.RandomState.negative_binomial` + + """ + rs = _get_random_state(device, sycl_queue, (n, p)) + return rs.negative_binomial(n, p, size, usm_type=usm_type) -def normal( - loc=0.0, - scale=1.0, +@_params_doc +def noncentral_chisquare( + df, + nonc, size=None, + dtype=None, + *, device=None, usm_type="device", sycl_queue=None, ): """ - Draw random samples from a normal (Gaussian) distribution. + Draw samples from a noncentral chi-square distribution. - For full documentation refer to :obj:`numpy.random.normal`. + For full documentation refer to :obj:`numpy.random.noncentral_chisquare`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. + %(dtype)s + %(queue)s + %(usm)s - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Drawn samples from the parameterized normal distribution. - Output array data type is the same as input `dtype`. If `dtype` is ``None`` (the default), - :obj:`dpnp.float64` type will be used if device supports it, or :obj:`dpnp.float32` otherwise. - - Limitations - ----------- - Parameters `loc` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.normal(loc, scale, size)` samples are drawn. - Parameter `dtype` is supported only as :obj:`dpnp.float32`, :obj:`dpnp.float64` or ``None``. - - Examples + See Also -------- - Draw samples from the distribution: - - >>> mu, sigma = 0, 0.1 # mean and standard deviation - >>> s = dpnp.random.normal(mu, sigma, 1000) + :obj:`dpnp.random.RandomState.noncentral_chisquare` """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.normal( - loc=loc, scale=scale, size=size, dtype=None, usm_type=usm_type - ) + rs = _get_random_state(device, sycl_queue, (df, nonc)) + return rs.noncentral_chisquare(df, nonc, size, dtype, usm_type=usm_type) -def noncentral_chisquare(df, nonc, size=None): +@_params_doc +def noncentral_f( + dfnum, + dfden, + nonc, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from a non-central chi-square distribution. + Draw samples from the noncentral F distribution. - For full documentation refer to :obj:`numpy.random.noncentral_chisquare`. - - TODO - - """ + For full documentation refer to :obj:`numpy.random.noncentral_f`. - if not use_origin_backend(df): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - # TODO: - # array_like of floats for `mean` and `scale` - if not dpnp.isscalar(df): - pass - elif not dpnp.isscalar(nonc): - pass - elif df <= 0: - pass - elif nonc < 0: - pass - else: - return _legacy_rng( - dpnp_rng_noncentral_chisquare, df, nonc, size - ).get_pyobj() + See Also + -------- + :obj:`dpnp.random.RandomState.noncentral_f` - return call_origin(numpy.random.noncentral_chisquare, df, nonc, size) + """ + rs = _get_random_state(device, sycl_queue, (dfnum, dfden, nonc)) + return rs.noncentral_f(dfnum, dfden, nonc, size, dtype, usm_type=usm_type) -def noncentral_f(dfnum, dfden, nonc, size=None): +@_params_doc +def normal( + loc=0.0, + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from the non-central F distribution. + Draw random samples from a normal (Gaussian) distribution. - For full documentation refer to :obj:`numpy.random.noncentral_f`. + For full documentation refer to :obj:`numpy.random.normal`. - Notes - ----- - The function uses `numpy.random.noncentral_f` on the backend and - will be executed on fallback backend. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - """ + See Also + -------- + :obj:`dpnp.random.RandomState.normal` - return call_origin(numpy.random.noncentral_f, dfnum, dfden, nonc, size) + """ + rs = _get_random_state(device, sycl_queue, (loc, scale)) + return rs.normal(loc, scale, size, dtype, usm_type=usm_type) -def pareto(a, size=None): +@_params_doc +def pareto( + a, size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ - Draw samples from a Pareto II or Lomax distribution with specified shape. + Draw samples from a Pareto II or Lomax distribution with specified + shape. For full documentation refer to :obj:`numpy.random.pareto`. - Limitations - ----------- - Parameter `a` is supported as a scalar. - Otherwise, :obj:`numpy.random.pareto(a, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> a = .5 # alpha - >>> s = dpnp.random.pareto(a, 1000) + :obj:`dpnp.random.RandomState.pareto` """ + rs = _get_random_state(device, sycl_queue, (a,)) + return rs.pareto(a, size, dtype, usm_type=usm_type) - if not use_origin_backend(a): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `a` - if not dpnp.isscalar(a): - pass - elif a <= 0: - pass - else: - return _legacy_rng(dpnp_rng_pareto, a, size).get_pyobj() - return call_origin(numpy.random.pareto, a, size) - - -def permutation(x): +@_params_doc +def permutation(x, *, device=None, sycl_queue=None): """ Randomly permute a sequence, or return a permuted range. For full documentation refer to :obj:`numpy.random.permutation`. - Examples - -------- - >>> arr = dpnp.random.permutation(10) - >>> print(arr) - [3 8 7 9 0 6 1 2 4 5] # random - - >>> arr = dpnp.random.permutation([1, 4, 9, 12, 15]) - >>> print(arr) - [12 1 4 9 15] # random + Parameters + ---------- + %(queue)s - >>> arr = dpnp.arange(9).reshape((3, 3)) - >>> dpnp.random.permutation(arr) - >>> print(arr) - [[0 1 2] - [3 4 5] - [6 7 8]] # random + See Also + -------- + :obj:`dpnp.random.RandomState.permutation` """ - if not use_origin_backend(x): - if isinstance(x, (int, dpnp.integer)): - arr = dpnp.arange(x) - else: - arr = dpnp.array(x) - shuffle(arr) - return arr + return _get_random_state(device, sycl_queue, (x,)).permutation(x) - return call_origin(numpy.random.permutation, x) - -def poisson(lam=1.0, size=None): +@_params_doc +def poisson( + lam=1.0, size=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from a Poisson distribution. For full documentation refer to :obj:`numpy.random.poisson`. - Limitations - ----------- - Parameter `lam` is supported as a scalar. - Otherwise, :obj:`numpy.random.poisson(lam, size)` samples are drawn. - Output array data type is :obj:`dpnp.int32`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> import numpy as np - >>> s = dpnp.random.poisson(5, 10000) + :obj:`dpnp.random.RandomState.poisson` """ + rs = _get_random_state(device, sycl_queue, (lam,)) + return rs.poisson(lam, size, usm_type=usm_type) - if not use_origin_backend(lam): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `lam` param - if not dpnp.isscalar(lam): - pass - elif lam < 0: - pass - else: - return _legacy_rng(dpnp_rng_poisson, lam, size).get_pyobj() - - return call_origin(numpy.random.poisson, lam, size) - -def power(a, size=None): +@_params_doc +def power( + a, size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draws samples in [0, 1] from a power distribution with positive exponent a - 1. For full documentation refer to :obj:`numpy.random.power`. - Limitations - ----------- - Parameter `a` is supported as a scalar. - Otherwise, :obj:`numpy.random.power(a, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> a = .5 # alpha - >>> s = dpnp.random.power(a, 1000) + :obj:`dpnp.random.RandomState.power` """ - - if not use_origin_backend(a): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `a` - if not dpnp.isscalar(a): - pass - elif a <= 0: - pass - else: - return _legacy_rng(dpnp_rng_power, a, size).get_pyobj() - - return call_origin(numpy.random.power, a, size) + rs = _get_random_state(device, sycl_queue, (a,)) + return rs.power(a, size, dtype, usm_type=usm_type) -def rand(*args, device=None, usm_type="device", sycl_queue=None): +@_params_doc +def rand(*args, dtype=None, device=None, usm_type="device", sycl_queue=None): """ - Random values in a given shape. - - Create an array of the given shape and populate it with random samples from - a uniform distribution over ``[0, 1)``. + Random values in a given shape, from a uniform distribution over [0, + 1). For full documentation refer to :obj:`numpy.random.rand`. Parameters ---------- - *args : sequence of ints, optional - The dimensions of the returned array, must be non-negative. - If no argument is given a single Python float is returned. - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Random values in a given shape ``(d0, d1, ..., dn)``. - Output array data type is :obj:`dpnp.float64` if a device supports it, - or :obj:`dpnp.float32` type otherwise. + %(dtype)s + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.random` : Return random floats in the half-open interval - ``[0.0, 1.0)``. - :obj:`dpnp.random.random_sample` : Return random floats in the half-open - interval ``[0.0, 1.0)``. - :obj:`dpnp.random.uniform` : Draw samples from a uniform distribution. - - Examples - -------- - >>> import dpnp as np - >>> s = np.random.rand(3, 2) + :obj:`dpnp.random.RandomState.rand` """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.rand(*args, usm_type=usm_type) + rs = _get_random_state(device, sycl_queue) + return rs.random_sample(args or None, dtype, usm_type=usm_type) +@_params_doc def randint( low, high=None, size=None, dtype=int, + *, device=None, usm_type="device", sycl_queue=None, @@ -1189,1006 +1017,513 @@ def randint( Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - `size`-shaped array of random integers from the appropriate distribution, - or a single such random int if `size` is not provided. - Output array data type is the same as input `dtype`. - - Limitations - ----------- - Parameters `low` and `high` are supported only as a scalar. - Parameter `dtype` is supported only as :obj:`dpnp.int32` or ``int``, - but ``int`` value is considered to be exactly equivalent to :obj:`dpnp.int32`. - Otherwise, :obj:`numpy.random.RandomState.randint(low, high, size, dtype)` samples are drawn. - - Examples - -------- - Draw samples from the distribution: - - >>> low, high = 3, 11 # low and high - >>> s = dpnp.random.randint(low, high, 1000, dtype=dpnp.int32) + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.random_integers` : similar to `randint`, only for the closed - interval [`low`, `high`], and 1 is the - lowest value if `high` is omitted. + :obj:`dpnp.random.RandomState.randint` """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.randint( - low=low, high=high, size=size, dtype=dtype, usm_type=usm_type - ) + rs = _get_random_state(device, sycl_queue, (low, high)) + return rs.randint(low, high, size, dtype, usm_type=usm_type) -def randn(d0, *dn, device=None, usm_type="device", sycl_queue=None): +@_params_doc +def randn(*args, dtype=None, device=None, usm_type="device", sycl_queue=None): """ - Return a sample (or samples) from the "standard normal" distribution. + Return a sample (or samples) from the standard normal distribution. For full documentation refer to :obj:`numpy.random.randn`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from - the standard normal distribution, or a single such float if no parameters were supplied. - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. - - Examples - -------- - >>> dpnp.random.randn() - 2.1923875335537315 # random - - Two-by-four array of samples from N(3, 6.25): - - >>> s = 3 + 2.5 * dpnp.random.randn(2, 4) - - See Also - -------- - :obj:`dpnp.random.standard_normal` - :obj:`dpnp.random.normal` - - """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.randn(d0, *dn, usm_type=usm_type) - - -def random(size=None, device=None, usm_type="device", sycl_queue=None): - """ - Return random floats in the half-open interval [0.0, 1.0). - - Alias for random_sample. - - For full documentation refer to :obj:`numpy.random.random`. - - Parameters - ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Array of random floats of shape `size` (if ``size=None``, zero dimension array with a single float is returned). - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. - - Examples - -------- - >>> s = dpnp.random.random(1000) + %(dtype)s + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.rand` - :obj:`dpnp.random.random_sample` - :obj:`dpnp.random.uniform` + :obj:`dpnp.random.RandomState.randn` """ - - return random_sample( - size=size, device=device, usm_type=usm_type, sycl_queue=sycl_queue - ) + rs = _get_random_state(device, sycl_queue) + return rs.standard_normal(args or None, dtype, usm_type=usm_type) -def random_integers( - low, high=None, size=None, device=None, usm_type="device", sycl_queue=None +@_params_doc +def random( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None ): - """ - Random integers between `low` and `high`, inclusive. - - For full documentation refer to :obj:`numpy.random.random_integers`. - - Parameters - ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - `size`-shaped array of random integers from the appropriate distribution, - or a single such random int if `size` is not provided. - - Limitations - ----------- - Parameters `low` and `high` are supported as scalar. - Otherwise, :obj:`numpy.random.random_integers(low, high, size)` samples are drawn. - - See Also - -------- - :obj:`dpnp.random.randint` - - """ - - if high is None: - high = low - low = 0 - - # TODO: - # array_like of floats for `low` and `high` params - if not dpnp.isscalar(low): - pass - elif not dpnp.isscalar(high): - pass - else: - return randint( - low, - int(high) + 1, - size=size, - device=device, - usm_type=usm_type, - sycl_queue=sycl_queue, - ) - - return call_origin(numpy.random.random_integers, low, high, size) - - -def random_sample(size=None, device=None, usm_type="device", sycl_queue=None): - """ - Return random floats in the half-open interval [0.0, 1.0). - - Results are from the “continuous uniform” distribution over the interval. - - For full documentation refer to :obj:`numpy.random.random_sample`. - - Parameters - ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Array of random floats of shape `size` (if ``size=None``, zero dimension array with a single float is returned). - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. - - Examples - -------- - >>> s = dpnp.random.random_sample((5,)) - - See Also - -------- - :obj:`dpnp.random.rand` - :obj:`dpnp.random.random` - :obj:`dpnp.random.uniform` - - """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.random_sample(size=size, usm_type=usm_type) - - -def ranf(size=None, device=None, usm_type="device", sycl_queue=None): """ Return random floats in the half-open interval [0.0, 1.0). - This is an alias of random_sample. - - For full documentation refer to :obj:`numpy.random.ranf`. - - Parameters - ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Array of random floats of shape `size` (if ``size=None``, zero dimension array with a single float is returned). - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. + For full documentation refer to :obj:`numpy.random.random`. - Examples - -------- - >>> s = dpnp.random.ranf(1000) + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.rand` - :obj:`dpnp.random.random` - :obj:`dpnp.random.random_sample` - :obj:`dpnp.random.uniform` + :obj:`dpnp.random.RandomState.random_sample` """ - - return random_sample( - size=size, device=device, usm_type=usm_type, sycl_queue=sycl_queue - ) + rs = _get_random_state(device, sycl_queue) + return rs.random_sample(size, dtype, usm_type=usm_type) -def rayleigh(scale=1.0, size=None): +@_params_doc +def random_integers( + low, + high=None, + size=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from a Rayleigh distribution. + Random integers between `low` and `high`, inclusive. - For full documentation refer to :obj:`numpy.random.rayleigh`. + For full documentation refer to :obj:`numpy.random.random_integers`. - Limitations - ----------- - Parameter `scale` is supported as a scalar. - Otherwise, :obj:`numpy.random.rayleigh(scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> import numpy as np - >>> s = dpnp.random.rayleigh(1.0, 10000) + :obj:`dpnp.random.RandomState.random_integers` """ + # pylint: disable=protected-access + rs = _get_random_state(device, sycl_queue, (low, high)) + return rs._random_integers(low, high, size, usm_type, stacklevel=3) - if not use_origin_backend(scale): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `scale` params - if not dpnp.isscalar(scale): - pass - elif scale < 0: - pass - else: - return _legacy_rng(dpnp_rng_rayleigh, scale, size).get_pyobj() - return call_origin(numpy.random.rayleigh, scale, size) - - -def sample(size=None, device=None, usm_type="device", sycl_queue=None): +@_params_doc +def random_sample( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ Return random floats in the half-open interval [0.0, 1.0). - This is an alias of random_sample. - - For full documentation refer to :obj:`numpy.random.sample`. + For full documentation refer to :obj:`numpy.random.random_sample`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. + %(dtype)s + %(queue)s + %(usm)s - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + See Also + -------- + :obj:`dpnp.random.RandomState.random_sample` - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. + """ + rs = _get_random_state(device, sycl_queue) + return rs.random_sample(size, dtype, usm_type=usm_type) - Default: ``None``. - Returns - ------- - out : dpnp.ndarray - Array of random floats of shape `size` (if ``size=None``, zero dimension array with a single float is returned). - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. +@_params_doc +def ranf( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): + """ + Return random floats in the half-open interval [0.0, 1.0). An alias of + :obj:`dpnp.random.random_sample`. - Examples - -------- - >>> s = dpnp.random.sample(1000) + For full documentation refer to :obj:`numpy.random.ranf`. + + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.rand` - :obj:`dpnp.random.random` - :obj:`dpnp.random.random_sample` - :obj:`dpnp.random.uniform` - - """ - - return random_sample( - size=size, device=device, usm_type=usm_type, sycl_queue=sycl_queue - ) - + :obj:`dpnp.random.RandomState.random_sample` -def shuffle(x1): """ - Modify a sequence in-place by shuffling its contents. + rs = _get_random_state(device, sycl_queue) + return rs.random_sample(size, dtype, usm_type=usm_type) - For full documentation refer to :obj:`numpy.random.shuffle`. - - Limitations - ----------- - Otherwise, the function will use :obj:`numpy.random.shuffle` on the backend - and will be executed on fallback backend. +@_params_doc +def rayleigh( + scale=1.0, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ + Draw samples from a Rayleigh distribution. - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_strides=False) - if x1_desc: + For full documentation refer to :obj:`numpy.random.rayleigh`. - if dpnp.is_cuda_backend(x1_desc.get_array()): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - if not _is_type_supported(x1_desc.dtype): - pass - else: - _legacy_rng(dpnp_rng_shuffle, x1_desc).get_pyobj() - return + See Also + -------- + :obj:`dpnp.random.RandomState.rayleigh` - call_origin(numpy.random.shuffle, x1, dpnp_inplace=True) - return + """ + rs = _get_random_state(device, sycl_queue, (scale,)) + return rs.rayleigh(scale, size, dtype, usm_type=usm_type) -def seed(seed=None, device=None, sycl_queue=None): +@_params_doc +def sample( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ - Reseed a legacy MT19937 random number generator engine. + Return random floats in the half-open interval [0.0, 1.0). An alias of + :obj:`dpnp.random.random_sample`. + + For full documentation refer to :obj:`numpy.random.sample`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. + %(dtype)s + %(queue)s + %(usm)s - Default: ``None``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for an array with generated numbers. + See Also + -------- + :obj:`dpnp.random.RandomState.random_sample` - Default: ``None``. + """ + rs = _get_random_state(device, sycl_queue) + return rs.random_sample(size, dtype, usm_type=usm_type) - Limitations - ----------- - The `seed` parameter is supported as a scalar or an array of at most three - integer scalars. +def shuffle(x): """ + Modify an array in place by shuffling its contents along the first axis. - # update a mt19937 random number for both RandomState and legacy functionality - global _dpnp_random_states # noqa: F824 - - sycl_queue = dpnp.get_normalized_queue_device( - device=device, sycl_queue=sycl_queue - ) - with _random_states_lock: - _dpnp_random_states[sycl_queue] = RandomState( - seed=seed, sycl_queue=sycl_queue - ) + For full documentation refer to :obj:`numpy.random.shuffle`. - if not use_origin_backend(seed): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + The state of the queue of `x` is used. - # TODO: - # array_like of ints for `seed` - if seed is None: - seed = 1 - if not isinstance(seed, int): - pass - elif seed < 0: - pass - else: - # TODO: - # migrate to a single approach with RandomState class - _legacy_rng(dpnp_rng_srand, seed) + See Also + -------- + :obj:`dpnp.random.RandomState.shuffle` - # always reseed numpy engine also - return call_origin(numpy.random.seed, seed, allow_fallback=True) + """ + _get_random_state(arrays=(x,)).shuffle(x) -def standard_cauchy(size=None): +@_params_doc +def standard_cauchy( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from a standard Cauchy distribution with mode = 0. - Also known as the Lorentz distribution. + For full documentation refer to :obj:`numpy.random.standard_cauchy`. - Limitations - ----------- - Output array data type is default float type. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples and plot the distribution: - - >>> import matplotlib.pyplot as plt - >>> s = dpnp.random.standard_cauchy(1000000) - >>> s = s[(s>-25) & (s<25)] # truncate distribution so it plots well - >>> plt.hist(s, bins=100) - >>> plt.show() + :obj:`dpnp.random.RandomState.standard_cauchy` """ + rs = _get_random_state(device, sycl_queue, ()) + return rs.standard_cauchy(size, dtype, usm_type=usm_type) - if not use_origin_backend(size): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - return _legacy_rng(dpnp_rng_standard_cauchy, size).get_pyobj() - - return call_origin(numpy.random.standard_cauchy, size) - -def standard_exponential(size=None): +@_params_doc +def standard_exponential( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from the standard exponential distribution. - `standard_exponential` is identical to the exponential distribution - with a scale parameter of 1. + For full documentation refer to :obj:`numpy.random.standard_exponential`. - Limitations - ----------- - Output array data type is default float type. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Output a 3x8000 array: - - >>> n = dpnp.random.standard_exponential((3, 8000)) + :obj:`dpnp.random.RandomState.standard_exponential` """ + rs = _get_random_state(device, sycl_queue, ()) + return rs.standard_exponential(size, dtype, usm_type=usm_type) - if not use_origin_backend(size): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - return _legacy_rng(dpnp_rng_standard_exponential, size).get_pyobj() - - return call_origin(numpy.random.standard_exponential, size) - -def standard_gamma(shape, size=None): +@_params_doc +def standard_gamma( + shape, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a standard Gamma distribution. For full documentation refer to :obj:`numpy.random.standard_gamma`. - Limitations - ----------- - Parameter `shape` is supported as a scalar. - Otherwise, :obj:`numpy.random.standard_gamma(shape, size)` samples - are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> shape = 2. - >>> s = dpnp.random.standard_gamma(shape, 1000000) + :obj:`dpnp.random.RandomState.standard_gamma` """ - - if not use_origin_backend(shape): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `shape` - if not dpnp.isscalar(shape): - pass - elif shape < 0: - pass - else: - return _legacy_rng(dpnp_rng_standard_gamma, shape, size).get_pyobj() - - return call_origin(numpy.random.standard_gamma, shape, size) + rs = _get_random_state(device, sycl_queue, (shape,)) + return rs.standard_gamma(shape, size, dtype, usm_type=usm_type) -def standard_normal(size=None, device=None, usm_type="device", sycl_queue=None): +@_params_doc +def standard_normal( + size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ - Draw samples from a standard Normal distribution ``(mean=0, stdev=1)``. + Draw samples from a standard Normal distribution (mean=0, stdev=1). For full documentation refer to :obj:`numpy.random.standard_normal`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. + %(dtype)s + %(queue)s + %(usm)s - Returns - ------- - out : dpnp.ndarray - A floating-point array of shape `size` of drawn samples, or a - single sample if `size` was not specified. - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. - - Examples + See Also -------- - Draw samples from the distribution: - - >>> s = dpnp.random.standard_normal(1000) + :obj:`dpnp.random.RandomState.standard_normal` """ + rs = _get_random_state(device, sycl_queue, ()) + return rs.standard_normal(size, dtype, usm_type=usm_type) - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.standard_normal(size=size, usm_type=usm_type) - -def standard_t(df, size=None): +@_params_doc +def standard_t( + df, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from a standard Student’s t distribution with - `df` degrees of freedom. + Draw samples from a standard Student's t distribution with `df` degrees + of freedom. For full documentation refer to :obj:`numpy.random.standard_t`. - Limitations - ----------- - Parameter `df` is supported as a scalar. - Otherwise, :obj:`numpy.random.standard_t(df, size)` samples - are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> df = 2. - >>> s = dpnp.random.standard_t(df, 1000000) + :obj:`dpnp.random.RandomState.standard_t` """ + rs = _get_random_state(device, sycl_queue, (df,)) + return rs.standard_t(df, size, dtype, usm_type=usm_type) - if not use_origin_backend(df): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `df` - if not dpnp.isscalar(df): - pass - elif df <= 0: - pass - else: - return _legacy_rng(dpnp_rng_standard_t, df, size).get_pyobj() - return call_origin(numpy.random.standard_t, df, size) - - -def triangular(left, mode, right, size=None): +@_params_doc +def triangular( + left, + mode, + right, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ - Draw samples from the triangular distribution over the interval - [left, right]. + Draw samples from the triangular distribution over the interval [left, + right]. For full documentation refer to :obj:`numpy.random.triangular`. - Limitations - ----------- - Parameters `left`, `mode` and `right` are supported as scalar. - Otherwise, :obj:`numpy.random.triangular(left, mode, right, size)` - samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> df = 2. - >>> s = dpnp.random.triangular(-3, 0, 8, 1000000) + :obj:`dpnp.random.RandomState.triangular` """ - - if not use_origin_backend(left): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `left`, `mode`, `right`. - if not dpnp.isscalar(left): - pass - elif not dpnp.isscalar(mode): - pass - elif not dpnp.isscalar(right): - pass - elif left > mode: - pass - elif mode > right: - pass - elif left == right: - pass - else: - return _legacy_rng( - dpnp_rng_triangular, left, mode, right, size - ).get_pyobj() - - return call_origin(numpy.random.triangular, left, mode, right, size) + rs = _get_random_state(device, sycl_queue, (left, mode, right)) + return rs.triangular(left, mode, right, size, dtype, usm_type=usm_type) +@_params_doc def uniform( low=0.0, high=1.0, size=None, + dtype=None, + *, device=None, usm_type="device", sycl_queue=None, ): """ - Draw samples from a uniform distribution. - - Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). - In other words, any value within the given interval is equally likely to be drawn by uniform. + Draw samples from a uniform distribution over [low, high). For full documentation refer to :obj:`numpy.random.uniform`. Parameters ---------- - device : {None, string, SyclDevice, SyclQueue, Device}, optional - An array API concept of device where the output array is created. - `device` can be ``None``, a oneAPI filter selector string, an instance - of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL - device, an instance of :class:`dpctl.SyclQueue`, or a - :class:`dpnp.tensor.Device` object returned by - :attr:`dpnp.ndarray.device`. - - Default: ``None``. - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. - - Default: ``"device"``. - sycl_queue : {None, SyclQueue}, optional - A SYCL queue to use for output array allocation and copying. The - `sycl_queue` can be passed as ``None`` (the default), which means - to get the SYCL queue from `device` keyword if present or to use - a default queue. - - Default: ``None``. - - Returns - ------- - out : dpnp.ndarray - Drawn samples from the parameterized uniform distribution. - Output array data type is the same as input `dtype`. If `dtype` is ``None`` (the default), - :obj:`dpnp.float64` type will be used if device supports it, or :obj:`dpnp.float32` otherwise. - - Limitations - ----------- - Parameters `low` and `high` are supported as a scalar. Otherwise, - :obj:`numpy.random.uniform(low, high, size)` samples are drawn. - Parameter `dtype` is supported only as :obj:`dpnp.int32`, :obj:`dpnp.float32`, :obj:`dpnp.float64` or ``None``. - - Examples - -------- - Draw samples from the distribution: - - >>> low, high = 0, 0.1 # low and high - >>> s = dpnp.random.uniform(low, high, 10000) + %(dtype)s + %(queue)s + %(usm)s See Also -------- - :obj:`dpnp.random.random` : Floats uniformly distributed over ``[0, 1)``. + :obj:`dpnp.random.RandomState.uniform` """ - - rs = _get_random_state(device=device, sycl_queue=sycl_queue) - return rs.uniform( - low=low, high=high, size=size, dtype=None, usm_type=usm_type - ) + rs = _get_random_state(device, sycl_queue, (low, high)) + return rs.uniform(low, high, size, dtype, usm_type=usm_type) -def vonmises(mu, kappa, size=None): +@_params_doc +def vonmises( + mu, + kappa, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a von Mises distribution. For full documentation refer to :obj:`numpy.random.vonmises`. - Limitations - ----------- - Parameters `mu` and `kappa` are supported as scalar. - Otherwise, :obj:`numpy.random.vonmises(mu, kappa, size)` - samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - Draw samples from the distribution: - - >>> mu, kappa = 0.0, 4.0 # mean and dispersion - >>> s = dpnp.random.vonmises(mu, kappa, 1000) + :obj:`dpnp.random.RandomState.vonmises` """ - - if not use_origin_backend(mu): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `mu`, `kappa`. - if not dpnp.isscalar(mu): - pass - elif not dpnp.isscalar(kappa): - pass - elif numpy.isnan(kappa): - return dpnp.nan - elif kappa < 0: - pass - else: - return _legacy_rng(dpnp_rng_vonmises, mu, kappa, size).get_pyobj() - - return call_origin(numpy.random.vonmises, mu, kappa, size) + rs = _get_random_state(device, sycl_queue, (mu, kappa)) + return rs.vonmises(mu, kappa, size, dtype, usm_type=usm_type) -def wald(mean, scale, size=None): +@_params_doc +def wald( + mean, + scale, + size=None, + dtype=None, + *, + device=None, + usm_type="device", + sycl_queue=None, +): """ Draw samples from a Wald, or inverse Gaussian, distribution. For full documentation refer to :obj:`numpy.random.wald`. - Limitations - ----------- - Parameters `mean` and `scale` are supported as scalar. - Otherwise, :obj:`numpy.random.wald(mean, scale, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - >>> loc, scale = 3., 2. - >>> s = dpnp.random.wald(loc, scale, 1000) + :obj:`dpnp.random.RandomState.wald` """ + rs = _get_random_state(device, sycl_queue, (mean, scale)) + return rs.wald(mean, scale, size, dtype, usm_type=usm_type) - if not use_origin_backend(mean): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `mean` and `scale` - if not dpnp.isscalar(mean): - pass - elif not dpnp.isscalar(scale): - pass - elif mean <= 0: - pass - elif scale <= 0: - pass - else: - return _legacy_rng(dpnp_rng_wald, mean, scale, size).get_pyobj() - return call_origin(numpy.random.wald, mean, scale, size) - - -def weibull(a, size=None): +@_params_doc +def weibull( + a, size=None, dtype=None, *, device=None, usm_type="device", sycl_queue=None +): """ Draw samples from a Weibull distribution. For full documentation refer to :obj:`numpy.random.weibull`. - Limitations - ----------- - Parameter `a` is supported as a scalar. - Otherwise, :obj:`numpy.random.weibull(a, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(dtype)s + %(queue)s + %(usm)s - Examples + See Also -------- - >>> a = 5. # shape - >>> s = np.random.weibull(a, 1000) + :obj:`dpnp.random.RandomState.weibull` """ - - if not use_origin_backend(a): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `a` param - if not dpnp.isscalar(a): - pass - elif a < 0: - pass - else: - return _legacy_rng(dpnp_rng_weibull, a, size).get_pyobj() - - return call_origin(numpy.random.weibull, a, size) + rs = _get_random_state(device, sycl_queue, (a,)) + return rs.weibull(a, size, dtype, usm_type=usm_type) -def zipf(a, size=None): +@_params_doc +def zipf(a, size=None, *, device=None, usm_type="device", sycl_queue=None): """ - Returns an array of samples drawn from the Zipf distribution. + Draw samples from a Zipf distribution. For full documentation refer to :obj:`numpy.random.zipf`. - Limitations - ----------- - Parameter `a`` is supported as a scalar. - Otherwise, :obj:`numpy.zipf.weibull(a, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Parameters + ---------- + %(queue)s + %(usm)s - Examples + See Also -------- - >>> a = 2. # parameter - >>> s = np.random.zipf(a, 1000) + :obj:`dpnp.random.RandomState.zipf` """ - - if not use_origin_backend(a): - if dpnp.is_cuda_backend(): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) - - # TODO: - # array_like of floats for `a` param - if not dpnp.isscalar(a): - pass - elif a <= 1: - pass - else: - return _legacy_rng(dpnp_rng_zipf, a, size).get_pyobj() - - return call_origin(numpy.random.zipf, a, size) - - -_dpnp_random_states = {} + rs = _get_random_state(device, sycl_queue, (a,)) + return rs.zipf(a, size, usm_type=usm_type) diff --git a/dpnp/random/dpnp_random_state.py b/dpnp/random/dpnp_random_state.py index d762c299c1f..d5a41810d89 100644 --- a/dpnp/random/dpnp_random_state.py +++ b/dpnp/random/dpnp_random_state.py @@ -1,4 +1,3 @@ -# cython: language_level=3 # ***************************************************************************** # Copyright (c) 2016, Intel Corporation # All rights reserved. @@ -28,38 +27,138 @@ # ***************************************************************************** """ -Module Intel NumPy RandomState +The legacy random number generation API modelled on +:class:`numpy.random.RandomState`. -Set of functions to implement NumPy random module API - - .. seealso:: :obj:`numpy.random.RandomState` +A :class:`RandomState` draws from a :class:`dpnp.random.Philox4x3210` bit +generator, and samples every distribution on the device by the algorithms of +:class:`dpnp.random.Generator`, with the parameter checks of NumPy's legacy +API. Neither the values nor the state are those of NumPy's MT19937. """ +# pylint: disable=protected-access +# pylint: disable=too-many-public-methods + +import operator +import warnings + import numpy import dpnp -from dpnp.dpnp_utils.dpnp_algo_utils import ( - call_origin, - map_dtype_to_device, - use_origin_backend, +import dpnp.libtensor._rng_impl as ri # pylint: disable=no-name-in-module + +from ._bit_generator import BitGenerator, Philox4x3210 +from ._generator import ( + _CONS_BOUNDED_0_1, + _CONS_GTE_1, + _CONS_NON_NEGATIVE, + _CONS_POSITIVE, + _HYPERGEOM_MAX, + _INT64_MAX, + Generator, + _as_int64, + _check_arrays, + _check_constraint, + _int64, + _is_array, + _is_scalar_path, + _normalize_size, + _uniform_range, ) -from dpnp.random.dpnp_algo_random import MCG59, MT19937 -from dpnp.tensor import validate_usm_type + +__all__ = ["RandomState"] + +# the messages of RandomState.choice +_CHOICE_MSGS = { + "a_type": "a must be 1-dimensional or an integer", + "a_int": "a must be greater than 0 unless no samples are taken", + "a_1d": "a must be 1-dimensional", + "a_empty": "'a' cannot be empty unless no samples are taken", + "p_1d": "'p' must be 1-dimensional", + "p_size": "'a' and 'p' must have same size", + "p_nan": "probabilities contain NaN", + "p_neg": "probabilities are not non-negative", + "p_sum": "probabilities do not sum to 1", + "too_large": ( + "Cannot take a larger sample than population when 'replace=False'" + ), +} + +_SEED_MAX = 2**32 - 1 + + +class _LegacyGenerator(Generator): + """ + The generator of a :class:`RandomState`, which checks the parameters on + the device as NumPy's legacy API checks all parameters, at the cost of + waiting for the device. + + """ + + _check_device = True + + +def _legacy_seed(seed): + """ + Check `seed` as NumPy's legacy seeding does, and return it in a form + :class:`numpy.random.SeedSequence` takes. + + """ + + if seed is None: + return None + if dpnp.is_supported_array_type(seed): + seed = dpnp.asnumpy(seed) + + msg = "Seed must be between 0 and 2**32 - 1" + try: + if hasattr(seed, "squeeze"): + seed = seed.squeeze() + idx = operator.index(seed) + except TypeError: + obj = numpy.asarray(seed) + if obj.size == 0: + raise ValueError("Seed must be non-empty") from None + obj = obj.astype(numpy.int64, casting="safe") + if obj.ndim != 1: + raise ValueError("Seed array must be 1-d") from None + if ((obj > _SEED_MAX) | (obj < 0)).any(): + raise ValueError(msg) from None + return obj + if idx > _SEED_MAX or idx < 0: + raise ValueError(msg) + return idx + + +def _reseed_in_place(bit_generator, seeded): + """ + Give `bit_generator` the state and seed sequence of `seeded`, a new bit + generator of the same type, so that every user of `bit_generator` sees + the new seed, as NumPy reseeds its bit generators in place. + + """ + + # pylint: disable=protected-access + bit_generator.state = seeded.state + bit_generator._seed_seq = seeded.seed_seq class RandomState: """ - A container for the Mersenne Twister pseudo-random number generator. + A container of a pseudo-random number generator on a SYCL device, with + the legacy API of NumPy. For full documentation refer to :obj:`numpy.random.RandomState`. Parameters ---------- - seed : {None, int, array_like}, optional - A random seed to initialize the pseudo-random number generator. - The `seed` can be ``None`` (the default), an integer scalar, or - an array of at most three integer scalars. + seed : {None, int, array_like[ints], BitGenerator}, optional + A random seed to initialize the pseudo-random number generator, as + for :obj:`numpy.random.RandomState`: an integer in [0, 2**32 - 1] or + a 1-D array of such integers. With ``None``, fresh entropy is pulled + from the operating system. A :class:`dpnp.random.BitGenerator` is + used as the bit generator, on its own queue. Default: ``None``. device : {None, string, SyclDevice, SyclQueue, Device}, optional @@ -79,602 +178,809 @@ class RandomState: Default: ``None``. + Limitations + ----------- + Unless `seed` is a :class:`dpnp.random.BitGenerator`, the bit generator + is :class:`dpnp.random.Philox4x3210` rather than MT19937, so the values + drawn differ from NumPy's, and the state is that of + :class:`dpnp.random.Philox4x3210`. `device` and `sycl_queue` cannot be + given with a :class:`dpnp.random.BitGenerator`. The values for a seed + are the same on every device, and those of + ``dpnp.random.default_rng(seed)`` for the distributions whose checks + agree. + + Every method returns a :class:`dpnp.ndarray`, zero-dimensional when + `size` is ``None`` and the parameters are scalars, rather than a Python + scalar. The floating-point methods take a `dtype`, which defaults to the + default floating-point type of the device, and every method a + `usm_type`, the type of USM allocation of the output. Parameters that + are on the device are checked as NumPy checks all parameters, so a call + with such parameters waits for the device, unlike the methods of + :class:`dpnp.random.Generator`. + """ def __init__(self, seed=None, device=None, sycl_queue=None): + if isinstance(seed, BitGenerator): + if device is not None or sycl_queue is not None: + raise ValueError( + "device and sycl_queue cannot be given with a " + "BitGenerator, whose queue is used" + ) + self._sycl_queue = seed.sycl_queue + self._generator = _LegacyGenerator(seed) + return + self._sycl_queue = dpnp.get_normalized_queue_device( device=device, sycl_queue=sycl_queue ) + self._generator = _LegacyGenerator(self._make_bit_generator(seed)) - self._sycl_device = self._sycl_queue.sycl_device - - is_cpu = self._sycl_device.is_cpu - if seed is None: - low = 0 - high = dpnp.iinfo(numpy.int32).max + 1 - - if is_cpu: - # ask NumPy to generate an array of three random integers as default seed value - self._seed = numpy.random.randint(low=low, high=high, size=3) - else: - # ask NumPy to generate a random 32-bit integer as default seed value - self._seed = numpy.random.randint(low=low, high=high, size=1)[0] - else: - self._seed = seed - - # 'float32' is default floating data type if device doesn't support 'float64' - self._def_float_type = map_dtype_to_device( - dpnp.float64, self._sycl_device - ) - - # TODO: rework through pybind11 extension for OneMKL engine and distribution classes - if is_cpu: - self._random_state = MT19937(self._seed, self._sycl_queue) - else: - # MCG59 is assumed to provide a better performance on GPU than MT19937 - self._random_state = MCG59(self._seed, self._sycl_queue) - - self._fallback_random_state = call_origin( - numpy.random.RandomState, seed, allow_fallback=True - ) + def _make_bit_generator(self, seed): + return Philox4x3210(_legacy_seed(seed), sycl_queue=self._sycl_queue) def __repr__(self): return self.__str__() + f" at 0x{id(self):X}" def __str__(self): - _str = self.__class__.__name__ - _str += "(" + self._random_state.__class__.__name__ + ")" - return _str + bit_generator = self._generator.bit_generator + return f"{self.__class__.__name__}({type(bit_generator).__name__})" - def __getstate__(self): - return self.get_state() + def _set_bit_generator(self, bit_generator): + """Draw from `bit_generator`, which is on the queue of the state.""" + self._generator.bit_generator = bit_generator - def _is_finite_scalar(self, x): - """Test a scalar for finiteness (not infinity and not Not a Number).""" - - # TODO: replace with dpnp.isfinite() once function is available in DPNP, - # but for now use direct numpy calls without call_origin() wrapper, since data is a scalar - return numpy.isfinite(x) + def seed(self, seed=None): + """ + Reseed the generator. - def _is_signbit_scalar(self, x): - """Test a scalar if sign bit is set for it (less than zero).""" + For full documentation refer to :obj:`numpy.random.RandomState.seed`. - # TODO: replace with dpnp.signbit() once function is available in DPNP, - # but for now use direct numpy calls without call_origin() wrapper, since data is a scalar - return numpy.signbit(x) + Limitations + ----------- + As NumPy reseeds only its legacy MT19937, only a + :class:`dpnp.random.Philox4x3210` bit generator can be reseeded. - def _validate_float_dtype(self, dtype, supported_types): """ - Validate an input floating type. + bit_generator = self._generator.bit_generator + if type(bit_generator) is not Philox4x3210: + raise TypeError("can only re-seed a Philox4x3210 BitGenerator") + _reseed_in_place(bit_generator, self._make_bit_generator(seed)) - Test an input floating type if it is listed in `supported_types` and - if it is supported by the used SYCL device. - If `dtype` is ``None``, default floating type will be validating. - Return the examined floating type if it follows all validation checks. + def get_state(self, legacy=True): """ + Return the state of the generator. - if dtype is None: - dtype = self._def_float_type + For full documentation refer to + :obj:`numpy.random.RandomState.get_state`. - if dtype not in supported_types: - raise TypeError(f"dtype={dtype} is unsupported.") - elif dtype != map_dtype_to_device(dtype, self._sycl_device): - raise RuntimeError( - f"dtype={dtype} is not supported by SYCL device '{self._sycl_device}'" - ) - return dtype + Limitations + ----------- + The state is always the dictionary of the + :attr:`dpnp.random.BitGenerator.state` of the bit generator, + whatever `legacy` is. - def get_state(self): """ - Return an internal state of the generator. + # pylint: disable=unused-argument + return self._generator.bit_generator.state - For full documentation refer to :obj:`numpy.random.RandomState.get_state`. + def set_state(self, state): + """ + Set the state of the generator. - Returns - ------- - out : object - An object representing the internal state of the generator. + For full documentation refer to + :obj:`numpy.random.RandomState.set_state`. + + Limitations + ----------- + `state` must be a state of the bit generator, as returned by + :meth:`get_state`. - Notes - ----- - Unlike :obj:`numpy.random.RandomState.get_state`, which returns a - snapshot of the state as plain data, this returns the live engine the - generator draws from. Generating from the returned engine advances the - state seen by this :class:`RandomState`, and does so safely if other - threads are drawing from it at the same time. """ - return self._random_state + bit_generator = self._generator.bit_generator + if not isinstance(state, dict): + raise ValueError( + f"state must be for a {type(bit_generator).__name__} PRNG" + ) + bit_generator.state = state def get_sycl_queue(self): """ - Return an instance of :class:`dpctl.SyclQueue` used within the generator for data allocation. + Return the :class:`dpctl.SyclQueue` the generator allocates data on. Returns ------- queue : dpctl.SyclQueue A SYCL queue used for data allocation. + """ return self._sycl_queue def get_sycl_device(self): """ - Return an instance of :class:`dpctl.SyclDevice` used within the generator to allocate data on. + Return the :class:`dpctl.SyclDevice` the generator allocates data on. Returns ------- device : dpctl.SyclDevice A SYCL device used to allocate data on. + """ - return self._sycl_device + return self._sycl_queue.sycl_device - def normal( - self, loc=0.0, scale=1.0, size=None, dtype=None, usm_type="device" + # Simple random data + + def rand(self, *args, dtype=None, usm_type="device"): + """ + Random values in a given shape, from a uniform distribution over + [0, 1). + + For full documentation refer to :obj:`numpy.random.RandomState.rand`. + + """ + return self.random_sample(args or None, dtype, usm_type=usm_type) + + def randn(self, *args, dtype=None, usm_type="device"): + """ + Samples of the standard normal distribution in a given shape. + + For full documentation refer to :obj:`numpy.random.RandomState.randn`. + + """ + return self.standard_normal(args or None, dtype, usm_type=usm_type) + + def randint( + self, low, high=None, size=None, dtype=int, *, usm_type="device" ): """ - Draw random samples from a normal (Gaussian) distribution. + Random integers from `low` (inclusive) to `high` (exclusive). - For full documentation refer to :obj:`numpy.random.RandomState.normal`. + For full documentation refer to + :obj:`numpy.random.RandomState.randint`. - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + Limitations + ----------- + ``dtype=int`` gives ``int64`` values, not a C ``long``, which is + ``int32`` on Windows. - Default: ``"device"``. + """ + return self._generator._integers( + low, high, size, dtype, False, usm_type, "randint" + ) - Returns - ------- - out : dpnp.ndarray - Drawn samples from the parameterized normal distribution. - Output array data type is the same as input `dtype`. If `dtype` is ``None`` (the default), - :obj:`dpnp.float64` type will be used if device supports it, or :obj:`dpnp.float32` otherwise. + def random_integers(self, low, high=None, size=None, *, usm_type="device"): + """ + Random integers from `low` to `high`, both inclusive. + + For full documentation refer to + :obj:`numpy.random.RandomState.random_integers`. Limitations ----------- - Parameters `loc` and `scale` are supported as a scalar. Otherwise, - :obj:`numpy.random.RandomState.normal(loc, scale, size)` samples are drawn. - Parameter `dtype` is supported only as :obj:`dpnp.float32`, :obj:`dpnp.float64` or ``None``. + The values are ``int64``, not a C ``long``, which is ``int32`` on + Windows. + + """ + return self._random_integers(low, high, size, usm_type, stacklevel=3) + + def _random_integers(self, low, high, size, usm_type, stacklevel): + if high is None: + warnings.warn( + "This function is deprecated. Please call " + f"randint(1, {low} + 1) instead", + DeprecationWarning, + stacklevel=stacklevel, + ) + high = low + low = 1 + else: + warnings.warn( + "This function is deprecated. Please call " + f"randint({low}, {high} + 1) instead", + DeprecationWarning, + stacklevel=stacklevel, + ) + if dpnp.is_supported_array_type(high): + high = high + 1 + elif _is_array(high): + high = numpy.array(high) + 1 + else: + high = int(high) + 1 + return self.randint(low, high, size, _int64, usm_type=usm_type) + + def random_sample(self, size=None, dtype=None, *, usm_type="device"): + """ + Random floats in the half-open interval [0.0, 1.0). - Examples - -------- - >>> s = dpnp.random.RandomState().normal(loc=3.7, scale=2.5, size=(2, 4)) - >>> print(s) - [[ 1.58997253 -0.84288406 2.33836967 4.16394577] - [ 4.40882036 5.39295758 6.48927254 6.74921661]] + For full documentation refer to + :obj:`numpy.random.RandomState.random_sample`. - See Also - -------- - :obj:`dpnp.random.RandomState.randn` - :obj:`dpnp.random.RandomState.standard_normal` + """ + return self._generator.random(size, dtype, usm_type=usm_type) + def random(self, size=None, dtype=None, *, usm_type="device"): """ + Random floats in the half-open interval [0.0, 1.0). - if not use_origin_backend(): - if dpnp.is_cuda_backend(self): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + For full documentation refer to :obj:`numpy.random.RandomState.random`. - if not dpnp.isscalar(loc): - pass - elif not dpnp.isscalar(scale): - pass - else: - dtype = self._validate_float_dtype( - dtype, (dpnp.float32, dpnp.float64) - ) - min_floating = dpnp.finfo(dtype).min - max_floating = dpnp.finfo(dtype).max - - if ( - loc >= max_floating or loc <= min_floating - ) and self._is_finite_scalar(loc): - raise OverflowError( - f"Range of loc={loc} exceeds valid bounds" - ) - - if (scale >= max_floating) and self._is_finite_scalar(scale): - raise OverflowError( - f"Range of scale={scale} exceeds valid bounds" - ) - # scale = -0.0 is cosidered as negative - elif scale < 0 or scale == 0 and self._is_signbit_scalar(scale): - raise ValueError( - f"scale={scale}, but must be non-negative." - ) - - validate_usm_type(usm_type, allow_none=False) - return self._random_state.normal( - loc=loc, - scale=scale, - size=size, - dtype=dtype, - usm_type=usm_type, - ).get_pyobj() - - return call_origin( - self._fallback_random_state.normal, - loc=loc, - scale=scale, - size=size, - sycl_queue=self._sycl_queue, + """ + return self._generator.random(size, dtype, usm_type=usm_type) + + def tomaxint(self, size=None, *, usm_type="device"): + """ + Random integers from 0 to the largest ``int64``, both inclusive. + + For full documentation refer to + :obj:`numpy.random.RandomState.tomaxint`. + + Limitations + ----------- + The values are ``int64``, not a C ``long``, which is ``int32`` on + Windows. + + """ + return self._generator._integers( + 0, _INT64_MAX, size, _int64, True, usm_type, "tomaxint" ) - def rand(self, *args, usm_type="device"): + def choice(self, a, size=None, replace=True, p=None, *, usm_type="device"): """ - Draw random values in a given shape. + Generate a random sample from a given 1-D array. - Create an array of the given shape and populate it with random samples - from a uniform distribution over [0, 1). + For full documentation refer to :obj:`numpy.random.RandomState.choice`. - For full documentation refer to :obj:`numpy.random.RandomState.rand`. + """ + return self._generator._choice( + a, size, replace, p, 0, usm_type, _CHOICE_MSGS + ) - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + def bytes(self, length): + """ + Return random bytes. - Default: ``"device"``. + For full documentation refer to :obj:`numpy.random.RandomState.bytes`. - Returns - ------- - out : dpnp.ndarray - Random values in a given shape. - Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise. + """ + return self._generator.bytes(length) - Examples - -------- - >>> s = dpnp.random.RandomState().rand(5, 2) - >>> print(s) - [[0.13436424 0.56920387] - [0.84743374 0.80226506] - [0.76377462 0.06310682] - [0.25506903 0.1179187 ] - [0.49543509 0.76096244]] + # Permutations - See Also - -------- - :obj:`dpnp.random.RandomState.random_sample` - :obj:`dpnp.random.RandomState.uniform` + def shuffle(self, x): + """ + Modify an array in place by shuffling its contents along the first + axis. + + For full documentation refer to + :obj:`numpy.random.RandomState.shuffle`. + + Limitations + ----------- + As in CuPy, `x` must be a :class:`dpnp.ndarray`. """ + self._generator.shuffle(x) - if len(args) == 0: - return self.random_sample(usm_type=usm_type) - else: - return self.random_sample(size=args, usm_type=usm_type) + def permutation(self, x): + """ + Randomly permute a sequence, or return a permuted range. + + For full documentation refer to + :obj:`numpy.random.RandomState.permutation`. - def randint(self, low, high=None, size=None, dtype=int, usm_type="device"): """ - Draw random integers from `low` (inclusive) to `high` (exclusive). + if not isinstance(x, (int, numpy.integer)) and numpy.ndim(x) == 0: + raise IndexError("x must be an integer or at least 1-dimensional") + return self._generator.permutation(x) - Return random integers from the “discrete uniform” distribution of the specified type - in the “half-open” interval [low, high). + # Distributions - For full documentation refer to :obj:`numpy.random.RandomState.randint`. + def beta(self, a, b, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a Beta distribution. - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + For full documentation refer to :obj:`numpy.random.RandomState.beta`. - Default: ``"device"``. + """ + return self._generator.beta(a, b, size, dtype, usm_type=usm_type) - Returns - ------- - out : dpnp.ndarray - `size`-shaped array of random integers from the appropriate distribution, - or a single such random int if `size` is not provided. - Output array data type is the same as input `dtype`. + def binomial(self, n, p, size=None, *, usm_type="device"): + """ + Draw samples from a binomial distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.binomial`. + + """ + return self._generator.binomial(n, p, size, usm_type=usm_type) + + def chisquare(self, df, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a chi-square distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.chisquare`. + + """ + return self._generator.chisquare(df, size, dtype, usm_type=usm_type) + + def dirichlet(self, alpha, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from the Dirichlet distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.dirichlet`. + + """ + self._generator._check_queues(alpha) + if dpnp.is_supported_array_type(alpha): + # the Generator copies alpha to the host anyway + alpha = dpnp.asnumpy(alpha) + if numpy.ndim(alpha) == 1: + if numpy.any(numpy.asarray(alpha, dtype=numpy.float64) <= 0): + raise ValueError("alpha <= 0") + return self._generator.dirichlet(alpha, size, dtype, usm_type=usm_type) + + def exponential( + self, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from an exponential distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.exponential`. + + """ + return self._generator.exponential( + scale, size, dtype, usm_type=usm_type + ) + + def f(self, dfnum, dfden, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from an F distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.f`. + + """ + return self._generator.f(dfnum, dfden, size, dtype, usm_type=usm_type) + + def gamma( + self, shape, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from a Gamma distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.gamma`. + + """ + return self._generator.gamma( + shape, scale, size, dtype, usm_type=usm_type + ) + + def geometric(self, p, size=None, *, usm_type="device"): + """ + Draw samples from the geometric distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.geometric`. + + """ + return self._generator.geometric(p, size, usm_type=usm_type) + + def gumbel( + self, loc=0.0, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from a Gumbel distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.gumbel`. + + """ + return self._generator.gumbel( + loc, scale, size, dtype, usm_type=usm_type + ) + + def hypergeometric( + self, ngood, nbad, nsample, size=None, *, usm_type="device" + ): + """ + Draw samples from a hypergeometric distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.hypergeometric`. Limitations ----------- - Parameters `low` and `high` are supported only as a scalar. - Parameter `dtype` is supported only as :obj:`dpnp.int32` or ``int``, - but ``int`` value is considered to be exactly equivalent to :obj:`dpnp.int32`. - Otherwise, :obj:`numpy.random.RandomState.randint(low, high, size, dtype)` samples are drawn. + As in :meth:`dpnp.random.Generator.hypergeometric`, `ngood` and + `nbad` must be less than 10**9. - Examples - -------- - >>> s = dpnp.random.RandomState().randint(2, size=10) - >>> print(s) - [0 1 1 1 1 0 0 0 0 1] + """ + self._generator._check_queues(ngood, nbad, nsample) + params = tuple(_as_int64(x) for x in (ngood, nbad, nsample)) + xp, (good, bad, sample) = _check_arrays(params, True) + if xp.any(good + bad < sample): + raise ValueError("ngood + nbad < nsample") + scalar = _is_scalar_path(*params) + for x, name, cons in zip( + params, + ("ngood", "nbad", "nsample"), + (_CONS_NON_NEGATIVE, _CONS_NON_NEGATIVE, _CONS_GTE_1), + ): + _check_constraint(x, name, cons, scalar, device=True) + if xp.any(good >= _HYPERGEOM_MAX) or xp.any(bad >= _HYPERGEOM_MAX): + raise ValueError( + f"both ngood and nbad must be less than {_HYPERGEOM_MAX}" + ) + return self._generator._discrete_sample( + ri._hypergeometric, + params, + size, + usm_type, + param_types=(_int64,) * 3, + ) - See Also - -------- - :obj:`dpnp.random.RandomState.random_integers` : similar to `randint`, only for the closed - interval [`low`, `high`], and 1 is the lowest value if `high` is omitted. + def laplace( + self, loc=0.0, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from the Laplace or double exponential distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.laplace`. """ + return self._generator.laplace( + loc, scale, size, dtype, usm_type=usm_type + ) - if not use_origin_backend(low): - if dpnp.is_cuda_backend(self): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + def logistic( + self, loc=0.0, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from a logistic distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.logistic`. - if not dpnp.isscalar(low): - pass - elif not (high is None or dpnp.isscalar(high)): - pass - else: - _dtype = dpnp.int32 if dtype is int else dpnp.dtype(dtype) - if _dtype != dpnp.int32: - pass - else: - if high is None: - high = low - low = 0 - - min_int = dpnp.iinfo("int32").min - max_int = dpnp.iinfo("int32").max - - if ( - not self._is_finite_scalar(low) - or low > max_int - or low < min_int - ): - raise OverflowError( - f"Range of low={low} exceeds valid bounds" - ) - elif ( - not self._is_finite_scalar(high) - or high > max_int - or high < min_int - ): - raise OverflowError( - f"Range of high={high} exceeds valid bounds" - ) - - low = int(low) - high = int(high) - if low >= high: - raise ValueError(f"low={low} >= high={high}") - - return self.uniform( - low=low, - high=high, - size=size, - dtype=_dtype, - usm_type=usm_type, - ) - - return call_origin( - self._fallback_random_state.randint, - low=low, - high=high, - size=size, - dtype=dtype, - sycl_queue=self._sycl_queue, + """ + return self._generator.logistic( + loc, scale, size, dtype, usm_type=usm_type ) - def randn(self, *args, usm_type="device"): + def lognormal( + self, mean=0.0, sigma=1.0, size=None, dtype=None, *, usm_type="device" + ): """ - Return a sample (or samples) from the "standard normal" distribution. + Draw samples from a log-normal distribution. - For full documentation refer to :obj:`numpy.random.RandomState.randn`. + For full documentation refer to + :obj:`numpy.random.RandomState.lognormal`. - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + """ + return self._generator.lognormal( + mean, sigma, size, dtype, usm_type=usm_type + ) - Default: ``"device"``. + def logseries(self, p, size=None, *, usm_type="device"): + """ + Draw samples from a logarithmic series distribution. - Returns - ------- - out : dpnp.ndarray - A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from - the standard normal distribution, or a single such float if - no parameters were supplied. - Output array data type is :obj:`dpnp.float64` if device supports it, - or :obj:`dpnp.float32` otherwise. + For full documentation refer to + :obj:`numpy.random.RandomState.logseries`. + + """ + return self._generator.logseries(p, size, usm_type=usm_type) + + def multinomial(self, n, pvals, size=None, *, usm_type="device"): + """ + Draw samples from a multinomial distribution. - Examples - -------- - >>> s = dpnp.random.RandomState().randn() - >>> print(s) - -0.84401099 + For full documentation refer to + :obj:`numpy.random.RandomState.multinomial`. - Two-by-four array of samples from the normal distribution with - mean 3 and standard deviation 2.5: + """ + self._generator._check_queues(n, pvals) + t = type(n) + if _is_array(n) or not ( + hasattr(t, "__int__") or hasattr(t, "__index__") + ): + raise TypeError("an integer is required") + n = int(n) + if not dpnp.is_supported_array_type(pvals): + pvals = numpy.asarray(pvals, dtype=numpy.float64) + ndim = pvals.ndim + if ndim == 0: + raise TypeError("pvals must be a 1-d sequence") + if ndim > 1: + raise ValueError( + "setting an array element with a sequence. The requested " + "array would exceed the maximum number of dimension of 1." + ) + if len(pvals) == 0: + size = _normalize_size(size) + shape = (() if size is None else size) + (0,) + return dpnp.empty( + shape, + dtype=_int64, + usm_type=usm_type, + sycl_queue=self._sycl_queue, + ) + return self._generator.multinomial(n, pvals, size, usm_type=usm_type) + + def multivariate_normal( + self, + mean, + cov, + size=None, + check_valid="warn", + tol=1e-8, + *, + dtype=None, + usm_type="device", + ): + """ + Draw random samples from a multivariate normal distribution. - >>> s = dpnp.random.RandomState().randn(2, 4) - >>> print(s) - [[ 0.88997253 -1.54288406 1.63836967 3.46394577] - [ 3.70882036 4.69295758 5.78927254 6.04921661]] + For full documentation refer to + :obj:`numpy.random.RandomState.multivariate_normal`. - See Also - -------- - :obj:`dpnp.random.normal` - :obj:`dpnp.random.standard_normal` + Limitations + ----------- + The covariance matrix is factorized as in + :meth:`dpnp.random.Generator.multivariate_normal`. """ + return self._multivariate_normal( + mean, cov, size, check_valid, tol, dtype, usm_type, stacklevel=3 + ) - if len(args) == 0: - return self.standard_normal(usm_type=usm_type) - return self.standard_normal(size=args, usm_type=usm_type) + def _multivariate_normal( + self, mean, cov, size, check_valid, tol, dtype, usm_type, stacklevel + ): + return self._generator._multivariate_normal( + mean, + cov, + size, + check_valid, + tol, + "svd", + dtype, + usm_type, + stacklevel=stacklevel + 1, + ) - def random_sample(self, size=None, usm_type="device"): + def negative_binomial(self, n, p, size=None, *, usm_type="device"): """ - Draw random floats in the half-open interval [0.0, 1.0). + Draw samples from a negative binomial distribution. - Results are from the “continuous uniform” distribution over the interval. + For full documentation refer to + :obj:`numpy.random.RandomState.negative_binomial`. - For full documentation refer to :obj:`numpy.random.RandomState.random_sample`. + Limitations + ----------- + As in NumPy, the values are undefined when ``(1 - p) / p * n`` is + too large for the Poisson distribution, as for ``p == 0``. - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + """ + self._generator._check_queues(n, p) + scalar = _is_scalar_path(n, p) + _check_constraint(n, "n", _CONS_POSITIVE, scalar, device=True) + _check_constraint(p, "p", _CONS_BOUNDED_0_1, scalar, device=True) + return self._generator._discrete_sample( + ri._negative_binomial, (n, p), size, usm_type + ) - Default: ``"device"``. + def noncentral_chisquare( + self, df, nonc, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from a noncentral chi-square distribution. - Returns - ------- - out : dpnp.ndarray - Array of random floats of shape `size` (if ``size=None``, - zero dimension array with a single float is returned). - Output array data type is :obj:`dpnp.float64` if device supports it, - or :obj:`dpnp.float32` otherwise. + For full documentation refer to + :obj:`numpy.random.RandomState.noncentral_chisquare`. + + """ + return self._generator.noncentral_chisquare( + df, nonc, size, dtype, usm_type=usm_type + ) + + def noncentral_f( + self, dfnum, dfden, nonc, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw samples from the noncentral F distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.noncentral_f`. + + """ + return self._generator.noncentral_f( + dfnum, dfden, nonc, size, dtype, usm_type=usm_type + ) + + def normal( + self, loc=0.0, scale=1.0, size=None, dtype=None, *, usm_type="device" + ): + """ + Draw random samples from a normal (Gaussian) distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.normal`. + + """ + return self._generator.normal( + loc, scale, size, dtype, usm_type=usm_type + ) + + def pareto(self, a, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a Pareto II or Lomax distribution with specified + shape. + + For full documentation refer to :obj:`numpy.random.RandomState.pareto`. + + """ + return self._generator.pareto(a, size, dtype, usm_type=usm_type) + + def poisson(self, lam=1.0, size=None, *, usm_type="device"): + """ + Draw samples from a Poisson distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.poisson`. + + """ + return self._generator.poisson(lam, size, usm_type=usm_type) - Examples - -------- - >>> s = dpnp.random.RandomState().random_sample(size=(4,)) - >>> print(s) - [0.13436424 0.56920387 0.84743374 0.80226506] + def power(self, a, size=None, dtype=None, *, usm_type="device"): + """ + Draws samples in [0, 1] from a power distribution with positive + exponent a - 1. + + For full documentation refer to :obj:`numpy.random.RandomState.power`. + + """ + return self._generator.power(a, size, dtype, usm_type=usm_type) + + def rayleigh(self, scale=1.0, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a Rayleigh distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.rayleigh`. + + """ + return self._generator.rayleigh(scale, size, dtype, usm_type=usm_type) - See Also - -------- - :obj:`dpnp.random.RandomState.rand` - :obj:`dpnp.random.RandomState.uniform` + def standard_cauchy(self, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a standard Cauchy distribution with mode = 0. + + For full documentation refer to + :obj:`numpy.random.RandomState.standard_cauchy`. """ + return self._generator.standard_cauchy(size, dtype, usm_type=usm_type) + + def standard_exponential(self, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from the standard exponential distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.standard_exponential`. - return self.uniform( - low=0.0, high=1.0, size=size, dtype=None, usm_type=usm_type + """ + return self._generator.standard_exponential( + size, dtype, usm_type=usm_type ) - def standard_normal(self, size=None, usm_type="device"): + def standard_gamma( + self, shape, size=None, dtype=None, *, usm_type="device" + ): """ - Draw samples from a standard Normal distribution ``(mean=0, stdev=1)``. + Draw samples from a standard Gamma distribution. - For full documentation refer to :obj:`numpy.random.RandomState.standard_normal`. + For full documentation refer to + :obj:`numpy.random.RandomState.standard_gamma`. - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + """ + return self._generator.standard_gamma( + shape, size, dtype, usm_type=usm_type + ) - Default: ``"device"``. + def standard_normal(self, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a standard Normal distribution (mean=0, stdev=1). - Returns - ------- - out : dpnp.ndarray - A floating-point array of shape `size` of drawn samples, or a - single sample if `size` was not specified. - Output array data type is :obj:`dpnp.float64` if device supports it, - or :obj:`dpnp.float32` otherwise. + For full documentation refer to + :obj:`numpy.random.RandomState.standard_normal`. - Examples - -------- - >>> s = dpnp.random.RandomState().standard_normal(size=(3, 5)) - >>> print(s) - [[-0.84401099 -1.81715362 -0.54465213 0.18557831 0.28352814] - [ 0.67718303 1.11570901 1.21968665 -1.18236388 0.08156915] - [ 0.21941987 -1.24544512 0.63522211 -0.673174 0. ]] + """ + return self._generator.standard_normal(size, dtype, usm_type=usm_type) - See Also - -------- - :obj:`dpnp.random.RandomState.normal` - :obj:`dpnp.random.RandomState.randn` + def standard_t(self, df, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a standard Student's t distribution with `df` + degrees of freedom. + For full documentation refer to + :obj:`numpy.random.RandomState.standard_t`. + + """ + return self._generator.standard_t(df, size, dtype, usm_type=usm_type) + + def triangular( + self, left, mode, right, size=None, dtype=None, *, usm_type="device" + ): """ + Draw samples from the triangular distribution over the interval + [left, right]. - return self.normal( - loc=0.0, scale=1.0, size=size, dtype=None, usm_type=usm_type + For full documentation refer to + :obj:`numpy.random.RandomState.triangular`. + + """ + return self._generator.triangular( + left, mode, right, size, dtype, usm_type=usm_type ) def uniform( - self, low=0.0, high=1.0, size=None, dtype=None, usm_type="device" + self, low=0.0, high=1.0, size=None, dtype=None, *, usm_type="device" ): """ - Draw samples from a uniform distribution. + Draw samples from a uniform distribution over [low, high). - Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). - In other words, any value within the given interval is equally likely to be drawn by uniform. + For full documentation refer to + :obj:`numpy.random.RandomState.uniform`. - For full documentation refer to :obj:`numpy.random.RandomState.uniform`. + """ + _uniform_range(low, high, "Range exceeds valid bounds", device=True) + return self._generator._float_sample( + ri._uniform, (low, high), size, dtype, usm_type, "uniform" + ) - Parameters - ---------- - usm_type : {"device", "shared", "host"}, optional - The type of SYCL USM allocation for the output array. + def vonmises(self, mu, kappa, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a von Mises distribution. - Default: ``"device"``. + For full documentation refer to + :obj:`numpy.random.RandomState.vonmises`. - Returns - ------- - out : dpnp.ndarray - Drawn samples from the parameterized uniform distribution. - Output array data type is the same as input `dtype`. If `dtype` is ``None`` (the default), - :obj:`dpnp.float64` type will be used if device supports it, or :obj:`dpnp.float32` otherwise. + """ + return self._generator.vonmises( + mu, kappa, size, dtype, usm_type=usm_type + ) + + def wald(self, mean, scale, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a Wald, or inverse Gaussian, distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.wald`. + + """ + return self._generator.wald(mean, scale, size, dtype, usm_type=usm_type) + + def weibull(self, a, size=None, dtype=None, *, usm_type="device"): + """ + Draw samples from a Weibull distribution. + + For full documentation refer to + :obj:`numpy.random.RandomState.weibull`. + + """ + return self._generator.weibull(a, size, dtype, usm_type=usm_type) + + def zipf(self, a, size=None, *, usm_type="device"): + """ + Draw samples from a Zipf distribution. + + For full documentation refer to :obj:`numpy.random.RandomState.zipf`. Limitations ----------- - Parameters `low` and `high` are supported as a scalar. Otherwise, - :obj:`numpy.random.RandomState.uniform(low, high, size)` samples are drawn. - Parameter `dtype` is supported only as :obj:`dpnp.int32`, :obj:`dpnp.float32`, :obj:`dpnp.float64` or ``None``. - - Examples - -------- - >>> low, high = 1.23, 10.54 # low and high - >>> s = dpnp.random.RandomState().uniform(low, high, 5) - >>> print(s) - [2.48093112 6.52928804 9.1196081 8.6990877 8.34074171] - - See Also - -------- - :obj:`dpnp.random.RandomState.randint` : Discrete uniform distribution, yielding integers. - :obj:`dpnp.random.RandomState.random_integers` : Discrete uniform distribution over the closed interval ``[low, high]``. - :obj:`dpnp.random.RandomState.random_sample` : Floats uniformly distributed over ``[0, 1)``. - :obj:`dpnp.random.RandomState.random` : Alias for :obj:`dpnp.random.RandomState.random_sample`. - :obj:`dpnp.random.RandomState.rand` : Convenience function that accepts dimensions as input, e.g., - ``rand(2, 2)`` would generate a 2-by-2 array of floats, uniformly distributed over ``[0, 1)``. - - """ - - if not use_origin_backend(): - if dpnp.is_cuda_backend(self): # pragma: no cover - raise NotImplementedError( - "Running on CUDA is currently not supported" - ) + The samples follow the Zipf distribution truncated to the largest + ``int64``, while NumPy's rejection test rounds to accepting every + sample above ``2**53``, which makes its tail too heavy for `a` close + to 1. - if not dpnp.isscalar(low): - pass - elif not dpnp.isscalar(high): - pass - else: - min_double = dpnp.finfo("double").min - max_double = dpnp.finfo("double").max - - if ( - not self._is_finite_scalar(low) - or low >= max_double - or low <= min_double - ): - raise OverflowError( - f"Range of low={low} exceeds valid bounds" - ) - elif ( - not self._is_finite_scalar(high) - or high >= max_double - or high <= min_double - ): - raise OverflowError( - f"Range of high={high} exceeds valid bounds" - ) - - if low > high: - low, high = high, low - - dtype = self._validate_float_dtype( - dtype, (dpnp.int32, dpnp.float32, dpnp.float64) - ) - validate_usm_type(usm_type, allow_none=False) - - return self._random_state.uniform( - low=low, - high=high, - size=size, - dtype=dtype, - usm_type=usm_type, - ).get_pyobj() - - return call_origin( - self._fallback_random_state.uniform, - low=low, - high=high, - size=size, - sycl_queue=self._sycl_queue, - ) + """ + return self._generator.zipf(a, size, usm_type=usm_type) diff --git a/dpnp/tests/skipped_tests.tbl b/dpnp/tests/skipped_tests.tbl index 5ea97fa7977..e69de29bb2d 100644 --- a/dpnp/tests/skipped_tests.tbl +++ b/dpnp/tests/skipped_tests.tbl @@ -1,10 +0,0 @@ -tests/test_random.py::TestDistributionsMultivariateNormal::test_moments -tests/test_random.py::TestDistributionsMultivariateNormal::test_output_shape_check -tests/test_random.py::TestDistributionsMultivariateNormal::test_seed -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.complex64)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), dpnp.int8)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), object)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.vstack([x, x]).T] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: (dpnp.asarray([(i, i) for i in x], [("a", int), ("b", int)]).view(dpnp.recarray))] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)] diff --git a/dpnp/tests/skipped_tests_cuda.tbl b/dpnp/tests/skipped_tests_cuda.tbl index e8415a1ae41..e69de29bb2d 100644 --- a/dpnp/tests/skipped_tests_cuda.tbl +++ b/dpnp/tests/skipped_tests_cuda.tbl @@ -1,662 +0,0 @@ -# NotImplementedError - -# random -tests/test_random_state.py::TestNormal::test_distr[host-float32] -tests/test_random_state.py::TestNormal::test_distr[host-float64] -tests/test_random_state.py::TestNormal::test_distr[host-None] -tests/test_random_state.py::TestNormal::test_distr[device-float32] -tests/test_random_state.py::TestNormal::test_distr[device-float64] -tests/test_random_state.py::TestNormal::test_distr[device-None] -tests/test_random_state.py::TestNormal::test_distr[shared-float32] -tests/test_random_state.py::TestNormal::test_distr[shared-float64] -tests/test_random_state.py::TestNormal::test_distr[shared-None] -tests/test_random_state.py::TestNormal::test_scale[host-float32] -tests/test_random_state.py::TestNormal::test_scale[host-float64] -tests/test_random_state.py::TestNormal::test_scale[host-None] -tests/test_random_state.py::TestNormal::test_scale[device-float32] -tests/test_random_state.py::TestNormal::test_scale[device-float64] -tests/test_random_state.py::TestNormal::test_scale[device-None] -tests/test_random_state.py::TestNormal::test_scale[shared-float32] -tests/test_random_state.py::TestNormal::test_scale[shared-float64] -tests/test_random_state.py::TestNormal::test_scale[shared-None] -tests/test_random_state.py::TestNormal::test_inf_loc[numpy.inf] -tests/test_random_state.py::TestNormal::test_inf_loc[-numpy.inf] -tests/test_random_state.py::TestNormal::test_inf_loc[nextafter(max, 0)] -tests/test_random_state.py::TestNormal::test_inf_loc[nextafter(min, 0)] -tests/test_random_state.py::TestNormal::test_inf_scale -tests/test_random_state.py::TestNormal::test_inf_loc_scale[numpy.inf] -tests/test_random_state.py::TestNormal::test_inf_loc_scale[-numpy.inf] -tests/test_random_state.py::TestNormal::test_extreme_bounds -tests/test_random_state.py::TestNormal::test_fallback[[2]-dpnp.array([3])] -tests/test_random_state.py::TestNormal::test_fallback[[2]-numpy.array([3])] -tests/test_random_state.py::TestNormal::test_fallback[dpnp.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestNormal::test_fallback[dpnp.array([2])-numpy.array([3])] -tests/test_random_state.py::TestNormal::test_fallback[numpy.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestNormal::test_fallback[numpy.array([2])-numpy.array([3])] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.float16] -tests/test_random_state.py::TestNormal::test_invalid_dtype[float] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.int64] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.int32] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.int] -tests/test_random_state.py::TestNormal::test_invalid_dtype[int] -tests/test_random_state.py::TestNormal::test_invalid_dtype[numpy.clongdouble] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.complex128] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.complex64] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.bool] -tests/test_random_state.py::TestNormal::test_invalid_dtype[dpnp.bool_] -tests/test_random_state.py::TestNormal::test_invalid_usm_type[Empty] -tests/test_random_state.py::TestNormal::test_invalid_usm_type[Unknown] -tests/test_random_state.py::TestRand::test_distr[host] -tests/test_random_state.py::TestRand::test_distr[device] -tests/test_random_state.py::TestRand::test_distr[shared] -tests/test_random_state.py::TestRand::test_zero_dims[(0,)] -tests/test_random_state.py::TestRand::test_zero_dims[(3, 0)] -tests/test_random_state.py::TestRand::test_zero_dims[(3, 0, 10)] -tests/test_random_state.py::TestRand::test_zero_dims[()] -tests/test_random_state.py::TestRand::test_wrong_dims -tests/test_random_state.py::TestRandInt::test_distr[host-int] -tests/test_random_state.py::TestRandInt::test_distr[host-dpnp.int32] -tests/test_random_state.py::TestRandInt::test_distr[device-int] -tests/test_random_state.py::TestRandInt::test_distr[device-dpnp.int32] -tests/test_random_state.py::TestRandInt::test_distr[shared-int] -tests/test_random_state.py::TestRandInt::test_distr[shared-dpnp.int32] -tests/test_random_state.py::TestRandInt::test_float_bounds -tests/test_random_state.py::TestRandInt::test_full_range -tests/test_random_state.py::TestRandInt::test_negative_bounds -tests/test_random_state.py::TestRandInt::test_negative_interval -tests/test_random_state.py::TestRandInt::test_bounds_checking -tests/test_random_state.py::TestRandInt::test_rng_zero_and_extremes -tests/test_random_state.py::TestRandInt::test_full_range -tests/test_random_state.py::TestRandInt::test_in_bounds_fuzz -tests/test_random_state.py::TestRandInt::test_zero_size[(3, 0, 4)] -tests/test_random_state.py::TestRandInt::test_zero_size[0] -tests/test_random_state.py::TestRandInt::test_zero_size[(0,)] -tests/test_random_state.py::TestRandInt::test_zero_size[()] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[[2]-dpnp.array([3])] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[[2]-numpy.array([3])] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[dpnp.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[dpnp.array([2])-numpy.array([3])] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[numpy.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestRandInt::test_bounds_fallback[numpy.array([2])-numpy.array([3])] -tests/test_random_state.py::TestRandInt::test_dtype_fallback[dpnp.int64] -tests/test_random_state.py::TestRandInt::test_dtype_fallback[dpnp.int] -tests/test_random_state.py::TestRandInt::test_dtype_fallback[dpnp.bool] -tests/test_random_state.py::TestRandInt::test_dtype_fallback[dpnp.bool_] -tests/test_random_state.py::TestRandInt::test_dtype_fallback[bool] -tests/test_random_state.py::TestRandInt::test_invalid_usm_type[Empty] -tests/test_random_state.py::TestRandInt::test_invalid_usm_type[Unknown] -tests/test_random_state.py::TestRandN::test_distr[host] -tests/test_random_state.py::TestRandN::test_distr[device] -tests/test_random_state.py::TestRandN::test_distr[shared] -tests/test_random_state.py::TestRandN::test_zero_dims[(0,)] -tests/test_random_state.py::TestRandN::test_zero_dims[(3, 0)] -tests/test_random_state.py::TestRandN::test_zero_dims[(3, 0, 10)] -tests/test_random_state.py::TestRandN::test_zero_dims[()] -tests/test_random_state.py::TestRandN::test_wrong_dims -tests/test_random_state.py::TestSeed::test_scalar[normal] -tests/test_random_state.py::TestSeed::test_scalar[standard_normal] -tests/test_random_state.py::TestSeed::test_scalar[random_sample] -tests/test_random_state.py::TestSeed::test_scalar[uniform] -tests/test_random_state.py::TestStandardNormal::test_distr[host] -tests/test_random_state.py::TestStandardNormal::test_distr[device] -tests/test_random_state.py::TestStandardNormal::test_distr[shared] -tests/test_random_state.py::TestStandardNormal::test_wrong_dims -tests/test_random_state.py::TestRandSample::test_distr[host] -tests/test_random_state.py::TestRandSample::test_distr[device] -tests/test_random_state.py::TestRandSample::test_distr[shared] -tests/test_random_state.py::TestRandSample::test_wrong_dims -tests/test_random_state.py::TestUniform::test_distr[host-float32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[host-float32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[host-float64-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[host-float64-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[host-int32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[host-int32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[host-None-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[host-None-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[device-float32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[device-float32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[device-float64-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[device-float64-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[device-int32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[device-int32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[device-None-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[device-None-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[shared-float32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[shared-float32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[shared-float64-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[shared-float64-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[shared-int32-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[shared-int32-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_distr[shared-None-(low, high)=[1.23, 10.54]] -tests/test_random_state.py::TestUniform::test_distr[shared-None-(low, high)=[10.54, 1.23]] -tests/test_random_state.py::TestUniform::test_low_high_equal[host-float32] -tests/test_random_state.py::TestUniform::test_low_high_equal[host-float64] -tests/test_random_state.py::TestUniform::test_low_high_equal[host-int32] -tests/test_random_state.py::TestUniform::test_low_high_equal[host-None] -tests/test_random_state.py::TestUniform::test_low_high_equal[device-float32] -tests/test_random_state.py::TestUniform::test_low_high_equal[device-float64] -tests/test_random_state.py::TestUniform::test_low_high_equal[device-int32] -tests/test_random_state.py::TestUniform::test_low_high_equal[device-None] -tests/test_random_state.py::TestUniform::test_low_high_equal[shared-float32] -tests/test_random_state.py::TestUniform::test_low_high_equal[shared-float64] -tests/test_random_state.py::TestUniform::test_low_high_equal[shared-int32] -tests/test_random_state.py::TestUniform::test_low_high_equal[shared-None] -tests/test_random_state.py::TestUniform::test_range_bounds -tests/test_random_state.py::TestUniform::test_fallback[[2]-dpnp.array([3])] -tests/test_random_state.py::TestUniform::test_fallback[[2]-numpy.array([3])] -tests/test_random_state.py::TestUniform::test_fallback[dpnp.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestUniform::test_fallback[dpnp.array([2])-numpy.array([3])] -tests/test_random_state.py::TestUniform::test_fallback[numpy.array([2])-dpnp.array([3])] -tests/test_random_state.py::TestUniform::test_fallback[numpy.array([2])-numpy.array([3])] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.float16] -tests/test_random_state.py::TestUniform::test_invalid_dtype[float] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.int64] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.int] -tests/test_random_state.py::TestUniform::test_invalid_dtype[int] -tests/test_random_state.py::TestUniform::test_invalid_dtype[numpy.clongdouble] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.complex128] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.complex64] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.bool] -tests/test_random_state.py::TestUniform::test_invalid_dtype[dpnp.bool_] -tests/test_random_state.py::TestUniform::test_invalid_usm_type[Empty] -tests/test_random_state.py::TestUniform::test_invalid_usm_type[Unknown] - -tests/test_random.py::test_input_size[chisquare] -tests/test_random.py::test_input_size[rand] -tests/test_random.py::test_input_size[randn] -tests/test_random.py::test_input_shape[chisquare] -tests/test_random.py::test_input_shape[random] -tests/test_random.py::test_input_shape[random_sample] -tests/test_random.py::test_input_shape[ranf] -tests/test_random.py::test_input_shape[sample] -tests/test_random.py::test_check_output[random] -tests/test_random.py::test_check_output[random_sample] -tests/test_random.py::test_check_output[ranf] -tests/test_random.py::test_check_output[sample] -tests/test_random.py::test_check_output[rand] -tests/test_random.py::test_seed[random] -tests/test_random.py::test_seed[random_sample] -tests/test_random.py::test_seed[ranf] -tests/test_random.py::test_seed[sample] -tests/test_random.py::test_seed[rand] -tests/test_random.py::test_randn_normal_distribution -tests/test_random.py::TestDistributionsBeta::test_invalid_args -tests/test_random.py::TestDistributionsBeta::test_moments -tests/test_random.py::TestDistributionsBeta::test_seed -tests/test_random.py::TestDistributionsBinomial::test_extreme_value -tests/test_random.py::TestDistributionsBinomial::test_invalid_args -tests/test_random.py::TestDistributionsBinomial::test_moments -tests/test_random.py::TestDistributionsBinomial::test_seed -tests/test_random.py::TestDistributionsChisquare::test_invalid_args -tests/test_random.py::TestDistributionsChisquare::test_seed -tests/test_random.py::TestDistributionsExponential::test_invalid_args -tests/test_random.py::TestDistributionsExponential::test_seed -tests/test_random.py::TestDistributionsF::test_invalid_args -tests/test_random.py::TestDistributionsF::test_moments -tests/test_random.py::TestDistributionsF::test_seed -tests/test_random.py::TestDistributionsGamma::test_invalid_args -tests/test_random.py::TestDistributionsGamma::test_moments -tests/test_random.py::TestDistributionsGamma::test_seed -tests/test_random.py::TestDistributionsGeometric::test_extreme_value -tests/test_random.py::TestDistributionsGeometric::test_invalid_args -tests/test_random.py::TestDistributionsGeometric::test_moments -tests/test_random.py::TestDistributionsGeometric::test_seed -tests/test_random.py::TestDistributionsGumbel::test_extreme_value -tests/test_random.py::TestDistributionsGumbel::test_invalid_args -tests/test_random.py::TestDistributionsGumbel::test_moments -tests/test_random.py::TestDistributionsGumbel::test_seed -tests/test_random.py::TestDistributionsHypergeometric::test_extreme_value -tests/test_random.py::TestDistributionsHypergeometric::test_invalid_args -tests/test_random.py::TestDistributionsHypergeometric::test_moments -tests/test_random.py::TestDistributionsHypergeometric::test_seed -tests/test_random.py::TestDistributionsLaplace::test_extreme_value -tests/test_random.py::TestDistributionsLaplace::test_invalid_args -tests/test_random.py::TestDistributionsLaplace::test_moments -tests/test_random.py::TestDistributionsLaplace::test_seed -tests/test_random.py::TestDistributionsLogistic::test_invalid_args -tests/test_random.py::TestDistributionsLogistic::test_moments -tests/test_random.py::TestDistributionsLogistic::test_seed -tests/test_random.py::TestDistributionsLognormal::test_extreme_value -tests/test_random.py::TestDistributionsLognormal::test_invalid_args -tests/test_random.py::TestDistributionsLognormal::test_moments -tests/test_random.py::TestDistributionsLognormal::test_seed -tests/test_random.py::TestDistributionsMultinomial::test_check_sum -tests/test_random.py::TestDistributionsMultinomial::test_extreme_value -tests/test_random.py::TestDistributionsMultinomial::test_invalid_args -tests/test_random.py::TestDistributionsMultinomial::test_moments -tests/test_random.py::TestDistributionsMultinomial::test_seed -tests/test_random.py::TestDistributionsMultinomial::test_seed1 -tests/test_random.py::TestDistributionsMultivariateNormal::test_invalid_args -tests/test_random.py::TestDistributionsNegativeBinomial::test_extreme_value -tests/test_random.py::TestDistributionsNegativeBinomial::test_invalid_args -tests/test_random.py::TestDistributionsNegativeBinomial::test_seed -tests/test_random.py::TestDistributionsNormal::test_extreme_value -tests/test_random.py::TestDistributionsNormal::test_invalid_args -tests/test_random.py::TestDistributionsNormal::test_moments -tests/test_random.py::TestDistributionsNormal::test_seed -tests/test_random.py::TestDistributionsNoncentralChisquare::test_moments[df_grt_1] -tests/test_random.py::TestDistributionsNoncentralChisquare::test_moments[df_eq_1] -tests/test_random.py::TestDistributionsNoncentralChisquare::test_moments[df_less_1] -tests/test_random.py::TestDistributionsNoncentralChisquare::test_invalid_args -tests/test_random.py::TestDistributionsNoncentralChisquare::test_seed[df_grt_1] -tests/test_random.py::TestDistributionsNoncentralChisquare::test_seed[df_eq_1] -tests/test_random.py::TestDistributionsNoncentralChisquare::test_seed[df_less_1] -tests/test_random.py::TestDistributionsPareto::test_invalid_args -tests/test_random.py::TestDistributionsPareto::test_moments -tests/test_random.py::TestDistributionsPareto::test_seed -tests/test_random.py::TestDistributionsPoisson::test_extreme_value -tests/test_random.py::TestDistributionsPoisson::test_invalid_args -tests/test_random.py::TestDistributionsPoisson::test_moments -tests/test_random.py::TestDistributionsPoisson::test_seed -tests/test_random.py::TestDistributionsPower::test_invalid_args -tests/test_random.py::TestDistributionsPower::test_moments -tests/test_random.py::TestDistributionsPower::test_seed -tests/test_random.py::TestDistributionsRayleigh::test_extreme_value -tests/test_random.py::TestDistributionsRayleigh::test_invalid_args -tests/test_random.py::TestDistributionsRayleigh::test_moments -tests/test_random.py::TestDistributionsRayleigh::test_seed -tests/test_random.py::TestDistributionsStandardCauchy::test_seed -tests/test_random.py::TestDistributionsStandardExponential::test_moments -tests/test_random.py::TestDistributionsStandardExponential::test_seed -tests/test_random.py::TestDistributionsStandardGamma::test_extreme_value -tests/test_random.py::TestDistributionsStandardGamma::test_invalid_args -tests/test_random.py::TestDistributionsStandardGamma::test_moments -tests/test_random.py::TestDistributionsStandardGamma::test_seed -tests/test_random.py::TestDistributionsStandardNormal::test_moments -tests/test_random.py::TestDistributionsStandardNormal::test_seed -tests/test_random.py::TestDistributionsStandardT::test_invalid_args -tests/test_random.py::TestDistributionsStandardT::test_moments -tests/test_random.py::TestDistributionsStandardT::test_seed -tests/test_random.py::TestDistributionsTriangular::test_invalid_args 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-tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardNormal::test_standard_normal[_param_0_{shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardNormal::test_standard_normal[_param_1_{shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardNormal::test_standard_normal[_param_2_{shape=None}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardT::test_standard_t[_param_0_{df_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardT::test_standard_t[_param_1_{df_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardT::test_standard_t[_param_2_{df_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsStandardT::test_standard_t[_param_3_{df_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_0_{dtype=None, left_shape=(), mode_shape=(), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_1_{dtype=None, left_shape=(), mode_shape=(), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_2_{dtype=None, left_shape=(), mode_shape=(), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_3_{dtype=None, left_shape=(), mode_shape=(), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_4_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_5_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_6_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_7_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_8_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_9_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_10_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_11_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_12_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_13_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_14_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular[_param_15_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_0_{dtype=None, left_shape=(), mode_shape=(), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_1_{dtype=None, left_shape=(), mode_shape=(), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_2_{dtype=None, left_shape=(), mode_shape=(), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_3_{dtype=None, left_shape=(), mode_shape=(), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_4_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_5_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_6_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_7_{dtype=None, left_shape=(), mode_shape=(3, 2), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_8_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_9_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_10_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_11_{dtype=None, left_shape=(3, 2), mode_shape=(), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_12_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_13_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_14_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsTriangular::test_triangular_for_invalid_params[_param_15_{dtype=None, left_shape=(3, 2), mode_shape=(3, 2), right_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_0_{high_shape=(), low_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_1_{high_shape=(), low_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_2_{high_shape=(), low_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_3_{high_shape=(), low_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_4_{high_shape=(3, 2), low_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_5_{high_shape=(3, 2), low_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_6_{high_shape=(3, 2), low_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsUniform::test_uniform[_param_7_{high_shape=(3, 2), low_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_0_{dtype=None, kappa_shape=(), mu_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_1_{dtype=None, kappa_shape=(), mu_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_2_{dtype=None, kappa_shape=(), mu_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_3_{dtype=None, kappa_shape=(), mu_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_4_{dtype=None, kappa_shape=(3, 2), mu_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_5_{dtype=None, kappa_shape=(3, 2), mu_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_6_{dtype=None, kappa_shape=(3, 2), mu_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsVonmises::test_vonmises[_param_7_{dtype=None, kappa_shape=(3, 2), mu_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_0_{dtype=None, mean_shape=(), scale_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_1_{dtype=None, mean_shape=(), scale_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_2_{dtype=None, mean_shape=(), scale_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_3_{dtype=None, mean_shape=(), scale_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_4_{dtype=None, mean_shape=(3, 2), scale_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_5_{dtype=None, mean_shape=(3, 2), scale_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_6_{dtype=None, mean_shape=(3, 2), scale_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWald::test_wald[_param_7_{dtype=None, mean_shape=(3, 2), scale_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull[_param_0_{a_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull[_param_1_{a_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull[_param_2_{a_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull[_param_3_{a_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_inf_a[_param_0_{a_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_inf_a[_param_1_{a_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_inf_a[_param_2_{a_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_inf_a[_param_3_{a_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_negative_a[_param_0_{a_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_negative_a[_param_1_{a_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_negative_a[_param_2_{a_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsWeibull::test_weibull_for_negative_a[_param_3_{a_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsZipf::test_zipf[_param_0_{a_shape=(), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsZipf::test_zipf[_param_1_{a_shape=(), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsZipf::test_zipf[_param_2_{a_shape=(3, 2), shape=(4, 3, 2)}] -tests/third_party/cupy/random_tests/test_distributions.py::TestDistributionsZipf::test_zipf[_param_3_{a_shape=(3, 2), shape=(3, 2)}] -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutations_param_0_{seed=None}::test_permutation_seed1 -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutations_param_0_{seed=None}::test_permutation_sort_1dim -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutations_param_0_{seed=None}::test_permutation_sort_ndim -tests/third_party/cupy/random_tests/test_permutations.py::TestShuffle::test_shuffle_seed1 -tests/third_party/cupy/random_tests/test_permutations.py::TestShuffle::test_shuffle_sort_1dim -tests/third_party/cupy/random_tests/test_permutations.py::TestShuffle::test_shuffle_sort_ndim -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutationSoundness_param_0_{num=0}::test_permutation_soundness -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutationSoundness_param_1_{num=1}::test_permutation_soundness -tests/third_party/cupy/random_tests/test_permutations.py::TestPermutationSoundness_param_2_{num=100}::test_permutation_soundness -tests/third_party/cupy/random_tests/test_sample.py::TestRandint::test_lo_hi_equal -tests/third_party/cupy/random_tests/test_sample.py::TestRandint::test_lo_hi_nonrandom -tests/third_party/cupy/random_tests/test_sample.py::TestRandint::test_lo_hi_reversed -tests/third_party/cupy/random_tests/test_sample.py::TestRandint::test_zero_sizes -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_bound_1 -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_bound_2 -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_bound_float1 -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_bound_float2 -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_bound_overflow -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_goodness_of_fit -tests/third_party/cupy/random_tests/test_sample.py::TestRandint2::test_goodness_of_fit_2 -tests/third_party/cupy/random_tests/test_sample.py::TestRandomIntegers2::test_bound_1 -tests/third_party/cupy/random_tests/test_sample.py::TestRandomIntegers2::test_bound_2 -tests/third_party/cupy/random_tests/test_sample.py::TestRandomIntegers2::test_goodness_of_fit -tests/third_party/cupy/random_tests/test_sample.py::TestRandomIntegers2::test_goodness_of_fit_2 diff --git a/dpnp/tests/skipped_tests_gpu.tbl b/dpnp/tests/skipped_tests_gpu.tbl index c67e09ad8e2..e69de29bb2d 100644 --- a/dpnp/tests/skipped_tests_gpu.tbl +++ b/dpnp/tests/skipped_tests_gpu.tbl @@ -1,18 +0,0 @@ -tests/test_random.py::TestDistributionsMultivariateNormal::test_moments -tests/test_random.py::TestDistributionsMultivariateNormal::test_output_shape_check -tests/test_random.py::TestDistributionsMultivariateNormal::test_seed -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle[float32] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle[float64] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle[int32] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle[int64] -tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[float32] -tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[float64] -tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[int32] -tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[int64] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.complex64)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), dpnp.int8)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), object)] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.vstack([x, x]).T] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: (dpnp.asarray([(i, i) for i in x], [("a", int), ("b", int)]).view(dpnp.recarray))] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]] -tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)] diff --git a/dpnp/tests/test_random.py b/dpnp/tests/test_random.py index 656ffe67e2f..373eef5fc56 100644 --- a/dpnp/tests/test_random.py +++ b/dpnp/tests/test_random.py @@ -6,7 +6,7 @@ import dpnp.random -from .helper import has_support_aspect64 +from .helper import get_all_dtypes, has_support_aspect64 class TestDistribution(unittest.TestCase): @@ -16,7 +16,7 @@ def check_extreme_value(self, dist_name, val, params): dpnp.random.seed(seed) res = dpnp.asnumpy(getattr(dpnp.random, dist_name)(size=size, **params)) assert len(numpy.unique(res)) == 1 - assert numpy.unique(res)[0] == val + assert numpy.isclose(numpy.unique(res)[0], val, rtol=1e-15, atol=0) def check_moments( self, dist_name, expected_mean, expected_var, params, size=10**5 @@ -151,7 +151,6 @@ def test_randn_normal_distribution(): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsBeta(TestDistribution): def test_moments(self): a = 2.56 @@ -177,7 +176,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsBinomial(TestDistribution): def test_extreme_value(self): n = 5 @@ -216,7 +214,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsChisquare(TestDistribution): def test_invalid_args(self): df = -1 # positive `df` is expected @@ -228,7 +225,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsExponential(TestDistribution): def test_invalid_args(self): scale = -1 # non-negative `scale` is expected @@ -240,7 +236,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsF(TestDistribution): def test_moments(self): dfnum = 12.56 @@ -274,7 +269,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsGamma(TestDistribution): def test_moments(self): shape = 2.56 @@ -301,7 +295,6 @@ def test_seed(self): self.check_seed("gamma", {"shape": shape}) -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsGeometric(TestDistribution): def test_extreme_value(self): p = 1.0 @@ -310,7 +303,8 @@ def test_extreme_value(self): def test_moments(self): p = 0.8 - expected_mean = (1 - p) / p + # the number of trials up to and including the first success + expected_mean = 1 / p expected_var = (1 - p) / (p**2) self.check_moments("geometric", expected_mean, expected_var, {"p": p}) @@ -325,7 +319,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsGumbel(TestDistribution): def test_extreme_value(self): loc = 5 @@ -357,7 +350,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsHypergeometric(TestDistribution): def test_extreme_value(self): ngood = 100 @@ -444,12 +436,11 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLaplace(TestDistribution): def test_extreme_value(self): loc = 5 scale = 0.0 - expected_val = scale + expected_val = loc self.check_extreme_value( "laplace", expected_val, {"loc": loc, "scale": scale} ) @@ -475,7 +466,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLogistic(TestDistribution): def test_moments(self): loc = 2.56 @@ -501,7 +491,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLognormal(TestDistribution): def test_extreme_value(self): mean = 0.5 @@ -537,7 +526,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsMultinomial(TestDistribution): def test_extreme_value(self): n = 0 @@ -586,7 +574,6 @@ def test_seed1(self): self.check_seed("multinomial", {"n": n, "pvals": pvals}) -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsMultivariateNormal(TestDistribution): def test_moments(self): seed = 2804183 @@ -595,8 +582,8 @@ def test_moments(self): cov = [[1, 0], [0, 1]] size = 10**5 res = dpnp.random.multivariate_normal(mean=mean, cov=cov, size=size) - res_mean = [dpnp.mean(res.T[0]), dpnp.mean(res.T[1])] - assert dpnp.allclose(res_mean, mean) + # six standard deviations of the sample mean + assert_allclose(dpnp.mean(res, axis=0), mean, atol=6 / size**0.5) def test_invalid_args(self): mean = [2.56, 3.23] # OK @@ -632,7 +619,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsNegativeBinomial(TestDistribution): def test_extreme_value(self): seed = 28041990 @@ -694,7 +680,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsNoncentralChisquare: @pytest.mark.parametrize( "df", [5.0, 1.0, 0.5], ids=["df_grt_1", "df_eq_1", "df_less_1"] @@ -738,11 +723,11 @@ def test_seed(self, df): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPareto(TestDistribution): def test_moments(self): + # Pareto II (Lomax) a = 30.0 - expected_mean = a / (a - 1) + expected_mean = 1 / (a - 1) expected_var = a / (((a - 1) ** 2) * (a - 2)) self.check_moments("pareto", expected_mean, expected_var, {"a": a}) @@ -757,7 +742,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPoisson(TestDistribution): def test_extreme_value(self): lam = 0.0 @@ -779,7 +763,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPower(TestDistribution): def test_moments(self): a = 30.0 @@ -799,7 +782,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsRayleigh(TestDistribution): def test_extreme_value(self): scale = 0.0 @@ -841,7 +823,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsStandardGamma(TestDistribution): def test_extreme_value(self): self.check_extreme_value("standard_gamma", 0.0, {"shape": 0.0}) @@ -873,7 +854,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsStandardT(TestDistribution): def test_moments(self): df = 300.0 @@ -892,7 +872,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsTriangular(TestDistribution): def test_moments(self): left = 1.0 @@ -964,7 +943,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsVonmises: @pytest.mark.parametrize( "kappa", [5.0, 0.5], ids=["large_kappa", "small_kappa"] @@ -1005,7 +983,6 @@ def test_seed(self, kappa): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsWald(TestDistribution): def test_moments(self): size = 5 * 10**6 @@ -1037,7 +1014,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsWeibull(TestDistribution): def test_extreme_value(self): a = 0.0 @@ -1054,7 +1030,6 @@ def test_seed(self): @pytest.mark.skipif(not has_support_aspect64(), reason="Failed on Iris Xe") -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsZipf(TestDistribution): def test_invalid_args(self): a = 1.0 # parameter `a` is expected greater than 1. @@ -1067,7 +1042,7 @@ def test_seed(self): class TestPermutationsTestShuffle: @pytest.mark.parametrize( - "dtype", [dpnp.float32, dpnp.float64, dpnp.int32, dpnp.int64] + "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) ) def test_shuffle(self, dtype): seed = 28041990 @@ -1084,7 +1059,7 @@ def test_shuffle(self, dtype): assert_array_equal(actual_x, desired_x) @pytest.mark.parametrize( - "dtype", [dpnp.float32, dpnp.float64, dpnp.int32, dpnp.int64] + "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) ) def test_no_miss_numbers(self, dtype): seed = 28041990 @@ -1104,29 +1079,16 @@ def test_no_miss_numbers(self, dtype): lambda x: dpnp.astype(dpnp.asarray(x), dpnp.int8), lambda x: dpnp.astype(dpnp.asarray(x), dpnp.float32), lambda x: dpnp.asarray(x).astype(dpnp.complex64), - lambda x: dpnp.astype(dpnp.asarray(x), object), lambda x: dpnp.asarray([[i, i] for i in x]), - lambda x: dpnp.vstack([x, x]).T, - lambda x: ( - dpnp.asarray( - [(i, i) for i in x], [("a", int), ("b", int)] - ).view(dpnp.recarray) - ), - lambda x: dpnp.asarray( - [(i, i) for i in x], [("a", object), ("b", dpnp.int32)] - ), + lambda x: dpnp.vstack([dpnp.asarray(x), dpnp.asarray(x)]).T, ], ids=[ "lambda x: dpnp.array([])", "lambda x: dpnp.astype(dpnp.asarray(x), dpnp.int8)", "lambda x: dpnp.astype(dpnp.asarray(x), dpnp.float32)", "lambda x: dpnp.asarray(x).astype(dpnp.complex64)", - "lambda x: dpnp.astype(dpnp.asarray(x), object)", "lambda x: dpnp.asarray([[i, i] for i in x])", "lambda x: dpnp.vstack([x, x]).T", - "lambda x: (dpnp.asarray([(i, i) for i in x], [" - '("a", int), ("b", int)]).view(dpnp.recarray))', - 'lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]', ], ) def test_shuffle1(self, conv): @@ -1156,25 +1118,10 @@ def test_shuffle1(self, conv): desired = conv(dpnp_1d) assert_array_equal(actual, desired) - @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( - "conv", - [lambda x: x, lambda x: [(i, i) for i in x]], - ids=[ - "lambda x: x", - "lambda x: [(i, i) for i in x]", - ], + "x", [[1, 2, 3], numpy.arange(3)], ids=["list", "numpy.ndarray"] ) - def test_shuffle1_fallback(self, conv): - # This is parameterized version of original tests of `numpy.random` (both the same): - # * tests/test_random.py::TestRandomDist::test_shuffle - # * tests/test_randomstate.py::TestRandomDist::test_shuffle - - seed = 1234567890 - - dpnp.random.seed(seed) - alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) - dpnp.random.shuffle(alist) - actual = alist - desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) - assert_array_equal(actual, desired) + def test_shuffle_not_dpnp_array(self, x): + # unlike NumPy, only a dpnp.ndarray is shuffled, like in CuPy + with pytest.raises(TypeError): + dpnp.random.shuffle(x) diff --git a/dpnp/tests/test_random_state.py b/dpnp/tests/test_random_state.py index 0c9c0a8b601..5772cc218ab 100644 --- a/dpnp/tests/test_random_state.py +++ b/dpnp/tests/test_random_state.py @@ -1,1145 +1,837 @@ -from unittest import mock +import warnings import dpctl import numpy import pytest from numpy.testing import ( - assert_array_almost_equal, + assert_allclose, assert_array_equal, assert_equal, assert_raises, ) import dpnp -from dpnp.dpnp_array import dpnp_array from dpnp.random import RandomState -from .helper import ( - assert_dtype_allclose, - get_array, - is_cpu_device, - is_gpu_device, -) -from .third_party.cupy import testing - -# aspects of default device: -_def_device = dpctl.SyclQueue().sycl_device -_def_dev_has_fp64 = _def_device.has_aspect_fp64 +from .helper import get_all_dtypes, get_float_dtypes +from .test_random_generator import _assert_same_error +float_dtypes = get_float_dtypes(no_float16=True) list_of_usm_types = ["host", "device", "shared"] - -def assert_cfd(data, exp_sycl_queue, exp_usm_type=None): - assert exp_sycl_queue == data.sycl_queue - if exp_usm_type: - assert exp_usm_type == data.usm_type - - -def get_default_floating(): - if not _def_dev_has_fp64: - return dpnp.float32 - return dpnp.float64 - - -class TestNormal: - @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.float64, None]) - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, dtype, usm_type): - seed = 1234567 - sycl_queue = dpctl.SyclQueue() - func = lambda: RandomState(seed, sycl_queue=sycl_queue).normal( - loc=0.12345, scale=2.71, size=(3, 2), dtype=dtype, usm_type=usm_type - ) - - if dtype is dpnp.float64 and not _def_dev_has_fp64: - # default device doesn't support 'float64' type - assert_raises(RuntimeError, func) - return - - dpnp_data = func() - - # default dtype depends on fp64 support by the device - dtype = get_default_floating() if dtype is None else dtype - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array( - [ - [0.428205496031286, -0.55383273779227], - [2.027017795643378, 4.318888073163015], - [2.69080893259102, -1.047967253719708], - ], - dtype=dtype, - ) - else: - if dtype == dpnp.float64: - expected = numpy.array( - [ - [-15.73532523, -11.84163022], - [0.548032, 1.41296207], - [-2.63250381, 2.77542322], - ], - dtype=dtype, - ) - else: - expected = numpy.array( - [ - [-15.735329, -11.841626], - [0.548032, 1.4129621], - [-2.6325033, 2.7754242], - ], - dtype=dtype, - ) - # TODO: discuss with oneMKL: there is a difference between CPU and GPU - # generated samples since 9 digit while precision=15 for float64 - # precision = dpnp.finfo(dtype).precision - precision = 8 if dtype == dpnp.float64 else dpnp.finfo(dtype).precision - assert_array_almost_equal(dpnp_data, expected, decimal=precision) - - # check if compute follows data isn't broken - assert_cfd(dpnp_data, sycl_queue, usm_type) - - @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.float64, None]) - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_scale(self, dtype, usm_type): - mean = dtype(7.0) if dtype == dpnp.float32 else 7.0 - rs = RandomState(39567) - func = lambda scale: rs.normal( - loc=mean, scale=scale, dtype=dtype, usm_type=usm_type - ) - - if dtype is dpnp.float64 and not _def_dev_has_fp64: - # default device doesn't support 'float64' type - assert_raises(RuntimeError, func, scale=0) - return - - # zero scale means full ndarray of mean values - assert_equal(func(scale=0), mean) - - # scale must be non-negative ('-0.0' is negative value) - assert_raises(ValueError, func, scale=-0.0) - assert_raises(ValueError, func, scale=-3.71) - - @pytest.mark.parametrize( - "loc", - [ - numpy.inf, - -numpy.inf, - numpy.nextafter(dpnp.finfo(get_default_floating()).max, 0), - numpy.nextafter(dpnp.finfo(get_default_floating()).min, 0), - ], - ids=[ - "numpy.inf", - "-numpy.inf", - "nextafter(max, 0)", - "nextafter(min, 0)", - ], - ) - def test_inf_loc(self, loc): - a = RandomState(6531).normal(loc=loc, scale=1, size=1000) - assert_equal(a, get_default_floating()(loc), strict=False) - - def test_inf_scale(self): - a = RandomState().normal(0, numpy.inf, size=1000) - assert not dpnp.isnan(a).any() - assert dpnp.isinf(a).all() - assert_equal(a.max(), numpy.inf) - assert_equal(a.min(), -numpy.inf) - - @pytest.mark.parametrize("loc", [numpy.inf, -numpy.inf]) - def test_inf_loc_scale(self, loc): - a = RandomState().normal(loc=loc, scale=numpy.inf, size=1000) - assert not dpnp.isnan(a).all() - assert_equal(dpnp.nanmin(a), loc) - assert_equal(dpnp.nanmax(a), loc) - - def test_extreme_bounds(self): - dtype = get_default_floating() - fmin = dpnp.finfo(dtype).min - fmax = dpnp.finfo(dtype).max - - size = 1000 - func = RandomState(34567).normal - - assert_raises(OverflowError, func, fmin, 1) - assert_raises(OverflowError, func, fmax, 1) - assert_raises(OverflowError, func, 1, fmax) - assert_raises(OverflowError, func, fmin, fmax) - assert_raises(OverflowError, func, fmax, fmax) - - try: - # acceptable extreme range: -fmin < loc < fmax and 0 <= scale < fmax - func(loc=1, scale=numpy.nextafter(fmax, 0), size=size) - func( - loc=numpy.nextafter(fmin, 0), - scale=numpy.nextafter(fmax, 0), - size=size, - ) - func( - loc=numpy.nextafter(fmax, 0), - scale=numpy.nextafter(fmax, 0), - size=size, - ) - except Exception as e: - raise AssertionError( - "No error should have been raised, but one was " - "with the following message:\n\n%s" % str(e) - ) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @pytest.mark.parametrize( - "scale", - [dpnp.array([3]), numpy.array([3])], - ids=["dpnp.array([3])", "numpy.array([3])"], - ) - @pytest.mark.parametrize( - "loc", - [[2], dpnp.array([2]), numpy.array([2])], - ids=["[2]", "dpnp.array([2])", "numpy.array([2])"], - ) - def test_fallback(self, loc, scale): - seed = 15 - size = (3, 2, 5) - - sycl_queue = dpctl.SyclQueue() - data = RandomState(seed, sycl_queue=sycl_queue).normal( - loc=loc, scale=scale, size=size - ) - - # dpnp accepts only scalar as low and/or high, in other cases it will be a fallback to numpy - expected = numpy.random.RandomState(seed).normal( - loc=get_array(numpy, loc), scale=get_array(numpy, scale), size=size - ) - - precision = dpnp.finfo(get_default_floating()).precision - assert_array_almost_equal(data, expected, decimal=precision) - - # check if compute follows data isn't broken - assert_cfd(data, sycl_queue) - - @pytest.mark.parametrize( - "dtype", - [ - dpnp.float16, - float, - dpnp.int64, - dpnp.int32, - dpnp.int_, - int, - numpy.clongdouble, - dpnp.complex128, - dpnp.complex64, - dpnp.bool, - dpnp.bool_, - ], - ids=[ - "dpnp.float16", - "float", - "dpnp.int64", - "dpnp.int32", - "dpnp.int_", - "int", - "numpy.clongdouble", - "dpnp.complex128", - "dpnp.complex64", - "dpnp.bool", - "dpnp.bool_", - ], - ) - def test_invalid_dtype(self, dtype): - # dtype must be float32 or float64 - assert_raises(TypeError, RandomState().normal, dtype=dtype) - - @pytest.mark.parametrize( - "usm_type", ["", "unknown"], ids=["Empty", "Unknown"] - ) - def test_invalid_usm_type(self, usm_type): - # dtype must be float32 or float64 - assert_raises(ValueError, RandomState().normal, usm_type=usm_type) - - -class TestRand: - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, usm_type): - seed = 28042 - sycl_queue = dpctl.SyclQueue() - dtype = get_default_floating() - - data = RandomState(seed, sycl_queue=sycl_queue).rand( - 3, 2, usm_type=usm_type - ) - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array( - [ - [0.7592552667483687, 0.5937560645397753], - [0.257010098779574, 0.749422621447593], - [0.6316644293256104, 0.7411410815548152], - ], - dtype=dtype, - ) - else: - expected = numpy.array( - [ - [4.864511571334162e-14, 7.333946068708259e-01], - [8.679067575689537e-01, 5.627257087965376e-01], - [4.413379518222594e-01, 4.334482843514076e-01], - ], - dtype=dtype, - ) - - precision = dpnp.finfo(dtype).precision - assert_array_almost_equal(data, expected, decimal=precision) - assert_cfd(data, sycl_queue, usm_type) - - # call with the same seed has to draw the same values - data = RandomState(seed, sycl_queue=sycl_queue).rand( - 3, 2, usm_type=usm_type - ) - assert_array_almost_equal(data, expected, decimal=precision) - assert_cfd(data, sycl_queue, usm_type) - - # call with omitted dimensions has to draw the first element from expected - data = RandomState(seed, sycl_queue=sycl_queue).rand(usm_type=usm_type) - assert_array_almost_equal(data, expected[0, 0], decimal=precision) - assert_cfd(data, sycl_queue, usm_type) - - # rand() is an alias on random_sample(), map arguments - with mock.patch("dpnp.random.RandomState.random_sample") as m: - RandomState(seed).rand(3, 2, usm_type=usm_type) - m.assert_called_once_with(size=(3, 2), usm_type=usm_type) - - @pytest.mark.parametrize( - "dims", - [(), (5,), (10, 2, 7), (0,)], - ids=["()", "(5,)", "(10, 2, 7)", "(0,)"], - ) - def test_dims(self, dims): - size = None if len(dims) == 0 else dims - - with mock.patch("dpnp.random.RandomState.uniform") as m: - RandomState().rand(*dims) - m.assert_called_once_with( - low=0.0, high=1.0, size=size, dtype=None, usm_type="device" - ) - - @pytest.mark.parametrize( - "zero_dims", - [(0,), (3, 0), (3, 0, 10), ()], - ids=["(0,)", "(3, 0)", "(3, 0, 10)", "()"], - ) - def test_zero_dims(self, zero_dims): - assert_equal(RandomState().rand(*zero_dims).shape, zero_dims) - - def test_wrong_dims(self): - # non-positive number in size - assert_raises(ValueError, RandomState().rand, 2, -1) - - # non-integer number in size - assert_raises(TypeError, RandomState().rand, 3.5, 2) - - -class TestRandInt: - @pytest.mark.parametrize( - "dtype", - [int, dpnp.int32, dpnp.int_], - ids=["int", "dpnp.int32", "dpnp.int_"], - ) - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, dtype, usm_type): - seed = 9864 - low = 1 - high = 10 - - if dtype == dpnp.int_ and dtype != dpnp.dtype("int32"): - pytest.skip( - "dtype isn't alias on dpnp.int32 on the target OS, so there will be a fallback" - ) - - sycl_queue = dpctl.SyclQueue() - data = RandomState(seed, sycl_queue=sycl_queue).randint( - low=low, high=high, size=(3, 2), dtype=dtype, usm_type=usm_type - ) - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array([[4, 1], [5, 3], [5, 7]], dtype=numpy.int32) - else: - expected = numpy.array([[1, 2], [1, 5], [3, 7]], dtype=numpy.int32) - assert_array_equal(data, expected) - assert_cfd(data, sycl_queue, usm_type) - - # call with the same seed has to draw the same values - data = RandomState(seed, sycl_queue=sycl_queue).randint( - low=low, high=high, size=(3, 2), dtype=dtype, usm_type=usm_type - ) - assert_array_equal(data, expected) - assert_cfd(data, sycl_queue, usm_type) - - # call with omitted dimensions has to draw the first element from expected - data = RandomState(seed, sycl_queue=sycl_queue).randint( - low=low, high=high, dtype=dtype, usm_type=usm_type - ) - assert_array_equal(data, expected[0, 0]) - assert_cfd(data, sycl_queue, usm_type) - - # rand() is an alias on random_sample(), map arguments - with mock.patch("dpnp.random.RandomState.uniform") as m: - RandomState(seed).randint( - low=low, high=high, size=(3, 2), dtype=dtype, usm_type=usm_type - ) - m.assert_called_once_with( - low=low, - high=high, - size=(3, 2), - dtype=dpnp.int32, - usm_type=usm_type, - ) - - def test_float_bounds(self): - actual = RandomState(365852).randint( - low=0.6, high=6.789102534, size=(7,) - ) - if actual.sycl_device.is_cpu: - expected = numpy.array([4, 4, 3, 3, 1, 0, 3], dtype=numpy.int32) - else: - expected = numpy.array([0, 1, 4, 0, 3, 3, 3], dtype=numpy.int32) - assert_array_equal(actual, expected) - - def test_negative_bounds(self): - actual = RandomState(5143).randint(low=-15.74, high=-3, size=(2, 7)) - if actual.sycl_device.is_cpu: - expected = numpy.array( - [ - [-9, -12, -4, -12, -5, -13, -9], - [-4, -6, -13, -9, -9, -6, -15], - ], - dtype=numpy.int32, - ) - else: - expected = numpy.array( - [ - [-15, -7, -12, -5, -10, -11, -11], - [-14, -7, -7, -10, -14, -9, -6], - ], - dtype=numpy.int32, - ) - assert_array_equal(actual, expected) - - def test_negative_interval(self): - rs = RandomState(3567) - - assert -5 <= rs.randint(-5, -1) < -1 - - x = rs.randint(-7, -1, 5) - assert_equal(-7 <= x, True, strict=False) - assert_equal(x < -1, True, strict=False) - - def test_bounds_checking(self): - dtype = dpnp.int32 - func = RandomState().randint - low = dpnp.iinfo(dtype).min - high = dpnp.iinfo(dtype).max - - # inf can't be converted to int boundary - assert_raises(OverflowError, func, -numpy.inf, 0) - assert_raises(OverflowError, func, 0, numpy.inf) - assert_raises(OverflowError, func, -numpy.inf, numpy.inf) - - # either low < min_int or high > max_int is prohibeted - assert_raises(OverflowError, func, low - 1, 0, dtype=dtype) - assert_raises(OverflowError, func, -1, high + 1, dtype=dtype) - assert_raises(OverflowError, func, low - 1, high + 1, dtype=dtype) - - # low >= high is prohibeted - assert_raises(ValueError, func, 1, 1, dtype=dtype) - assert_raises(ValueError, func, high, low, dtype=dtype) - assert_raises(ValueError, func, 1, 0, dtype=dtype) - assert_raises(ValueError, func, high, low / 2, dtype=dtype) - - def test_rng_zero_and_extremes(self): - dtype = dpnp.int32 - func = RandomState().randint - low = dpnp.iinfo(dtype).min - high = dpnp.iinfo(dtype).max - - sycl_device = dpctl.SyclQueue().sycl_device - if sycl_device.has_aspect_gpu and not sycl_device.has_aspect_fp64: - # TODO: discuss with oneMKL - pytest.skip( - f"Due to some reason, oneMKL wrongly returns high value instead of low" - ) - - tgt = high - 1 - assert_equal(func(tgt, tgt + 1, size=1000), tgt, strict=False) - - tgt = low - assert_equal(func(tgt, tgt + 1, size=1000), tgt, strict=False) - - tgt = (low + high) // 2 - assert_equal(func(tgt, tgt + 1, size=1000), tgt, strict=False) - - def test_full_range(self): - dtype = dpnp.int32 - low = dpnp.iinfo(dtype).min - high = dpnp.iinfo(dtype).max - - try: - RandomState().randint(low, high) - except Exception as e: - raise AssertionError( - "No error should have been raised, but one was " - "with the following message:\n\n%s" % str(e) - ) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - def test_in_bounds_fuzz(self): - for high in [4, 8, 16]: - vals = RandomState().randint(2, high, size=2**16) - assert vals.max() < high - assert vals.min() >= 2 - - @pytest.mark.parametrize( - "zero_size", - [(3, 0, 4), 0, (0,), ()], - ids=["(3, 0, 4)", "0", "(0,)", "()"], - ) - def test_zero_size(self, zero_size): - exp_shape = zero_size if zero_size != 0 else (0,) - assert_equal( - RandomState().randint(0, 10, size=zero_size).shape, exp_shape - ) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @pytest.mark.parametrize( - "high", - [dpnp.array([3]), numpy.array([3])], - ids=["dpnp.array([3])", "numpy.array([3])"], - ) - @pytest.mark.parametrize( - "low", - [[2], dpnp.array([2]), numpy.array([2])], - ids=["[2]", "dpnp.array([2])", "numpy.array([2])"], - ) - def test_bounds_fallback(self, low, high): - if isinstance(high, dpnp_array) and high.dtype == numpy.int32: - pytest.skip("NumPy fails: 'high' is out of bounds for int32") - - seed = 15 - size = (3, 2, 5) - - # dpnp accepts only scalar as low and/or high, in other cases it will be a fallback to numpy - actual = RandomState(seed).randint(low=low, high=high, size=size) - expected = numpy.random.RandomState(seed).randint( - low=get_array(numpy, low), high=get_array(numpy, high), size=size - ) - assert_equal(actual, expected) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @pytest.mark.parametrize( - "dtype", - [dpnp.int64, dpnp.int_, dpnp.bool, dpnp.bool_, bool], - ids=[ - "dpnp.int64", - "dpnp.int_", - "dpnp.bool", - "dpnp.bool_", - "bool", - ], - ) - def test_dtype_fallback(self, dtype): - seed = 157 - low = -3 if not dtype in {dpnp.bool_, bool} else 0 - high = 37 if not dtype in {dpnp.bool_, bool} else 2 - size = (3, 2, 5) - - if dtype == dpnp.int_ and dtype == dpnp.dtype("int32"): - pytest.skip( - "dtype is alias on dpnp.int32 on the target OS, so no fallback here" - ) - - # dtype must be int or dpnp.int32, in other cases it will be a fallback to numpy - actual = RandomState(seed).randint( - low=low, high=high, size=size, dtype=dtype - ) - expected = numpy.random.RandomState(seed).randint( - low=low, high=high, size=size, dtype=dtype - ) - assert_array_equal(actual, expected) - assert_raises(TypeError, RandomState().randint, dtype=dtype) - - @pytest.mark.parametrize( - "usm_type", ["", "unknown"], ids=["Empty", "Unknown"] - ) - def test_invalid_usm_type(self, usm_type): - # dtype must be float32 or float64 - assert_raises(ValueError, RandomState().uniform, usm_type=usm_type) - - -class TestRandN: - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, usm_type): - seed = 3649 - sycl_queue = dpctl.SyclQueue() - dtype = get_default_floating() - - data = RandomState(seed, sycl_queue=sycl_queue).randn( - 3, 2, usm_type=usm_type - ) - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array( - [ - [-0.862485623762009, 1.169492612490272], - [-0.405876118480338, 0.939006537666719], - [-0.615075625641019, 0.555260469834381], - ], - dtype=dtype, - ) - else: - expected = numpy.array( - [ - [-4.019566117504177, 7.016412093100934], - [-1.044015254820266, -0.839721616192757], - [0.545079768980527, 0.380676324099473], - ], - dtype=dtype, - ) - - # TODO: discuss with oneMKL: there is a difference between CPU and GPU - # generated samples since 9 digit while precision=15 for float64 - # precision = dpnp.finfo(numpy.float64).precision - precision = dpnp.finfo(numpy.float32).precision - assert_array_almost_equal(data, expected, decimal=precision) - - # call with the same seed has to draw the same values - data = RandomState(seed, sycl_queue=sycl_queue).randn( - 3, 2, usm_type=usm_type - ) - assert_array_almost_equal(data, expected, decimal=precision) - - # call with omitted dimensions has to draw the first element from expected - actual = RandomState(seed).randn(usm_type=usm_type) - assert_array_almost_equal(actual, expected[0, 0], decimal=precision) - - # randn() is an alias on standard_normal(), map arguments - with mock.patch("dpnp.random.RandomState.standard_normal") as m: - RandomState(seed).randn(2, 7, usm_type=usm_type) - m.assert_called_once_with(size=(2, 7), usm_type=usm_type) - - @pytest.mark.parametrize( - "dims", - [(), (5,), (10, 2, 7), (0,)], - ids=["()", "(5,)", "(10, 2, 7)", "(0,)"], - ) - def test_dims(self, dims): - size = None if len(dims) == 0 else dims - - with mock.patch("dpnp.random.RandomState.normal") as m: - RandomState().randn(*dims) - m.assert_called_once_with( - loc=0.0, scale=1.0, size=size, dtype=None, usm_type="device" - ) - - @pytest.mark.parametrize( - "zero_dims", - [(0,), (3, 0), (3, 0, 10), ()], - ids=["(0,)", "(3, 0)", "(3, 0, 10)", "()"], - ) - def test_zero_dims(self, zero_dims): - assert_equal(RandomState().randn(*zero_dims).shape, zero_dims) - - def test_wrong_dims(self): - # non-positive number in size - assert_raises(ValueError, RandomState().randn, 2, -1) - - # non-integer number in size - assert_raises(TypeError, RandomState().randn, 3.5, 2) +# method name, positional parameters, whether the samples are floats +distributions = [ + ("beta", (0.5, 2.0), True), + ("binomial", (10, 0.3), False), + ("chisquare", (3.0,), True), + ("exponential", (2.0,), True), + ("f", (5.0, 7.0), True), + ("gamma", (3.0, 2.0), True), + ("geometric", (0.3,), False), + ("gumbel", (1.0, 2.0), True), + ("hypergeometric", (10, 5, 7), False), + ("laplace", (1.0, 2.0), True), + ("logistic", (1.0, 2.0), True), + ("lognormal", (0.0, 0.5), True), + ("logseries", (0.6,), False), + ("negative_binomial", (3.0, 0.4), False), + ("noncentral_chisquare", (3.0, 2.0), True), + ("noncentral_f", (5.0, 7.0, 2.0), True), + ("normal", (1.0, 2.0), True), + ("pareto", (5.0,), True), + ("poisson", (4.0,), False), + ("power", (3.0,), True), + ("rayleigh", (2.0,), True), + ("standard_cauchy", (), True), + ("standard_exponential", (), True), + ("standard_gamma", (0.5,), True), + ("standard_normal", (), True), + ("standard_t", (5.0,), True), + ("triangular", (0.0, 1.0, 3.0), True), + ("uniform", (-1.0, 2.0), True), + ("vonmises", (0.5, 2.0), True), + ("wald", (1.0, 2.0), True), + ("weibull", (2.0,), True), + ("zipf", (3.0,), False), + ("random_sample", (), True), + ("randint", (-3, 7), False), + ("tomaxint", (), False), +] +float_distributions = [d[:2] for d in distributions if d[2]] + + +def _all_devices(): + return [ + dev for dev in dpctl.get_devices() if not dev.has_aspect_accelerator + ] + + +def _device_id(device): + # str() holds the address, which differs between pytest-xdist workers + return device.filter_string + + +class TestAPI: + def test_methods(self): + public = lambda c: {n for n in dir(c) if not n.startswith("_")} + missing = public(numpy.random.RandomState) - public(RandomState) + assert not missing + + def test_module_functions(self): + # NumPy's other bit generators are not provided + bit_generators = {"MT19937", "PCG64", "Philox", "SFC64"} + missing = set(numpy.random.__all__) - set(dpnp.random.__all__) + assert missing == bit_generators class TestSeed: - @pytest.mark.parametrize( - "func", ["normal", "standard_normal", "random_sample", "uniform"] - ) - def test_scalar(self, func): - seed = 28041997 - size = (3, 2, 4) - - rs = RandomState(seed) - a1 = getattr(rs, func)(size=size) - - rs = RandomState(seed) - a2 = getattr(rs, func)(size=size) - - precision = dpnp.finfo(numpy.float64).precision - assert_array_almost_equal(a1, a2, decimal=precision) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( "seed", [ - range(3), - numpy.arange(3, dtype=numpy.int32), - dpnp.arange(3, dtype=numpy.int32), + 0, + 4294967295, + numpy.uint32(5), [0], [4294967295], [2, 7, 15], - (1,), + [4, 3, 2, 1], (85, 6, 17), - ], - ids=[ - "range(2)", - "numpy.arange(2)", - "dpnp.arange(2)", - "[0]", - "[4294967295]", - "[2, 7, 15]", - "(1,)", - "(85, 6, 17)", + range(3), + numpy.arange(3, dtype=numpy.int32), + dpnp.arange(3, dtype=dpnp.int32), ], ) - def test_array_range(self, seed): - if is_gpu_device(): - pytest.skip("seed as a scalar is only supported on GPU") + def test_reproducible(self, seed): + a1 = RandomState(seed).random_sample(15) + a2 = RandomState(seed).random_sample(15) + assert_array_equal(a1, a2) + + def test_distinct(self): + a1 = RandomState(1).random_sample(8) + a2 = RandomState(2).random_sample(8) + assert not (a1 == a2).any() - size = 15 - a1 = RandomState(seed).uniform(size=size) - a2 = RandomState(seed).uniform(size=size) - assert dpnp.allclose(a1, a2) + def test_none(self): + a1 = RandomState().random_sample(8) + a2 = RandomState(None).random_sample(8) + assert not (a1 == a2).any() @pytest.mark.parametrize( "seed", [ + -1, + 4294967296, + [-3, 7], + (1, 7, 4294967296), + [], + (), + [[1, 2, 3]], + [[1, 2, 3], [4, 5, 6]], + numpy.array([], dtype=numpy.int64), 0.5, -1.5, [-0.3], (1.7, 3), "text", numpy.arange(0, 1, 0.5), - dpnp.arange(3, dtype=numpy.float32), - dpnp.arange(3, dtype=numpy.complex64), - ], - ids=[ - "0.5", - "-1.5", - "[-0.3]", - "(1.7, 3)", - "text", - "numpy.arange(0, 1, 0.5)", - "dpnp.arange(3, dtype=numpy.float32)", - "dpnp.arange(3, dtype=numpy.complex64)", ], ) - def test_invalid_type(self, seed): - # seed must be an unsigned 32-bit integer - assert_raises(TypeError, RandomState, seed) - - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @pytest.mark.parametrize( - "seed", - [ - -1, - [-3, 7], - (17, 3, -5), - [4, 3, 2, 1], - (7, 6, 5, 1), - range(-1, -11, -1), - numpy.arange(4, dtype=numpy.int32), - dpnp.arange(-3, 3, dtype=numpy.int32), - dpnp.iinfo(numpy.uint32).max + 1, - (1, 7, dpnp.iinfo(numpy.uint32).max + 1), - ], - ids=[ - "-1", - "[-3, 7]", - "(17, 3, -5)", - "[4, 3, 2, 1]", - "(7, 6, 5, 1)", - "range(-1, -11, -1)", - "numpy.arange(4, dtype=numpy.int32)", - "dpnp.arange(-3, 3, dtype=numpy.int32)", - "dpnp.iinfo(numpy.uint32).max + 1", - "(1, 7, dpnp.iinfo(numpy.uint32).max + 1)", - ], - ) - def test_invalid_value(self, seed): - if is_cpu_device(): - # seed must be an unsigned 32-bit integer - assert_raises(ValueError, RandomState, seed) - else: - if dpnp.isscalar(seed): - # seed must be an unsigned 64-bit integer - assert_raises(ValueError, RandomState, seed) - else: - # seed must be a scalar - assert_raises(TypeError, RandomState, seed) + def test_invalid(self, seed): + _assert_same_error( + lambda: numpy.random.RandomState(seed), + lambda: RandomState(seed), + ) @pytest.mark.parametrize( "seed", [ - [], - (), - [[1, 2, 3]], - [[1, 2, 3], [4, 5, 6]], - numpy.array([], dtype=numpy.int64), - dpnp.array([], dtype=numpy.int64), - ], - ids=[ - "[]", - "()", - "[[1, 2, 3]]", - "[[1, 2, 3], [4, 5, 6]]", - "numpy.array([], dtype=numpy.int64)", - "dpnp.array([], dtype=numpy.int64)", + dpnp.arange(3, dtype=dpnp.float32), + dpnp.array([], dtype=dpnp.int64), + dpnp.arange(-3, 3), ], ) - def test_invalid_shape(self, seed): - if is_cpu_device(): - # seed must be an unsigned or 1-D array - assert_raises(ValueError, RandomState, seed) - else: - if dpnp.isscalar(seed): - # seed must be an unsigned 64-bit scalar - assert_raises(ValueError, RandomState, seed) - else: - # seed must be a scalar - assert_raises(TypeError, RandomState, seed) + def test_invalid_dpnp_array(self, seed): + np_seed = dpnp.asnumpy(seed) + _assert_same_error( + lambda: numpy.random.RandomState(np_seed), + lambda: RandomState(seed), + ) + def test_reseed(self): + rs = RandomState(3) + rs.random_sample(5) + rs.seed(7) + assert_array_equal(rs.random_sample(5), RandomState(7).random_sample(5)) + + def test_module_seed(self): + dpnp.random.seed(7) + a1 = dpnp.random.random_sample(5) + a2 = dpnp.random.random_sample(5) + dpnp.random.seed(7) + assert_array_equal(dpnp.random.random_sample(5), a1) + assert_array_equal(dpnp.random.random_sample(5), a2) + assert_array_equal(a1, RandomState(7).random_sample(5)) + + +class TestState: + def test_roundtrip(self): + rs = RandomState(12) + rs.normal(size=3) + state = rs.get_state() + a1 = rs.normal(size=7) + rs.random_sample(3) + rs.set_state(state) + assert_array_equal(rs.normal(size=7), a1) + + def test_legacy_false(self): + rs = RandomState(12) + assert rs.get_state(legacy=False) == rs.get_state() + + def test_restore_on_new_instance(self): + rs = RandomState(12) + rs.poisson(3.0, size=10) + state = rs.get_state() + a1 = rs.poisson(3.0, size=10) + rs2 = RandomState() + rs2.set_state(state) + assert_array_equal(rs2.poisson(3.0, size=10), a1) + + def test_module_state(self): + dpnp.random.seed(5) + state = dpnp.random.get_state() + a1 = dpnp.random.random_sample(5) + dpnp.random.set_state(state) + assert_array_equal(dpnp.random.random_sample(5), a1) + + def test_numpy_state(self): + state = numpy.random.RandomState(3).get_state() + assert_raises(ValueError, RandomState().set_state, state) + + def test_not_a_state(self): + assert_raises(ValueError, RandomState().set_state, 1) + + +@pytest.fixture +def restore_bit_generator(): + saved = dpnp.random.get_bit_generator() + yield + dpnp.random.set_bit_generator(saved) + + +@pytest.mark.usefixtures("restore_bit_generator") +class TestBitGenerator: + def test_get_same_object(self): + bg = dpnp.random.get_bit_generator() + assert isinstance(bg, dpnp.random.Philox4x3210) + assert dpnp.random.get_bit_generator() is bg + + def test_module_seed_keeps_object(self): + bg = dpnp.random.get_bit_generator() + dpnp.random.seed(3) + assert dpnp.random.get_bit_generator() is bg + assert_array_equal( + dpnp.random.random_sample(5), RandomState(3).random_sample(5) + ) -class TestStandardNormal: + def test_set(self): + bg = dpnp.random.PCG64DXSM(5) + dpnp.random.set_bit_generator(bg) + assert dpnp.random.get_bit_generator() is bg + ref = dpnp.random.Generator(dpnp.random.PCG64DXSM(5)).random(6) + assert_array_equal(dpnp.random.random_sample(6), ref) + + def test_shared_stream(self): + bg = dpnp.random.PCG64DXSM(5) + dpnp.random.set_bit_generator(bg) + a = dpnp.random.random_sample(3) + b = dpnp.random.Generator(bg).random(3) + ref = dpnp.random.Generator(dpnp.random.PCG64DXSM(5)).random(6) + assert_array_equal(dpnp.concatenate((a, b)), ref) + + def test_module_seed_reseeds_in_place(self): + bg = dpnp.random.PCG64DXSM(5) + dpnp.random.set_bit_generator(bg) + dpnp.random.random_sample(3) + dpnp.random.seed(9) + assert dpnp.random.get_bit_generator() is bg + assert bg.state == dpnp.random.PCG64DXSM(9).state + ref = dpnp.random.Generator(dpnp.random.PCG64DXSM(9)).random(4) + assert_array_equal(dpnp.random.random_sample(4), ref) + + def test_queue(self): + queue = dpctl.SyclQueue() + bg = dpnp.random.PCG64DXSM(1, sycl_queue=queue) + dpnp.random.set_bit_generator(bg) + assert dpnp.random.get_bit_generator(sycl_queue=queue) is bg + assert dpnp.random.get_bit_generator() is not bg + + res = dpnp.random.random_sample(4, sycl_queue=queue) + assert res.sycl_queue == queue + gen = dpnp.random.Generator(dpnp.random.PCG64DXSM(1, sycl_queue=queue)) + assert_array_equal(res, gen.random(4)) + + @pytest.mark.parametrize("xp", [dpnp, numpy]) + def test_class(self, xp): + # NumPy keeps the class as its global bit generator after raising + saved = numpy.random.get_bit_generator() + bg_type = xp.random.PCG64DXSM + try: + assert_raises(ValueError, xp.random.set_bit_generator, bg_type) + finally: + numpy.random.set_bit_generator(saved) + + @pytest.mark.parametrize("bitgen", [None, 5, numpy.random.PCG64DXSM(1)]) + def test_not_a_bit_generator(self, bitgen): + assert_raises(TypeError, dpnp.random.set_bit_generator, bitgen) + + +class TestBitGeneratorSeed: + def test_random_state(self): + bg = dpnp.random.PCG64DXSM(7) + rs = RandomState(bg) + assert str(rs) == "RandomState(PCG64DXSM)" + assert rs.get_sycl_queue() == bg.sycl_queue + ref = dpnp.random.Generator(dpnp.random.PCG64DXSM(7)).random(5) + assert_array_equal(rs.random_sample(5), ref) + + def test_state(self): + rs = RandomState(dpnp.random.PCG64DXSM(7)) + state = rs.get_state() + assert state["bit_generator"] == "PCG64DXSM" + a1 = rs.random_sample(5) + rs.set_state(state) + assert_array_equal(rs.random_sample(5), a1) + philox_state = RandomState(7).get_state() + assert_raises(ValueError, rs.set_state, philox_state) + + @pytest.mark.parametrize("xp", [dpnp, numpy]) + def test_reseed(self, xp): + # only the legacy bit generator can be reseeded + rs = xp.random.RandomState(xp.random.PCG64DXSM(7)) + assert_raises(TypeError, rs.seed, 3) + + @pytest.mark.parametrize("kw", ["device", "sycl_queue"]) + def test_queue_kwarg(self, kw): + bg = dpnp.random.PCG64DXSM(7) + queue = dpctl.SyclQueue() + assert_raises(ValueError, RandomState, bg, **{kw: queue}) + + +class TestQueue: + @pytest.mark.parametrize("device", _all_devices(), ids=_device_id) + def test_device(self, device): + rs = RandomState(1, device=device) + assert rs.get_sycl_device() == device + res = rs.normal(size=5) + assert res.sycl_device == device + + @pytest.mark.parametrize("device", _all_devices(), ids=_device_id) + def test_sycl_queue(self, device): + queue = dpctl.SyclQueue(device) + rs = RandomState(1, sycl_queue=queue) + assert rs.get_sycl_queue() == queue + assert rs.normal(size=5).sycl_queue == queue + + @pytest.mark.parametrize("name, params, is_float", distributions) + def test_device_independent(self, name, params, is_float): + devices = _all_devices() + if len(devices) < 2: + pytest.skip("a single device") + + results = [] + for device in devices: + rs = RandomState(1234, device=device) + kw = {"dtype": dpnp.float32} if is_float else {} + results.append(dpnp.asnumpy(getattr(rs, name)(*params, 64, **kw))) + for dev, res in zip(devices[1:], results[1:]): + if is_float: + # the streams match, but the math functions of the devices + # round differently, which e.g. vonmises amplifies + assert_allclose(res, results[0], rtol=1e-3, atol=1e-5) + elif dev.has_aspect_fp64 == devices[0].has_aspect_fp64: + # the discrete samplers compute in float32 without fp64, + # and a float rounding may rarely take a rejection sampler + # down another path + assert numpy.mean(res != results[0]) < 0.05 + + def test_follows_param_queue(self): + queue = dpctl.SyclQueue() + lam = dpnp.asarray([1.0, 2.0], sycl_queue=queue) + res = dpnp.random.poisson(lam) + assert res.sycl_queue == queue + + x = dpnp.arange(5, sycl_queue=queue) + assert dpnp.random.permutation(x).sycl_queue == queue + assert dpnp.random.choice(x, 3).sycl_queue == queue + + +class TestDistributions: @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, usm_type): - seed = 1234567 - sycl_queue = dpctl.SyclQueue() - dtype = get_default_floating() - - data = RandomState(seed, sycl_queue=sycl_queue).standard_normal( - size=(4, 2), usm_type=usm_type - ) - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array( - [ - [0.112455902594571, -0.249919829443642], - [0.702423540827815, 1.548132130318456], - [0.947364919775284, -0.432257289195464], - [0.736848611436872, 1.557284323302839], - ], - dtype=dtype, - ) + @pytest.mark.parametrize("name, params, is_float", distributions) + def test_shape_dtype(self, name, params, is_float, usm_type): + rs = RandomState(0) + meth = getattr(rs, name) + + res = meth(*params, size=(3, 4), usm_type=usm_type) + assert isinstance(res, dpnp.ndarray) + assert res.shape == (3, 4) + assert res.usm_type == usm_type + if is_float: + assert res.dtype == dpnp.default_float_type() else: - expected = numpy.array( - [ - [-5.851946579836138, -4.415158753007455], - [0.156672323326223, 0.475834711471613], - [-1.016957125278234, 0.978587902851975], - [-0.295425067084912, 1.438622345507964], - ], - dtype=dtype, - ) + assert res.dtype == dpnp.int64 - # TODO: discuss with oneMKL: there is a difference between CPU and GPU - # generated samples since 9 digit while precision=15 for float64 - # precision = dpnp.finfo(numpy.float64).precision - precision = dpnp.finfo(numpy.float32).precision - assert_array_almost_equal(data, expected, decimal=precision) + assert meth(*params).shape == () + assert meth(*params, size=0).shape == (0,) - # call with the same seed has to draw the same values - data = RandomState(seed, sycl_queue=sycl_queue).standard_normal( - size=(4, 2), usm_type=usm_type - ) - assert_array_almost_equal(data, expected, decimal=precision) + @pytest.mark.parametrize("dtype", float_dtypes) + @pytest.mark.parametrize("name, params", float_distributions) + def test_dtype(self, name, params, dtype): + res = getattr(RandomState(0), name)(*params, size=5, dtype=dtype) + assert res.dtype == dtype - # call with omitted dimensions has to draw the first element from expected - actual = RandomState(seed).standard_normal(usm_type=usm_type) - assert_array_almost_equal(actual, expected[0, 0], decimal=precision) + @pytest.mark.parametrize( + "dtype", [dpnp.float16, dpnp.int32, dpnp.complex64, dpnp.bool] + ) + @pytest.mark.parametrize("name, params", float_distributions) + def test_bad_dtype(self, name, params, dtype): + meth = getattr(RandomState(0), name) + assert_raises(TypeError, meth, *params, size=5, dtype=dtype) + + @pytest.mark.parametrize("usm_type", ["", "unknown"]) + def test_bad_usm_type(self, usm_type): + assert_raises(ValueError, RandomState().normal, usm_type=usm_type) - # random_sample() is an alias on uniform(), map arguments - with mock.patch("dpnp.random.RandomState.normal") as m: - RandomState(seed).standard_normal((4, 2), usm_type=usm_type) - m.assert_called_once_with( - loc=0.0, scale=1.0, size=(4, 2), dtype=None, usm_type=usm_type + @pytest.mark.parametrize("name, params, is_float", distributions) + def test_moments(self, name, params, is_float): + # the sample means agree with the ones of NumPy within six standard + # deviations + n = 2**16 + np_res = getattr(numpy.random.RandomState(0), name)(*params, n) + if name == "tomaxint" and numpy.dtype("l").itemsize < 8: + # NumPy draws C longs, which are 32-bit on Windows + np_res = numpy.random.RandomState(0).randint( + 0, 2**63 - 1, n, dtype=numpy.int64 ) + res = dpnp.asnumpy(getattr(RandomState(0), name)(*params, n)) + if name == "standard_cauchy": + np_res, res = numpy.median(np_res), numpy.median(res) + assert abs(res - np_res) < 0.05 + return + tol = 6 * numpy.sqrt(np_res.var() / n + res.var() / n) + assert abs(res.mean() - np_res.mean()) <= tol @pytest.mark.parametrize( - "size", - [(), (5,), (10, 2, 7), (0,)], - ids=["()", "(5,)", "(10, 2, 7)", "(0,)"], + "name, args", + [ + ("dirichlet", ([1.0, 2.0, 3.0],)), + ("multinomial", (10, [0.2, 0.3, 0.5])), + ("multivariate_normal", ([0.0, 1.0], [[2.0, 0.5], [0.5, 1.0]])), + ], ) - def test_sizes(self, size): - with mock.patch("dpnp.random.RandomState.normal") as m: - RandomState().standard_normal(size) - m.assert_called_once_with( - loc=0.0, scale=1.0, size=size, dtype=None, usm_type="device" - ) - - def test_wrong_dims(self): - # non-positive number in size - assert_raises(ValueError, RandomState().standard_normal, (2, -1)) + @pytest.mark.parametrize("size", [None, 4, (2, 3)]) + def test_multivariate_shape(self, name, args, size): + np_res = getattr(numpy.random.RandomState(0), name)(*args, size) + res = getattr(RandomState(0), name)(*args, size) + assert res.shape == np_res.shape - # non-integer number in size - assert_raises(TypeError, RandomState().standard_normal, (3.5, 2)) + @pytest.mark.parametrize("name, params, is_float", distributions) + def test_module_function(self, name, params, is_float): + if name == "tomaxint": + pytest.skip("not a module function") + dpnp.random.seed(11) + res = getattr(dpnp.random, name)(*params, size=5, usm_type="shared") + assert res.usm_type == "shared" + assert_array_equal(res, getattr(RandomState(11), name)(*params, 5)) -class TestRandSample: - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, usm_type): - seed = 12657 - sycl_queue = dpctl.SyclQueue() - dtype = get_default_floating() - data = RandomState(seed, sycl_queue=sycl_queue).random_sample( - size=(4, 2), usm_type=usm_type - ) - if sycl_queue.sycl_device.is_cpu: - expected = numpy.array( - [ - [0.1887628440745175, 0.2763057765550911], - [0.3973943444434553, 0.2975987731479108], - [0.4144027342554182, 0.2636592474300414], - [0.6129623607266694, 0.2596735346596688], - ], - dtype=dtype, - ) - else: - expected = numpy.array( - [ - [0.219563950354e-13, 0.6500454867400344], - [0.8847833902913576, 0.9030532521302965], - [0.2943803743033427, 0.2688879158061396], - [0.2730219631925900, 0.8695396883048091], - ], - dtype=dtype, - ) - - precision = dpnp.finfo(dtype).precision - assert_array_almost_equal(data, expected, decimal=precision) - - # call with omitted dimensions has to draw the first element from expected - data = RandomState(seed, sycl_queue=sycl_queue).random_sample( - usm_type=usm_type +class TestSize: + @pytest.mark.parametrize( + "size", + [ + True, + [True], + numpy.bool_(True), + numpy.array(True), + numpy.array([True]), + -1, + (2, -1), + 2.0, + (3.5, 2), + numpy.array([[2]]), + ], + ) + def test_invalid(self, size): + _assert_same_error( + lambda: numpy.random.RandomState(0).uniform(size=size), + lambda: RandomState(0).uniform(size=size), ) - assert_array_almost_equal(data, expected[0, 0], decimal=precision) - # random_sample() is an alias on uniform(), map arguments - with mock.patch("dpnp.random.RandomState.uniform") as m: - RandomState(seed).random_sample((4, 2), usm_type=usm_type) - m.assert_called_once_with( - low=0.0, high=1.0, size=(4, 2), dtype=None, usm_type=usm_type - ) @pytest.mark.parametrize( "size", - [(), (5,), (10, 2, 7), (0,)], - ids=["()", "(5,)", "(10, 2, 7)", "(0,)"], + [ + (numpy.int64(31), 63), + (numpy.int64(31), numpy.int64(31)), + numpy.array(1), + numpy.array([2]), + dpnp.array(3), + ], ) - def test_sizes(self, size): - with mock.patch("dpnp.random.RandomState.uniform") as m: - RandomState().random_sample(size) - m.assert_called_once_with( - low=0.0, high=1.0, size=size, dtype=None, usm_type="device" - ) - - def test_wrong_dims(self): - # non-positive number in size - assert_raises(ValueError, RandomState().random_sample, (2, -1)) + def test_castable_to_integer(self, size): + res = RandomState().uniform(size=size) + exp = numpy.random.RandomState().uniform(size=dpnp.asnumpy(size)) + assert res.shape == exp.shape + + @pytest.mark.parametrize("dims", [(), (5,), (10, 2, 7), (0,), (3, 0)]) + @pytest.mark.parametrize("name", ["rand", "randn"]) + def test_rand_dims(self, name, dims): + res = getattr(RandomState(), name)(*dims) + assert res.shape == dims + + @pytest.mark.parametrize("dims", [(2, -1), (3.5, 2)]) + @pytest.mark.parametrize("name", ["rand", "randn"]) + def test_rand_bad_dims(self, name, dims): + _assert_same_error( + lambda: getattr(numpy.random.RandomState(), name)(*dims), + lambda: getattr(RandomState(), name)(*dims), + ) - # non-integer number in size - assert_raises(TypeError, RandomState().random_sample, (3.5, 2)) + @pytest.mark.parametrize("dtype", float_dtypes) + def test_rand_dtype(self, dtype): + assert RandomState().rand(2, 3, dtype=dtype).dtype == dtype + assert RandomState().randn(2, 3, dtype=dtype).dtype == dtype -class TestUniform: - @pytest.mark.parametrize( - "bounds", - [[1.23, 10.54], [10.54, 1.23]], - ids=["(low, high)=[1.23, 10.54]", "(low, high)=[10.54, 1.23]"], - ) +class TestIntegers: @pytest.mark.parametrize( - "dtype", [dpnp.float32, dpnp.float64, dpnp.int32, None] + "dtype", get_all_dtypes(no_float16=True, no_none=True, no_complex=True) ) - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_distr(self, bounds, dtype, usm_type): - seed = 28041997 - low = bounds[0] - high = bounds[1] - - sycl_queue = dpctl.SyclQueue() - func = lambda: RandomState(seed, sycl_queue=sycl_queue).uniform( - low=low, high=high, size=(3, 2), dtype=dtype, usm_type=usm_type - ) - - if dtype is dpnp.float64 and not _def_dev_has_fp64: - # default device doesn't support 'float64' type - assert_raises(RuntimeError, func) + def test_full_range(self, dtype): + if dpnp.issubdtype(dtype, dpnp.floating): + assert_raises(TypeError, RandomState().randint, 5, dtype=dtype) return - # get drawn samples by dpnp - actual = func() - - # default dtype depends on fp64 support by the device - dtype = get_default_floating() if dtype is None else dtype - if sycl_queue.sycl_device.is_cpu: - if dtype != dpnp.int32: - data = [ - [4.023770128630567, 8.87456423597643], - [2.888630247435067, 4.823004481580574], - [2.030351535445079, 4.533497077834326], - ] - expected = numpy.array(data, dtype=dtype) - precision = dpnp.finfo(dtype).precision - assert_array_almost_equal(actual, expected, decimal=precision) - else: - expected = numpy.array([[3, 8], [2, 4], [1, 4]], dtype=dtype) - assert_array_equal(actual, expected) - else: - if dtype != dpnp.int32: - data = [ - [1.230000000452886, 4.889115418092382], - [6.084098950993071, 1.682066500463302], - [3.316473517549554, 8.428297791221597], - ] - expected = numpy.array(data, dtype=dtype) - precision = dpnp.finfo(dtype).precision - assert_array_almost_equal(actual, expected, decimal=precision) - else: - expected = numpy.array([[1, 4], [5, 1], [3, 7]], dtype=dtype) - assert_array_equal(actual, expected) - - # check if compute follows data isn't broken - assert_cfd(actual, sycl_queue, usm_type) + info = numpy.iinfo(dtype) if dtype != dpnp.bool else None + low, high = (0, 2) if info is None else (info.min, info.max + 1) + res = RandomState(0).randint(low, high, size=2**12, dtype=dtype) + assert res.dtype == dtype + assert int(res.min()) >= low + assert int(res.max()) < high + assert len(numpy.unique(dpnp.asnumpy(res))) > 1 + + def test_default_dtype(self): + assert RandomState().randint(5).dtype == dpnp.int64 + assert RandomState().randint(5, dtype=int).dtype == dpnp.int64 + + @pytest.mark.parametrize("low, high", [(2, 4), (-7, -1), (0.6, 6.78)]) + def test_bounds(self, low, high): + res = dpnp.asnumpy(RandomState(0).randint(low, high, 2**12)) + exp = numpy.random.RandomState(0).randint(low, high, 2**12) + # NumPy's default is a C long, which is int32 on Windows + assert_equal(numpy.unique(res), numpy.unique(exp), strict=False) + + def test_one_bound(self): + res = dpnp.asnumpy(RandomState(0).randint(5, size=2**12)) + assert_equal(numpy.unique(res), numpy.arange(5)) + + def test_zero_range(self): + res = RandomState(0).randint(-3, -2, size=100) + assert (res == -3).all() + + def test_array_bounds(self): + low = dpnp.asarray([0, 10]) + res = RandomState(0).randint(low, numpy.array([2, 12]), size=(64, 2)) + res = dpnp.asnumpy(res) + assert_equal(numpy.unique(res[:, 0]), [0, 1]) + assert_equal(numpy.unique(res[:, 1]), [10, 11]) @pytest.mark.parametrize( - "dtype", [dpnp.float32, dpnp.float64, dpnp.int32, None] + "args, kwargs", + [ + ((-numpy.inf, 0), {}), + ((0, numpy.inf), {}), + ((1, 1), {}), + ((5, 2), {}), + ((-(2**31) - 1, 0), {"dtype": numpy.int32}), + ((0, 2**31 + 1), {"dtype": numpy.int32}), + ((0, 3), {"dtype": numpy.bool_}), + ((5,), {"dtype": numpy.float64}), + ((0,), {}), + ((1, [0, 1]), {}), + ((0, [0, 1]), {}), + (([-1], 3), {"dtype": numpy.uint8}), + (([0], 257), {"dtype": numpy.uint8}), + ], ) - @pytest.mark.parametrize("usm_type", list_of_usm_types) - def test_low_high_equal(self, dtype, usm_type): - seed = 28045 - low = high = 3.75 - shape = (7, 6, 20) - - func = lambda: RandomState(seed).uniform( - low=low, high=high, size=shape, dtype=dtype, usm_type=usm_type + def test_errors(self, args, kwargs): + _assert_same_error( + lambda: numpy.random.RandomState().randint(*args, **kwargs), + lambda: RandomState().randint(*args, **kwargs), ) - if dtype is dpnp.float64 and not _def_dev_has_fp64: - # default device doesn't support 'float64' type - assert_raises(RuntimeError, func) - return + def test_random_integers_none(self): + with pytest.warns(DeprecationWarning): + _assert_same_error( + lambda: numpy.random.RandomState().random_integers(None), + lambda: RandomState().random_integers(None), + ) - # get drawn samples by dpnp - actual = func() + @pytest.mark.parametrize("args", [(5,), (-2, 3)]) + def test_random_integers(self, args): + with pytest.warns(DeprecationWarning) as np_warn: + exp = numpy.random.RandomState(0).random_integers(*args, size=2**12) + with pytest.warns(DeprecationWarning) as dp_warn: + res = RandomState(0).random_integers(*args, size=2**12) + assert str(dp_warn[0].message) == str(np_warn[0].message) + assert dp_warn[0].filename == __file__ + assert res.dtype == dpnp.int64 + # NumPy returns C longs, which are int32 on Windows + assert_equal( + numpy.unique(dpnp.asnumpy(res)), numpy.unique(exp), strict=False + ) - # default dtype depends on fp64 support by the device - dtype = get_default_floating() if dtype is None else dtype - expected = numpy.full(shape=shape, fill_value=low, dtype=dtype) + @pytest.mark.parametrize("args", [(5,), (-2, 3)]) + def test_random_integers_module_warning(self, args): + with pytest.warns(DeprecationWarning, match="randint") as w: + dpnp.random.random_integers(*args) + assert len(w) == 1 + assert w[0].filename == __file__ - assert_dtype_allclose(actual, expected) + def test_tomaxint(self): + res = RandomState(0).tomaxint(2**12) + assert res.dtype == dpnp.int64 + assert (res >= 0).all() + # the high bit is random too + assert (res > 2**62).any() - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - def test_range_bounds(self): - fmin = dpnp.finfo("double").min - fmax = dpnp.finfo("double").max - func = RandomState().uniform - assert_raises(OverflowError, func, -numpy.inf, 0) - assert_raises(OverflowError, func, 0, numpy.inf) - assert_raises(OverflowError, func, -numpy.inf, numpy.inf) - assert_raises(OverflowError, func, fmin, fmax) - assert_raises(OverflowError, func, [-numpy.inf], [0]) - assert_raises(OverflowError, func, [0], [numpy.inf]) +class TestLegacy: + """The behaviors of RandomState that differ from the ones of Generator.""" - func(low=numpy.nextafter(fmin, 0), high=numpy.nextafter(fmax, 0)) + nan = numpy.nan - @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @pytest.mark.parametrize( - "high", - [dpnp.array([3]), numpy.array([3])], - ids=["dpnp.array([3])", "numpy.array([3])"], - ) @pytest.mark.parametrize( - "low", - [[2], dpnp.array([2]), numpy.array([2])], - ids=["[2]", "dpnp.array([2])", "numpy.array([2])"], + "name, args", + [ + ("uniform", (-numpy.inf, 0.0)), + ("uniform", (0.0, numpy.inf)), + ("uniform", (-1e308, 1e308)), + ("uniform", ([-numpy.inf], [0.0])), + ("uniform", ([0.0], nan)), + ("normal", (0.0, -1.0)), + ("normal", (0.0, -0.0)), + ("exponential", ([-1.0],)), + ("gamma", (1.0, -1.0)), + ("chisquare", (0.0,)), + ("beta", (0.0, 1.0)), + ("wald", (1.0, 0.0)), + ("triangular", (1.0, 0.0, 2.0)), + ("vonmises", (0.0, -1.0)), + ("poisson", (-1.0,)), + ("poisson", (nan,)), + ("poisson", (1e19,)), + ("binomial", (-1, 0.5)), + ("binomial", (5, 1.5)), + ("binomial", (5, nan)), + ("negative_binomial", (0.0, 0.5)), + ("negative_binomial", (-1.0, 0.5)), + ("negative_binomial", (1.0, 1.5)), + ("negative_binomial", (1.0, nan)), + ("negative_binomial", ([1.0], [nan])), + ("geometric", (0.0,)), + ("logseries", (1.0,)), + ("zipf", (1.0,)), + ("hypergeometric", (1, 2, 4)), + ("hypergeometric", ([1], 2, 4)), + ("hypergeometric", (-1, 5, 3)), + ("hypergeometric", (5, -1, 3)), + ("hypergeometric", (5, 3, 0)), + ("hypergeometric", (5, 3, -1)), + ("hypergeometric", ([5], [3], [0])), + ("dirichlet", ([-1.0, 1.0],)), + ("dirichlet", ([0.0, 1.0],)), + ("multinomial", ([5], [0.5, 0.5])), + ("multinomial", (5, 0.5)), + ("multinomial", (5, [[0.5, 0.5]])), + ("multinomial", (5, [0.7, 0.7, 0.1])), + ("multinomial", (-1, [0.2, 0.8])), + ("multinomial", (5, [-0.1, 1.1])), + ("multivariate_normal", ([[0.0, 0.0]], numpy.eye(2))), + ("multivariate_normal", ([0.0, 0.0], numpy.eye(3))), + ( + "multivariate_normal", + ([0.0, 0.0], [[1.0, 2.0], [2.0, 1.0]], None, "raise"), + ), + ("permutation", (1.5,)), + ("permutation", (numpy.zeros(()),)), + ("choice", (0,)), + ("choice", (-1,)), + ("choice", (1.5,)), + ("choice", ([],)), + ("choice", ([[1, 2]],)), + ("choice", (3, 4, False)), + ("choice", (3, None, True, [0.5, 0.6, -0.1])), + ("choice", (3, None, True, [0.5, 0.6, 0.1])), + ("choice", (3, None, True, [0.5, nan, 0.5])), + ("choice", (3, None, True, [0.5, 0.5])), + ("choice", (3, None, True, [[0.5, 0.5, 0.0]])), + ("choice", (3, 2, False, [1.0, 0.0, 0.0])), + ("poisson", (None,)), + ("normal", (0.0, None)), + ("uniform", (None, 1.0)), + ("negative_binomial", (None, 0.5)), + ("exponential", ("x",)), + ("multinomial", (None, [0.5, 0.5])), + ("multinomial", ("5", [0.5, 0.5])), + ("multinomial", (5, "x")), + ], ) - def test_fallback(self, low, high): - seed = 15 - size = (3, 2, 5) - - sycl_queue = dpctl.SyclQueue() - data = RandomState(seed, sycl_queue=sycl_queue).uniform( - low=low, high=high, size=size + @pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning") + def test_same_error_as_numpy(self, name, args): + _assert_same_error( + lambda: getattr(numpy.random.RandomState(0), name)(*args), + lambda: getattr(RandomState(0), name)(*args), ) - # dpnp accepts only scalar as low and/or high, in other cases it will be a fallback to numpy - expected = numpy.random.RandomState(seed).uniform( - low=get_array(numpy, low), high=get_array(numpy, high), size=size - ) - - precision = dpnp.finfo(get_default_floating()).precision - assert_array_almost_equal(data, expected, decimal=precision) - - # check if compute follows data isn't broken - assert_cfd(data, sycl_queue) - @pytest.mark.parametrize( - "dtype", + "name, args", [ - dpnp.float16, - float, - dpnp.int64, - dpnp.int_, - int, - numpy.clongdouble, - dpnp.complex128, - dpnp.complex64, - dpnp.bool, - dpnp.bool_, - ], - ids=[ - "dpnp.float16", - "float", - "dpnp.int64", - "dpnp.int_", - "int", - "numpy.clongdouble", - "dpnp.complex128", - "dpnp.complex64", - "dpnp.bool", - "dpnp.bool_", + ("beta", ([1.0, -1.0], 1.0)), + ("binomial", ([3, -1], 0.5)), + ("binomial", (3, [0.5, 1.5])), + ("exponential", ([1.0, -1.0],)), + ("geometric", ([0.5, 0.0],)), + ("logseries", ([0.5, 1.0],)), + ("negative_binomial", ([1.0, 0.0], 0.5)), + ("noncentral_f", (1.0, [1.0, 0.0], 1.0)), + ("normal", (0.0, [1.0, -1.0])), + ("poisson", ([1.0, 1e19],)), + ("poisson", ([1.0, nan],)), + ("standard_gamma", ([1.0, -1.0],)), + ("zipf", ([2.0, 1.0],)), + ("hypergeometric", ([3, -1], 3, 2)), + ("hypergeometric", ([3, 1], 3, [2, 5])), + ("hypergeometric", ([3, 1], 3, [2, 0])), + ("triangular", ([0.0, 2.0], 1.0, 3.0)), + ("triangular", ([0.0, 3.0], 3.0, 3.0)), + ("uniform", ([0.0, -numpy.inf], 1.0)), + ("randint", ([0, 5], [3, 4])), + ("randint", ([0, -1], 3, None, "uint8")), + ("randint", (0, [3, 300], None, "uint8")), + ("choice", (3, 2, True, [0.5, 0.5, 0.5])), + ("choice", (3, 2, True, [0.5, -0.5, 1.0])), + ("choice", (3, 2, True, [0.5, nan, 0.5])), + ("choice", (3, 3, False, [0.5, 0.5, 0.0])), + ("multinomial", (3, [0.5, 1.5])), + ("multinomial", (3, [0.7, 0.7, 0.0])), + ("dirichlet", ([1.0, 0.0],)), ], ) - def test_invalid_dtype(self, dtype): - if dtype == dpnp.int_ and dtype == dpnp.dtype("int32"): - pytest.skip( - "dtype is alias on dpnp.int32 on the target OS, so no error here" - ) - - # dtype must be int32, float32 or float64 - assert_raises(TypeError, RandomState().uniform, dtype=dtype) + def test_same_error_as_numpy_on_device(self, name, args): + # unlike Generator, RandomState checks the parameters on the device + dp_args = [dpnp.asarray(a) if isinstance(a, list) else a for a in args] + _assert_same_error( + lambda: getattr(numpy.random.RandomState(0), name)(*args), + lambda: getattr(RandomState(0), name)(*dp_args), + ) + def test_uniform_reversed(self): + res = RandomState(0).uniform(2.0, 1.0, size=100) + assert ((res > 1.0) & (res <= 2.0)).all() + + def test_negative_binomial_accepted(self): + # NumPy's legacy checks allow p == 0 and a NaN n + rs = RandomState(0) + rs.negative_binomial(1.0, 0.0, size=4) + rs.negative_binomial(self.nan, 0.5, size=4) + + def test_hypergeometric_limit(self): + # unlike NumPy's legacy checks, the ones of Generator apply + assert_raises(ValueError, RandomState().hypergeometric, 10**9, 1, 1) + + def test_multinomial_empty(self): + res = RandomState(0).multinomial(5, [], size=3) + exp = numpy.random.RandomState(0).multinomial(5, [], size=3) + assert res.shape == exp.shape + assert res.dtype == dpnp.int64 + + def test_multinomial_float_n(self): + res = RandomState(0).multinomial(5.7, [0.5, 0.5], size=10) + assert (res.sum(axis=-1) == 5).all() + + def test_dirichlet_nan(self): + RandomState(0).dirichlet([self.nan, 1.0]) + + def test_multivariate_normal_svd(self): + cov = [[2.0, 1.0], [1.0, 2.0]] + res = RandomState(0).multivariate_normal([1.0, -1.0], cov, 2**16) + assert_allclose(dpnp.mean(res, axis=0), [1.0, -1.0], atol=0.05) + assert_allclose(numpy.cov(dpnp.asnumpy(res).T), cov, atol=0.05) + + def test_not_positive_semidefinite_warns(self): + cov = [[1.0, 2.0], [2.0, 1.0]] + with pytest.warns(RuntimeWarning, match="positive-semidefinite") as w: + RandomState(0).multivariate_normal([0.0, 0.0], cov) + assert w[0].filename == __file__ + with pytest.warns(RuntimeWarning, match="positive-semidefinite") as w: + dpnp.random.multivariate_normal([0.0, 0.0], cov) + assert w[0].filename == __file__ + + +class TestPermutations: @pytest.mark.parametrize( - "usm_type", ["", "unknown"], ids=["Empty", "Unknown"] + "dtype", get_all_dtypes(no_bool=True, no_none=True, no_complex=True) ) - def test_invalid_usm_type(self, usm_type): - # dtype must be float32 or float64 - assert_raises(ValueError, RandomState().uniform, usm_type=usm_type) - - def test_size_castable_to_integer(self): - M = numpy.int64(31) - N = numpy.int64(31) - K = 63 # plain Python int - - sizes = [(M, K), (M, N), (K, N)] - for size in sizes: - result = RandomState().uniform(size=size) - assert result.shape == size - - @testing.with_requires("numpy>=2.3.2") - @pytest.mark.parametrize("xp", [numpy, dpnp]) + def test_shuffle(self, dtype): + x = dpnp.arange(10, dtype=dtype) + RandomState(0).shuffle(x) + assert not (x == dpnp.arange(10, dtype=dtype)).all() + assert_array_equal(dpnp.sort(x), dpnp.arange(10, dtype=dtype)) + + def test_shuffle_reproducible(self): + x1 = dpnp.arange(10) + x2 = dpnp.arange(10) + RandomState(4).shuffle(x1) + RandomState(4).shuffle(x2) + assert_array_equal(x1, x2) + + def test_shuffle_rows(self): + x = dpnp.arange(20).reshape(10, 2) + RandomState(0).shuffle(x) + x = dpnp.asnumpy(x) + assert (x[:, 1] - x[:, 0] == 1).all() + assert_array_equal(numpy.sort(x[:, 0]), numpy.arange(0, 20, 2)) + @pytest.mark.parametrize( - "size", - [True, [True], dpnp.bool(True), numpy.array(True), numpy.array([True])], + "x", [[1, 2, 3], numpy.arange(3)], ids=["list", "numpy.ndarray"] ) - def test_bool_size(self, xp, size): - rs = xp.random.RandomState() - assert_raises(TypeError, rs.uniform, size=size) - - @pytest.mark.parametrize("size", [numpy.array(1), numpy.array([2])]) - def test_numpy_ndarray_size(self, size): - result = RandomState().uniform(size=size) - assert result.shape == size - - def test_dpnp_ndarray_size(self): - assert_raises(ValueError, RandomState().uniform, size=dpnp.array(1)) + def test_shuffle_not_dpnp_array(self, x): + # unlike NumPy, only a dpnp.ndarray is shuffled, like in CuPy + assert_raises(TypeError, RandomState().shuffle, x) + + def test_permutation_int(self): + res = RandomState(0).permutation(10) + assert res.dtype == dpnp.int64 + assert_array_equal(dpnp.sort(res), dpnp.arange(10)) + + def test_permutation_array(self): + x = dpnp.arange(20).reshape(10, 2) + res = RandomState(0).permutation(x) + assert_array_equal(x, dpnp.arange(20).reshape(10, 2)) + assert_array_equal(dpnp.sort(res[:, 0]), dpnp.arange(0, 20, 2)) + + def test_choice(self): + res = RandomState(0).choice(5, 2**12) + assert_equal(numpy.unique(dpnp.asnumpy(res)), numpy.arange(5)) + + res = RandomState(0).choice(5, 5, replace=False) + assert_array_equal(dpnp.sort(res), dpnp.arange(5)) + + res = RandomState(0).choice(5, 2**12, p=[0.0, 0.5, 0.0, 0.5, 0.0]) + assert_equal(numpy.unique(dpnp.asnumpy(res)), [1, 3]) + + def test_bytes(self): + res = RandomState(0).bytes(13) + assert isinstance(res, bytes) + assert len(res) == 13 diff --git a/dpnp/tests/test_sycl_queue.py b/dpnp/tests/test_sycl_queue.py index a315c6ff6bb..77e05b8b401 100644 --- a/dpnp/tests/test_sycl_queue.py +++ b/dpnp/tests/test_sycl_queue.py @@ -852,10 +852,15 @@ def test_vecmat(device, shape1, shape2): [], {"low": 2, "high": 15, "size": (4, 8, 16), "dtype": dpnp.int32}, ), - pytest.param("randn", [], {"d0": 20}), + pytest.param("randn", [20], {}), pytest.param("random", [], {"size": (35, 45)}), pytest.param( - "random_integers", [], {"low": -17, "high": 3, "size": (12, 16)} + "random_integers", + [], + {"low": -17, "high": 3, "size": (12, 16)}, + marks=pytest.mark.filterwarnings( + "ignore:This function is deprecated:DeprecationWarning" + ), ), pytest.param("random_sample", [], {"size": (7, 7)}), pytest.param("ranf", [], {"size": (10, 7, 12)}), @@ -1016,6 +1021,30 @@ def test_generator_permutations(func, device): assert_sycl_queue_equal(res.sycl_queue, sycl_queue) +@pytest.mark.parametrize( + "func, args", + [ + pytest.param("normal", ["f"]), + pytest.param("permutation", ["f"]), + pytest.param("poisson", ["f"]), + pytest.param("randint", ["i"]), + pytest.param("shuffle", ["f"]), + ], +) +@pytest.mark.parametrize("device", valid_dev, ids=dev_ids) +def test_random_param_queue(func, args, device): + # with no device given, the functions use the queue of the parameters + sycl_queue = dpctl.SyclQueue(device) + x = { + "f": dpnp.full(4, 2.0, sycl_queue=sycl_queue), + "i": dpnp.full(4, 5, sycl_queue=sycl_queue), + } + res = getattr(dpnp.random, func)(*[x[a] for a in args]) + if res is None: + res = x[args[0]] + assert_sycl_queue_equal(res.sycl_queue, sycl_queue) + + # "f", "i" and "p" stand for float, integer and probability arrays on another # queue than the generator's _diff_queue_methods = [ @@ -1074,6 +1103,44 @@ def test_generator_diff_queue(func, args, device): getattr(rng, func)(*args, **kwargs) +@pytest.mark.parametrize( + "func, args", _diff_queue_methods + [pytest.param("randint", ["i"])] +) +@pytest.mark.parametrize("device", valid_dev, ids=dev_ids) +def test_random_state_diff_queue(func, args, device): + rs = dpnp.random.RandomState(1, device=device) + args = _diff_queue_args(args, device) + with assert_raises(ExecutionPlacementError): + getattr(rs, func)(*args) + if func != "shuffle": # shuffle has no device keyword, as in NumPy + with assert_raises(ExecutionPlacementError): + getattr(dpnp.random, func)(*args, device=device) + + +@pytest.mark.parametrize( + "func, args", + [ + pytest.param("binomial", ["i", "p"]), + pytest.param("choice", ["f", None, True, "p"]), + pytest.param("hypergeometric", ["i", "i", 1]), + pytest.param("normal", ["f", "f"]), + pytest.param("triangular", [0.0, "f", "f"]), + pytest.param("uniform", ["f", "f"]), + ], +) +@pytest.mark.parametrize("device", valid_dev, ids=dev_ids) +def test_random_func_params_diff_queue(func, args, device): + # each array on its own queue + args = [ + _diff_queue_args([a], device)[0] if isinstance(a, str) else a + for a in args + ] + n_states = len(dpnp.random.dpnp_iface_random._dpnp_random_states) + with assert_raises(ExecutionPlacementError): + getattr(dpnp.random, func)(*args) + assert len(dpnp.random.dpnp_iface_random._dpnp_random_states) == n_states + + @pytest.mark.parametrize("device", valid_dev, ids=dev_ids) def test_einsum(device): array_list = [] diff --git a/dpnp/tests/test_usm_type.py b/dpnp/tests/test_usm_type.py index 6550d1f2e3d..ffc2c52742b 100644 --- a/dpnp/tests/test_usm_type.py +++ b/dpnp/tests/test_usm_type.py @@ -1866,7 +1866,7 @@ def test_tensorsolve(self, usm_type, usm_type_other): ) @pytest.mark.parametrize("usm_type", list_of_usm_types) def test_generator(func, args, kwargs, usm_type): - for rng in (dpnp.random.default_rng(1),): + for rng in (dpnp.random.default_rng(1), dpnp.random.RandomState(1)): meth = getattr(rng, func, None) if meth is None: # a Generator method RandomState doesn't have continue @@ -1894,6 +1894,7 @@ def test_generator_permutations(usm_type): assert rng.permutation(x, axis=1).usm_type == usm_type assert rng.permuted(x).usm_type == usm_type assert rng.permuted(x, axis=1).usm_type == usm_type + assert dpnp.random.RandomState(1).permutation(x).usm_type == usm_type rng.shuffle(x) assert x.usm_type == usm_type diff --git a/dpnp/tests/third_party/cupy/random_tests/common_distributions.py b/dpnp/tests/third_party/cupy/random_tests/common_distributions.py index c7f7f977631..e6204463645 100644 --- a/dpnp/tests/third_party/cupy/random_tests/common_distributions.py +++ b/dpnp/tests/third_party/cupy/random_tests/common_distributions.py @@ -2,11 +2,17 @@ import unittest import numpy +import pytest import dpnp as cupy +from dpnp.tests.helper import has_support_aspect64 from dpnp.tests.third_party.cupy import testing from dpnp.tests.third_party.cupy.testing import _condition +_requires_fp64 = pytest.mark.skipif( + not has_support_aspect64(), reason="fp64 is required" +) + def two_sample_Kolmogorov_Smirnov_test(observed1, observed2): """Computes the Kolmogorov-Smirnov statistic on 2 samples @@ -158,6 +164,7 @@ def test_uniform(self): assert cupy.all(result >= cupy.asarray(low).min()) assert cupy.all(result < cupy.asarray(high).max()) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_uniform_ks(self): if isinstance(self.low, list) or isinstance(self.high, list): @@ -187,6 +194,7 @@ def test_beta(self): b = cupy.array(self.b) self.generate(a, b, size=(3, 5)) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_beta_ks(self): if isinstance(self.a, list) or isinstance(self.b, list): @@ -259,6 +267,7 @@ class Exponential: def test_exponential(self): self.generate(scale=self.scale, size=(3, 2)) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_exponential_ks(self): self.check_ks(0.05)(self.scale, size=2000) @@ -305,6 +314,7 @@ def test_gamma_1(self): def test_gamma_2(self): self.generate(shape=self.shape, size=(3, 2)) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_gamma_ks(self): self.check_ks(0.05)(self.shape, self.scale, size=2000) @@ -416,6 +426,7 @@ def test_power(self): a = cupy.array(self.a) self.generate(a=a) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_power_ks(self): if not isinstance(self.a, float): @@ -466,6 +477,7 @@ def test_chisquare(self): df = cupy.array(self.df) self.generate(df=df) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_chisquare_ks(self): if not isinstance(self.df, float): @@ -493,6 +505,7 @@ def test_f(self): dfden = cupy.array(self.dfden) self.generate(dfnum, dfden) + @_requires_fp64 @_condition.repeat_with_success_at_least(10, 3) def test_f_ks(self): if isinstance(self.dfnum, list) or isinstance(self.dfden, list): diff --git a/dpnp/tests/third_party/cupy/random_tests/test_bit_generator.py b/dpnp/tests/third_party/cupy/random_tests/test_bit_generator.py index 339b2457c49..d5f5d1bdeaa 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_bit_generator.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_bit_generator.py @@ -7,8 +7,6 @@ from dpnp import random from dpnp.tests.third_party.cupy import testing -pytest.skip("bit generator is not supported yet", allow_module_level=True) - class BitGeneratorTestCase: @@ -49,6 +47,7 @@ def test_array_seed(self): @testing.with_requires("numpy>=1.17.0") @testing.fix_random() +@pytest.mark.skip("XORWOW is not provided") class TestBitGeneratorXORWOW(BitGeneratorTestCase, unittest.TestCase): def setUp(self): super().setUp() diff --git a/dpnp/tests/third_party/cupy/random_tests/test_distributions.py b/dpnp/tests/third_party/cupy/random_tests/test_distributions.py index b72edc4c90c..7a3a03e20a7 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_distributions.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_distributions.py @@ -83,7 +83,6 @@ def test_beta(self, a_dtype, b_dtype): ) class TestDistributionsBinomial(RandomDistributionsTestCase): - @pytest.mark.skip("n and p params as arrays are not supported") @testing.for_signed_dtypes("n_dtype") @testing.for_float_dtypes("p_dtype") def test_binomial(self, n_dtype, p_dtype): @@ -230,7 +229,6 @@ def test_gamma_legacy(self, shape_dtype, scale_dtype): _distributions.gamma, shape_dtype, scale_dtype, self.dtype ) - @pytest.mark.skip("no support of generator yet") @testing.for_dtypes_combination( _float_dtypes, names=["shape_dtype", "scale_dtype"] ) @@ -434,7 +432,6 @@ def test_logseries_for_invalid_p(self, p_dtype): } ) ) -@pytest.mark.skip("multivariate_normal is not fully supported yet") class TestDistributionsMultivariateNormal: def check_distribution(self, dist_func, mean_dtype, cov_dtype, dtype): @@ -648,7 +645,6 @@ def check_distribution(self, dist_func, lam_dtype, dtype=None): def test_poisson_legacy(self, lam_dtype): self.check_distribution(_distributions.poisson, lam_dtype) - @pytest.mark.skip("no support of generator yet") @testing.for_float_dtypes("lam_dtype") def test_poisson_generator(self, lam_dtype): self.check_distribution(cupy.random.default_rng(0).poisson, lam_dtype) @@ -656,7 +652,6 @@ def test_poisson_generator(self, lam_dtype): class TestDistributionsPoissonInvalid: - @pytest.mark.skip("no support of generator yet") def test_none_lam_generator(self): with pytest.raises(TypeError): cupy.random.default_rng(0).poisson(None) @@ -770,7 +765,7 @@ def test_standard_gamma_legacy(self, shape_dtype): shape = numpy.ones(self.shape_shape, dtype=shape_dtype) self.check_distribution("standard_gamma", {"shape": shape}) - @pytest.mark.skip("no support of generator yet") + @pytest.mark.skipif(not has_support_aspect64(), reason="fp64 is required") @testing.for_float_dtypes("dtype", no_float16=True) @testing.for_float_dtypes("shape_dtype") def test_standard_gamma_generator(self, shape_dtype, dtype): @@ -782,7 +777,6 @@ def test_standard_gamma_generator(self, shape_dtype, dtype): class TestDistributionsStandardGammaInvalid(RandomDistributionsTestCase): - @pytest.mark.skip("no support of generator yet") def test_none_shape_generator(self): with pytest.raises(TypeError): cupy.random.default_rng(0).standard_gamma(None) diff --git a/dpnp/tests/third_party/cupy/random_tests/test_generator.py b/dpnp/tests/third_party/cupy/random_tests/test_generator.py index af33a006b52..09c33dd28d4 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_generator.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_generator.py @@ -18,7 +18,10 @@ from . import common_distributions -pytest.skip("random.generator() is not supported yet", allow_module_level=True) +pytest.skip( + "tests the internals of CuPy's legacy random generator", + allow_module_level=True, +) def test_get_indices_preserves_uint64_cumsum_values(): diff --git a/dpnp/tests/third_party/cupy/random_tests/test_generator_api.py b/dpnp/tests/third_party/cupy/random_tests/test_generator_api.py index 4f7a5d9783b..081667f0e48 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_generator_api.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_generator_api.py @@ -8,12 +8,15 @@ import dpnp as cupy from dpnp import random +from dpnp.tests.helper import has_support_aspect64 from dpnp.tests.third_party.cupy import testing from dpnp.tests.third_party.cupy.testing import _condition from . import common_distributions -pytest.skip("random.Generator() is not supported yet", allow_module_level=True) +_requires_fp64 = pytest.mark.skipif( + not has_support_aspect64(), reason="fp64 is required" +) class GeneratorTestCase(common_distributions.BaseGeneratorTestCase): @@ -49,12 +52,15 @@ def invalid_shape(self, **kwargs): with pytest.raises(ValueError): self.generate(size=(3, 2), out=out, **kwargs) + @_requires_fp64 def test_invalid_dtype_out(self): self.invalid_dtype_out() + @_requires_fp64 def test_invalid_contiguity(self): self.invalid_contiguity() + @_requires_fp64 def test_invalid_shape(self): self.invalid_shape() @@ -127,9 +133,11 @@ class TestStandardGammaInvalid(InvalidOutsMixin, GeneratorTestCase): target_method = "standard_gamma" + @_requires_fp64 def test_invalid_dtype_out(self): self.invalid_dtype_out(shape=1.0) + @_requires_fp64 def test_invalid_contiguity(self): self.invalid_contiguity(shape=1.0) @@ -137,6 +145,7 @@ def test_invalid_contiguity(self): with pytest.raises(ValueError): self.generate(size=(4, 6), out=out, shape=1.0) + @_requires_fp64 def test_invalid_shape(self): self.invalid_shape(shape=1.0) @@ -338,6 +347,7 @@ def test_hypergeometric_nsample_equals_total(self): out = self.gen.hypergeometric(5, 10, 15, size=10) testing.assert_array_equal(out, cupy.full(10, 5, dtype=cupy.int64)) + @pytest.mark.skip("NumPy raises ValueError for nsample > ngood + nbad") def test_hypergeometric_nsample_exceeds_total(self): # nsample > ngood + nbad would previously cause an infinite # loop in the HRUA kernel. The kernel now routes this through @@ -361,13 +371,14 @@ class TestPower(common_distributions.Power, GeneratorTestCase): @testing.with_requires("numpy>=1.17.0") -@pytest.mark.skipif( - cupy.cuda.runtime.is_hip - and (int(str(cupy.cuda.runtime.runtimeGetVersion())[:3]) < 403), - reason="HIP<4.3 not supported ", -) +# @pytest.mark.skipif( +# cupy.cuda.runtime.is_hip +# and (int(str(cupy.cuda.runtime.runtimeGetVersion())[:3]) < 403), +# reason="HIP<4.3 not supported ", +# ) class TestRandomStateThreadSafe(unittest.TestCase): + @pytest.mark.skip("uses cupy.cuda.Device") def test_default_rng_thread_safe(self): def _f(func, args=()): cupy.cuda.Device().use() @@ -416,11 +427,16 @@ class TestF(common_distributions.F, GeneratorTestCase): @testing.parameterize(*common_distributions.dirichlet_params) @testing.fix_random() +@pytest.mark.skip( + "dpnp raises TypeError for a scalar alpha, as NumPy does, and two of " + "the alpha params are scalars" +) class TestDrichlet(common_distributions.Dirichlet, GeneratorTestCase): pass @testing.slow +@pytest.mark.skip("XORWOW is not provided") class TestLarge: # @pytest.mark.thread_unsafe(reason="allocates large memory") def test_large(self): diff --git a/dpnp/tests/third_party/cupy/random_tests/test_permutations.py b/dpnp/tests/third_party/cupy/random_tests/test_permutations.py index ac36d985b2c..12c8da030bf 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_permutations.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_permutations.py @@ -20,13 +20,12 @@ def _xp_random(self, xp): if self.seed is None: return xp.random else: - pytest.skip("random.RandomState.permutation() is not supported yet") return xp.random.RandomState(seed=self.seed) # Test ranks # TODO(niboshi): Fix xfail - @pytest.mark.xfail(reason="Explicit error types required") + # @pytest.mark.xfail(reason="Explicit error types required") def test_permutation_zero_dim(self): for xp in (numpy, cupy): xp_random = self._xp_random(xp) @@ -38,11 +37,6 @@ def test_permutation_zero_dim(self): @testing.for_all_dtypes(no_float16=True, no_bool=True, no_complex=True) def test_permutation_sort_1dim(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.permutation() does not support new integer dtypes." - ) cupy_random = self._xp_random(cupy) a = cupy.arange(10, dtype=dtype) b = cupy.copy(a) @@ -52,11 +46,6 @@ def test_permutation_sort_1dim(self, dtype): @testing.for_all_dtypes(no_float16=True, no_bool=True, no_complex=True) def test_permutation_sort_ndim(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.permutation() does not support new integer dtypes." - ) cupy_random = self._xp_random(cupy) a = cupy.arange(15, dtype=dtype).reshape(5, 3) b = cupy.copy(a) @@ -70,11 +59,6 @@ def test_permutation_sort_ndim(self, dtype): # @pytest.mark.thread_unsafe(reason="relies on global random state") @testing.for_all_dtypes() def test_permutation_seed1(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.permutation() does not support new integer dtypes." - ) a = testing.shaped_random((10,), cupy, dtype) b = cupy.copy(a) @@ -95,7 +79,6 @@ class TestShuffle(unittest.TestCase): # Test ranks - @pytest.mark.skip("no proper validation yet") def test_shuffle_zero_dim(self): for xp in (numpy, cupy): a = testing.shaped_random((), xp) @@ -106,11 +89,6 @@ def test_shuffle_zero_dim(self): @testing.for_all_dtypes(no_float16=True, no_bool=True, no_complex=True) def test_shuffle_sort_1dim(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.shuffle() does not support new integer dtypes." - ) a = cupy.arange(10, dtype=dtype) b = cupy.copy(a) cupy.random.shuffle(a) @@ -118,11 +96,6 @@ def test_shuffle_sort_1dim(self, dtype): @testing.for_all_dtypes(no_float16=True, no_bool=True, no_complex=True) def test_shuffle_sort_ndim(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.shuffle() does not support new integer dtypes." - ) a = cupy.arange(15, dtype=dtype).reshape(5, 3) b = cupy.copy(a) cupy.random.shuffle(a) @@ -134,11 +107,6 @@ def test_shuffle_sort_ndim(self, dtype): # @pytest.mark.thread_unsafe(reason="relies on global random state") @testing.for_all_dtypes() def test_shuffle_seed1(self, dtype): - flag = cupy.issubdtype(dtype, cupy.unsignedinteger) - if flag or dtype in [cupy.int8, cupy.int16]: - pytest.skip( - "dpnp.random.shuffle() does not support new integer dtypes." - ) a = testing.shaped_random((10,), cupy, dtype) b = cupy.copy(a) cupy.random.seed(0) @@ -204,7 +172,7 @@ def setUp(self): # Note that this is not an established method to check randomness. # TODO(anaruse): implement randomness check using some established methods. @_condition.repeat_with_success_at_least(5, 3) - @pytest.mark.skip("no support of index as numpy array") + @pytest.mark.skip("numpy.count_nonzero does not accept a dpnp.ndarray") def test_permutation_randomness(self): if self.mask > self.num_half: return diff --git a/dpnp/tests/third_party/cupy/random_tests/test_random.py b/dpnp/tests/third_party/cupy/random_tests/test_random.py index 994667bdcde..9f0db6bd5f3 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_random.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_random.py @@ -6,7 +6,7 @@ from dpnp.tests.third_party.cupy import testing -@pytest.mark.skip("random.get_random_state() is not supported yet") +@pytest.mark.skip("random.get_random_state() is CuPy-only API") class TestResetSeed(unittest.TestCase): # thread_unsafe marker requires pytest-run-parallel, not used by dpnp diff --git a/dpnp/tests/third_party/cupy/random_tests/test_sample.py b/dpnp/tests/third_party/cupy/random_tests/test_sample.py index 645d6a252ad..aae85ca68aa 100644 --- a/dpnp/tests/third_party/cupy/random_tests/test_sample.py +++ b/dpnp/tests/third_party/cupy/random_tests/test_sample.py @@ -105,7 +105,6 @@ def test_goodness_of_fit_2(self): assert _hypothesis.chi_square_test(counts, expected) -@pytest.mark.skip("high=(max+1) is not supported") class TestRandintDtype(unittest.TestCase): @testing.with_requires("numpy>=2.0") @@ -120,6 +119,7 @@ def test_dtype(self, dtype): assert low <= min(x) assert max(x) <= high + @pytest.mark.skip("dpnp.ndarray has no get()") @testing.for_int_dtypes(no_bool=True) @testing.for_dtypes([numpy.int32]) def test_dtype2(self, dtype): @@ -146,6 +146,10 @@ def test_dtype2(self, dtype): random.randint(iinfo.max - 10, iinfo.max + 2, size, dtype) +@pytest.mark.skip( + "dpnp passes the arguments of randint positionally, and low=1 for " + "high=None as NumPy does" +) class TestRandomIntegers(unittest.TestCase): def test_normal(self): @@ -171,6 +175,9 @@ def test_size_is_not_none(self): @testing.fix_random() +@pytest.mark.filterwarnings( + "ignore:This function is deprecated:DeprecationWarning" +) class TestRandomIntegers2(unittest.TestCase): @_condition.repeat(3, 10) @@ -207,7 +214,7 @@ def test_goodness_of_fit_2(self): assert _hypothesis.chi_square_test(counts, expected) -@pytest.mark.skip("random.choice() is not supported yet") +@pytest.mark.skip("mocks CuPy internals (random._generator._random_states)") class TestChoice(unittest.TestCase): def setUp(self): @@ -255,11 +262,13 @@ def test_no_none(self): class TestRandomSample(unittest.TestCase): + @pytest.mark.skip("dpnp passes size and dtype positionally") def test_rand(self): with mock.patch("dpnp.random.RandomState.random_sample") as m: random.rand(1, 2, 3) m.assert_called_once_with(size=(1, 2, 3), usm_type="device") + @pytest.mark.skip("dpnp passes size and dtype positionally") def test_rand_default_dtype(self): with mock.patch("dpnp.random.RandomState.random_sample") as m: random.rand(1, 2, 3) @@ -269,11 +278,13 @@ def test_rand_invalid_argument(self): with self.assertRaises(TypeError): random.rand(1, 2, 3, unnecessary="unnecessary_argument") + @pytest.mark.skip("dpnp passes size and dtype positionally") def test_randn(self): with mock.patch("dpnp.random.RandomState.standard_normal") as m: random.randn(1, 2, 3) m.assert_called_once_with(size=(1, 2, 3), usm_type="device") + @pytest.mark.skip("dpnp passes size and dtype positionally") def test_randn_default_dtype(self): with mock.patch("dpnp.random.RandomState.standard_normal") as m: random.randn(1, 2, 3) @@ -292,7 +303,10 @@ def test_randn_invalid_argument(self): {"size": (1, 0)}, ) @testing.fix_random() -@pytest.mark.skip("random.multinomial() is not fully supported") +@pytest.mark.skip( + "tests CuPy's internal _sample._multinominal_kernel, and asserts " + 'dtype.kind == "l", which fails for NumPy as well' +) class TestMultinomial(unittest.TestCase): def test_kernel_accepts_large_n(self):