# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pytype: skip-file
"""
Implements the NumPy API, using the primitives in :mod:`jax.lax`.
NumPy operations are implemented in Python in terms of the primitive operations
in :mod:`jax.lax`. Since NumPy operations are not primitive and instead are
implemented in terms of :mod:`jax.lax` operations, we do not need to define
transformation rules such as gradient or batching rules. Instead,
transformations for NumPy primitives can be derived from the transformation
rules for the underlying :code:`lax` primitives.
"""
import abc
import builtins
import collections
from functools import partial
import operator
import types
from typing import Sequence, FrozenSet, Optional, Tuple, Union, Set, Type, Callable
from textwrap import dedent as _dedent
import warnings
import numpy as np
import opt_einsum
import jax
from jax import jit, custom_jvp
from jax._src.numpy.vectorize import vectorize
from jax._src.numpy.util import _wraps
from jax import core
from jax._src import dtypes
from jax._src.api_util import _ensure_index_tuple
from jax import errors
from jax.core import UnshapedArray, ShapedArray, ConcreteArray, canonicalize_shape
from jax.config import config
from jax.interpreters import pxla
from jax import lax
from jax._src import device_array
from jax._src.lax.lax import _array_copy, _sort_lt_comparator, _sort_le_comparator
from jax._src.ops import scatter
from jax._src.util import (unzip2, prod as _prod, subvals, safe_zip, ceil_of_ratio,
canonicalize_axis as _canonicalize_axis, maybe_named_axis)
from jax.tree_util import tree_leaves, tree_flatten, tree_map
newaxis = None
# Common docstring additions:
_PRECISION_DOC = """\
In addition to the original NumPy arguments listed below, also supports
``precision`` for extra control over matrix-multiplication precision
on supported devices. ``precision`` may be set to ``None``, which means
default precision for the backend, a :class:`~jax.lax.Precision` enum value
(``Precision.DEFAULT``, ``Precision.HIGH`` or ``Precision.HIGHEST``) or a tuple
of two :class:`~jax.lax.Precision` enums indicating separate precision for each argument.
"""
# We replace some builtin names to follow Numpy's API, so we capture here.
_abs = builtins.abs
_all = builtins.all
_any = builtins.any
_max = builtins.max
_min = builtins.min
_sum = builtins.sum
_divmod = builtins.divmod
# NumPy constants
pi = np.pi
e = np.e
euler_gamma = np.euler_gamma
inf = np.inf
NINF = np.NINF
PZERO = np.PZERO
NZERO = np.NZERO
nan = np.nan
# NumPy utility functions
get_printoptions = np.get_printoptions
printoptions = np.printoptions
set_printoptions = np.set_printoptions
# ndarray is defined as an virtual abstract base class.
class ArrayMeta(abc.ABCMeta):
"""Metaclass for overriding ndarray isinstance checks."""
def __instancecheck__(self, instance):
# Allow tracer instances with avals that are instances of UnshapedArray.
# We could instead just declare Tracer an instance of the ndarray type, but
# there can be traced values that are not arrays. The main downside here is
# that isinstance(x, ndarray) might return true but
# issubclass(type(x), ndarray) might return false for an array tracer.
try:
return (hasattr(instance, "aval") and
isinstance(instance.aval, UnshapedArray))
except AttributeError:
super().__instancecheck__(instance)
class ndarray(metaclass=ArrayMeta):
dtype: np.dtype
ndim: int
shape: Tuple[int, ...]
size: int
def __init__(shape, dtype=None, buffer=None, offset=0, strides=None,
order=None):
raise TypeError("jax.numpy.ndarray() should not be instantiated explicitly."
" Use jax.numpy.array, or jax.numpy.zeros instead.")
@abc.abstractmethod
def __getitem__(self, key, indices_are_sorted=False,
unique_indices=False): ...
@abc.abstractmethod
def __setitem__(self, key, value): ...
@abc.abstractmethod
def __len__(self): ...
@abc.abstractmethod
def __iter__(self): ...
@abc.abstractmethod
def __reversed__(self): ...
# Comparisons
@abc.abstractmethod
def __lt__(self, other): ...
@abc.abstractmethod
def __le__(self, other): ...
@abc.abstractmethod
def __eq__(self, other): ...
@abc.abstractmethod
def __ne__(self, other): ...
@abc.abstractmethod
def __gt__(self, other): ...
@abc.abstractmethod
def __ge__(self, other): ...
# Unary arithmetic
@abc.abstractmethod
def __neg__(self): ...
@abc.abstractmethod
def __pos__(self): ...
@abc.abstractmethod
def __abs__(self): ...
@abc.abstractmethod
def __invert__(self): ...
# Binary arithmetic
@abc.abstractmethod
def __add__(self, other): ...
@abc.abstractmethod
def __sub__(self, other): ...
@abc.abstractmethod
def __mul__(self, other): ...
@abc.abstractmethod
def __matmul__(self, other): ...
@abc.abstractmethod
def __truediv__(self, other): ...
@abc.abstractmethod
def __floordiv__(self, other): ...
@abc.abstractmethod
def __mod__(self, other): ...
@abc.abstractmethod
def __divmod__(self, other): ...
@abc.abstractmethod
def __pow__(self, other): ...
@abc.abstractmethod
def __lshift__(self, other): ...
@abc.abstractmethod
def __rshift__(self, other): ...
@abc.abstractmethod
def __and__(self, other): ...
@abc.abstractmethod
def __xor__(self, other): ...
@abc.abstractmethod
def __or__(self, other): ...
@abc.abstractmethod
def __radd__(self, other): ...
@abc.abstractmethod
def __rsub__(self, other): ...
@abc.abstractmethod
def __rmul__(self, other): ...
@abc.abstractmethod
def __rmatmul__(self, other): ...
@abc.abstractmethod
def __rtruediv__(self, other): ...
@abc.abstractmethod
def __rfloordiv__(self, other): ...
@abc.abstractmethod
def __rmod__(self, other): ...
@abc.abstractmethod
def __rdivmod__(self, other): ...
@abc.abstractmethod
def __rpow__(self, other): ...
@abc.abstractmethod
def __rlshift__(self, other): ...
@abc.abstractmethod
def __rrshift__(self, other): ...
@abc.abstractmethod
def __rand__(self, other): ...
@abc.abstractmethod
def __rxor__(self, other): ...
@abc.abstractmethod
def __ror__(self, other): ...
@abc.abstractmethod
def __bool__(self): ...
@abc.abstractmethod
def __complex__(self): ...
@abc.abstractmethod
def __int__(self): ...
@abc.abstractmethod
def __float__(self): ...
@abc.abstractmethod
def __round__(self, ndigits=None): ...
@abc.abstractmethod
def __index__(self): ...
# np.ndarray methods:
@abc.abstractmethod
def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None): ...
@abc.abstractmethod
def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None): ...
@abc.abstractmethod
def argmax(self, axis: Optional[int] = None, out=None): ...
@abc.abstractmethod
def argmin(self, axis: Optional[int] = None, out=None): ...
@abc.abstractmethod
def argpartition(self, kth, axis=-1, kind='introselect', order=None): ...
@abc.abstractmethod
def argsort(self, axis: Optional[int] = -1, kind='quicksort', order=None): ...
@abc.abstractmethod
def astype(self, dtype): ...
@abc.abstractmethod
def choose(self, choices, out=None, mode='raise'): ...
@abc.abstractmethod
def clip(self, a_min=None, a_max=None, out=None): ...
@abc.abstractmethod
def compress(self, condition, axis: Optional[int] = None, out=None): ...
@abc.abstractmethod
def conj(self): ...
@abc.abstractmethod
def conjugate(self): ...
@abc.abstractmethod
def copy(self): ...
@abc.abstractmethod
def cumprod(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None): ...
@abc.abstractmethod
def cumsum(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None): ...
@abc.abstractmethod
def diagonal(self, offset=0, axis1: int = 0, axis2: int = 1): ...
@abc.abstractmethod
def dot(self, b, *, precision=None): ...
@abc.abstractmethod
def flatten(self): ...
@property
@abc.abstractmethod
def imag(self): ...
@abc.abstractmethod
def item(self, *args): ...
@abc.abstractmethod
def max(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None): ...
@abc.abstractmethod
def mean(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=False, *, where=None,): ...
@abc.abstractmethod
def min(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None): ...
@property
@abc.abstractmethod
def nbytes(self): ...
@abc.abstractmethod
def nonzero(self, *, size=None, fill_value=None): ...
@abc.abstractmethod
def prod(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None, initial=None, where=None): ...
@abc.abstractmethod
def ptp(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=False,): ...
@abc.abstractmethod
def ravel(self, order='C'): ...
@property
@abc.abstractmethod
def real(self): ...
@abc.abstractmethod
def repeat(self, repeats, axis: Optional[int] = None, *,
total_repeat_length=None): ...
@abc.abstractmethod
def reshape(self, *args, order='C'): ...
@abc.abstractmethod
def round(self, decimals=0, out=None): ...
@abc.abstractmethod
def searchsorted(self, v, side='left', sorter=None): ...
@abc.abstractmethod
def sort(self, axis: Optional[int] = -1, kind='quicksort', order=None): ...
@abc.abstractmethod
def squeeze(self, axis: Optional[Union[int, Tuple[int, ...]]] = None): ...
@abc.abstractmethod
def std(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None, ddof=0, keepdims=False, *, where=None): ...
@abc.abstractmethod
def sum(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None, initial=None, where=None): ...
@abc.abstractmethod
def swapaxes(self, axis1: int, axis2: int): ...
@abc.abstractmethod
def take(self, indices, axis: Optional[int] = None, out=None,
mode=None): ...
@abc.abstractmethod
def tobytes(self, order='C'): ...
@abc.abstractmethod
def tolist(self): ...
@abc.abstractmethod
def trace(self, offset=0, axis1: int = 0, axis2: int = 1, dtype=None,
out=None): ...
@abc.abstractmethod
def transpose(self, *args): ...
@abc.abstractmethod
def var(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None, ddof=0, keepdims=False, *, where=None): ...
@abc.abstractmethod
def view(self, dtype=None, type=None): ...
# Even though we don't always support the NumPy array protocol, e.g., for
# tracer types, for type checking purposes we must declare support so we
# implement the NumPy ArrayLike protocol.
def __array__(self): ...
# JAX extensions
@property
@abc.abstractmethod
def at(self): ...
@property
@abc.abstractmethod
def aval(self): ...
@property
@abc.abstractmethod
def weak_type(self) -> bool: ...
ndarray.register(device_array.DeviceArray)
for t in device_array.device_array_types:
ndarray.register(t)
ndarray.register(pxla._SDA_BASE_CLASS)
iscomplexobj = np.iscomplexobj
shape = _shape = np.shape
ndim = _ndim = np.ndim
size = np.size
_dtype = partial(dtypes.dtype, canonicalize=True)
# At present JAX doesn't have a reason to distinguish between scalars and arrays
# in its object system. Further, we want JAX scalars to have the same type
# promotion behaviors as JAX arrays. Rather than introducing a new type of JAX
# scalar object with JAX promotion behaviors, instead we make the JAX scalar
# types return JAX arrays when instantiated.
class _ScalarMeta(type):
def __hash__(self):
return hash(self.dtype.type)
def __eq__(self, other):
return id(self) == id(other) or self.dtype.type == other
def __ne__(self, other):
return not (self == other)
def __call__(self, x):
return array(x, dtype=self.dtype)
def __instancecheck__(self, instance):
return isinstance(instance, self.dtype.type)
def _make_scalar_type(np_scalar_type):
return _ScalarMeta(np_scalar_type.__name__, (object,),
{"dtype": np.dtype(np_scalar_type)})
bool_ = _make_scalar_type(np.bool_)
uint8 = _make_scalar_type(np.uint8)
uint16 = _make_scalar_type(np.uint16)
uint32 = _make_scalar_type(np.uint32)
uint64 = _make_scalar_type(np.uint64)
int8 = _make_scalar_type(np.int8)
int16 = _make_scalar_type(np.int16)
int32 = _make_scalar_type(np.int32)
int64 = _make_scalar_type(np.int64)
bfloat16 = _make_scalar_type(dtypes.bfloat16)
float16 = _make_scalar_type(np.float16)
float32 = single = _make_scalar_type(np.float32)
float64 = double = _make_scalar_type(np.float64)
complex64 = csingle = _make_scalar_type(np.complex64)
complex128 = cdouble = _make_scalar_type(np.complex128)
int_ = int32 if dtypes.int_ == np.int32 else int64
uint = uint32 if dtypes.uint == np.uint32 else uint64
float_ = float32 if dtypes.float_ == np.float32 else float64
complex_ = complex64 if dtypes.complex_ == np.complex64 else complex128
number = np.number
inexact = np.inexact
complexfloating = np.complexfloating
floating = np.floating
integer = np.integer
signedinteger = np.signedinteger
unsignedinteger = np.unsignedinteger
flexible = np.flexible
character = np.character
object_ = np.object_
iinfo = dtypes.iinfo
finfo = dtypes.finfo
dtype = np.dtype
can_cast = dtypes.can_cast
issubsctype = dtypes.issubsctype
promote_types = dtypes.promote_types
ComplexWarning = np.ComplexWarning
array_str = np.array_str
array_repr = np.array_repr
save = np.save
savez = np.savez
@_wraps(np.dtype)
def _jnp_dtype(obj, align=False, copy=False):
"""Similar to np.dtype, but respects JAX dtype defaults."""
if obj is None:
obj = dtypes.float_
elif isinstance(obj, type) and obj in dtypes.python_scalar_dtypes:
obj = _DEFAULT_TYPEMAP[np.dtype(obj, align=align, copy=copy).type]
return np.dtype(obj, align=align, copy=copy)
### utility functions
_DEFAULT_TYPEMAP = {
np.bool_: bool_,
np.int_: int_,
np.float_: float_,
np.complex_: complex_
}
_INT_DTYPES = {
16: np.int16,
32: np.int32,
64: np.int64,
}
def _promote_shapes(fun_name, *args):
"""Prepend implicit leading singleton dimensions for Numpy broadcasting."""
if len(args) < 2:
return args
else:
shapes = [shape(arg) for arg in args]
nonscalar_ranks = [len(shp) for shp in shapes if shp]
if not nonscalar_ranks or len(set(nonscalar_ranks)) == 1:
return args
else:
if config.jax_numpy_rank_promotion != "allow":
_rank_promotion_warning_or_error(fun_name, shapes)
result_rank = len(lax.broadcast_shapes(*shapes))
return [broadcast_to(arg, (1,) * (result_rank - len(shp)) + shp)
for arg, shp in zip(args, shapes)]
def _rank_promotion_warning_or_error(fun_name, shapes):
if config.jax_numpy_rank_promotion == "warn":
msg = ("Following NumPy automatic rank promotion for {} on shapes {}. "
"Set the jax_numpy_rank_promotion config option to 'allow' to "
"disable this warning; for more information, see "
"https://jax.readthedocs.io/en/latest/rank_promotion_warning.html.")
warnings.warn(msg.format(fun_name, ' '.join(map(str, shapes))))
elif config.jax_numpy_rank_promotion == "raise":
msg = ("Operands could not be broadcast together for {} on shapes {} "
"and with the config option jax_numpy_rank_promotion='raise'. "
"For more information, see "
"https://jax.readthedocs.io/en/latest/rank_promotion_warning.html.")
raise ValueError(msg.format(fun_name, ' '.join(map(str, shapes))))
def _promote_dtypes(*args):
"""Convenience function to apply Numpy argument dtype promotion."""
# TODO(dougalm,mattjj): This is a performance bottleneck. Consider memoizing.
if len(args) < 2:
return args
else:
to_dtype, weak_type = dtypes._lattice_result_type(*args)
to_dtype = dtypes.canonicalize_dtype(to_dtype)
return [lax._convert_element_type(x, to_dtype, weak_type) for x in args]
def _promote_dtypes_inexact(*args):
"""Convenience function to apply Numpy argument dtype promotion.
Promotes arguments to an inexact type."""
to_dtype, weak_type = dtypes._lattice_result_type(*args)
to_dtype = dtypes.canonicalize_dtype(to_dtype)
to_dtype_inexact = _to_inexact_dtype(to_dtype)
weak_type = (weak_type and to_dtype == to_dtype_inexact)
return [lax._convert_element_type(x, to_dtype_inexact, weak_type) for x in args]
def _to_inexact_dtype(dtype):
"""Promotes a dtype into an inexact dtype, if it is not already one."""
return dtype if issubdtype(dtype, inexact) else promote_types(dtype, float_)
def _complex_elem_type(dtype):
"""Returns the float type of the real/imaginary parts of a complex dtype."""
return np.abs(np.zeros((), dtype)).dtype
def _result_dtype(op, *args):
"""Compute result dtype of applying op to arguments with given dtypes."""
args = [np.ones((0,) * ndim(arg), _dtype(arg)) for arg in args]
return _dtype(op(*args))
def _arraylike(x):
return (isinstance(x, np.ndarray) or isinstance(x, ndarray) or
hasattr(x, '__jax_array__') or isscalar(x))
def _stackable(*args):
return _all(type(arg) in stackables for arg in args)
stackables: Set[Type] = set()
_register_stackable: Callable[[Type], None] = stackables.add
def _check_arraylike(fun_name, *args):
"""Check if all args fit JAX's definition of arraylike."""
assert isinstance(fun_name, str), f"fun_name must be a string. Got {fun_name}"
if _any(not _arraylike(arg) for arg in args):
pos, arg = next((i, arg) for i, arg in enumerate(args)
if not _arraylike(arg))
msg = "{} requires ndarray or scalar arguments, got {} at position {}."
raise TypeError(msg.format(fun_name, type(arg), pos))
def _check_no_float0s(fun_name, *args):
"""Check if none of the args have dtype float0."""
if _any(dtypes.dtype(arg) is dtypes.float0 for arg in args):
raise TypeError(
f"Called {fun_name} with a float0 array. "
"float0s do not support any operations by design because they "
"are not compatible with non-trivial vector spaces. No implicit dtype "
"conversion is done. You can use np.zeros_like(arr, dtype=np.float) "
"to cast a float0 array to a regular zeros array. \n"
"If you didn't expect to get a float0 you might have accidentally "
"taken a gradient with respect to an integer argument.")
def _promote_args(fun_name, *args):
"""Convenience function to apply Numpy argument shape and dtype promotion."""
_check_arraylike(fun_name, *args)
_check_no_float0s(fun_name, *args)
return _promote_shapes(fun_name, *_promote_dtypes(*args))
def _promote_args_inexact(fun_name, *args):
"""Convenience function to apply Numpy argument shape and dtype promotion.
Promotes non-inexact types to an inexact type."""
_check_arraylike(fun_name, *args)
_check_no_float0s(fun_name, *args)
return _promote_shapes(fun_name, *_promote_dtypes_inexact(*args))
def _convert_and_clip_integer(val, dtype):
"""
Convert integer-typed val to specified integer dtype, clipping to dtype
range rather than wrapping.
Args:
val: value to be converted
dtype: dtype of output
Returns:
equivalent of val in new dtype
Examples
--------
Normal integer type conversion will wrap:
>>> val = jnp.uint32(0xFFFFFFFF)
>>> val.astype('int32')
DeviceArray(-1, dtype=int32)
This function clips to the values representable in the new type:
>>> _convert_and_clip_integer(val, 'int32')
DeviceArray(2147483647, dtype=int32)
"""
val = val if isinstance(val, ndarray) else asarray(val)
dtype = dtypes.canonicalize_dtype(dtype)
if not (issubdtype(dtype, integer) and issubdtype(val.dtype, integer)):
raise TypeError("_convert_and_clip_integer only accepts integer dtypes.")
val_dtype = dtypes.canonicalize_dtype(val.dtype)
if val_dtype != val.dtype:
# TODO(jakevdp): this is a weird corner case; need to figure out how to handle it.
# This happens in X32 mode and can either come from a jax value created in another
# context, or a Python integer converted to int64.
pass
min_val = _constant_like(val, _max(iinfo(dtype).min, iinfo(val_dtype).min))
max_val = _constant_like(val, _min(iinfo(dtype).max, iinfo(val_dtype).max))
return clip(val, min_val, max_val).astype(dtype)
def _constant_like(x, const):
return np.array(const, dtype=_dtype(x))
@_wraps(np.load, update_doc=False)
def load(*args, **kwargs):
# The main purpose of this wrapper is to recover bfloat16 data types.
# Note: this will only work for files created via np.save(), not np.savez().
out = np.load(*args, **kwargs)
if isinstance(out, np.ndarray):
# numpy does not recognize bfloat16, so arrays are serialized as void16
if out.dtype == 'V2':
out = out.view(bfloat16)
out = asarray(out)
return out
### implementations of numpy functions in terms of lax
@_wraps(np.fmin)
@jit
def fmin(x1, x2):
return where((x1 < x2) | isnan(x2), x1, x2)
@_wraps(np.fmax)
@jit
def fmax(x1, x2):
return where((x1 > x2) | isnan(x2), x1, x2)
@_wraps(np.issubdtype)
def issubdtype(arg1, arg2):
return dtypes.issubdtype(arg1, arg2)
@_wraps(np.isscalar)
def isscalar(element):
if hasattr(element, '__jax_array__'):
element = element.__jax_array__()
return dtypes.is_python_scalar(element) or np.isscalar(element)
iterable = np.iterable
@_wraps(np.result_type)
def result_type(*args):
return dtypes.result_type(*args)
def _one_to_one_unop(numpy_fn, lax_fn, promote_to_inexact=False, lax_doc=False):
if promote_to_inexact:
fn = lambda x: lax_fn(*_promote_args_inexact(numpy_fn.__name__, x))
else:
fn = lambda x: lax_fn(*_promote_args(numpy_fn.__name__, x))
fn = jit(fn, inline=True)
if lax_doc:
doc = _dedent('\n\n'.join(lax_fn.__doc__.split('\n\n')[1:])).strip()
return _wraps(numpy_fn, lax_description=doc)(fn)
else:
return _wraps(numpy_fn)(fn)
def _one_to_one_binop(numpy_fn, lax_fn, promote_to_inexact=False, lax_doc=False):
if promote_to_inexact:
fn = lambda x1, x2: lax_fn(*_promote_args_inexact(numpy_fn.__name__, x1, x2))
else:
fn = lambda x1, x2: lax_fn(*_promote_args(numpy_fn.__name__, x1, x2))
fn = jit(fn, inline=True)
if lax_doc:
doc = _dedent('\n\n'.join(lax_fn.__doc__.split('\n\n')[1:])).strip()
return _wraps(numpy_fn, lax_description=doc)(fn)
else:
return _wraps(numpy_fn)(fn)
def _maybe_bool_binop(numpy_fn, lax_fn, bool_lax_fn, lax_doc=False):
def fn(x1, x2):
x1, x2 = _promote_args(numpy_fn.__name__, x1, x2)
return lax_fn(x1, x2) if x1.dtype != bool_ else bool_lax_fn(x1, x2)
fn = jit(fn, inline=True)
if lax_doc:
doc = _dedent('\n\n'.join(lax_fn.__doc__.split('\n\n')[1:])).strip()
return _wraps(numpy_fn, lax_description=doc)(fn)
else:
return _wraps(numpy_fn)(fn)
fabs = _one_to_one_unop(np.fabs, lax.abs, True)
bitwise_not = _one_to_one_unop(np.bitwise_not, lax.bitwise_not)
invert = _one_to_one_unop(np.invert, lax.bitwise_not)
negative = _one_to_one_unop(np.negative, lax.neg)
positive = _one_to_one_unop(np.positive, lambda x: x)
floor = _one_to_one_unop(np.floor, lax.floor, True)
ceil = _one_to_one_unop(np.ceil, lax.ceil, True)
exp = _one_to_one_unop(np.exp, lax.exp, True)
log = _one_to_one_unop(np.log, lax.log, True)
expm1 = _one_to_one_unop(np.expm1, lax.expm1, True)
log1p = _one_to_one_unop(np.log1p, lax.log1p, True)
sin = _one_to_one_unop(np.sin, lax.sin, True)
cos = _one_to_one_unop(np.cos, lax.cos, True)
tan = _one_to_one_unop(np.tan, lax.tan, True)
arcsin = _one_to_one_unop(np.arcsin, lax.asin, True)
arccos = _one_to_one_unop(np.arccos, lax.acos, True)
arctan = _one_to_one_unop(np.arctan, lax.atan, True)
sinh = _one_to_one_unop(np.sinh, lax.sinh, True)
cosh = _one_to_one_unop(np.cosh, lax.cosh, True)
arcsinh = _one_to_one_unop(np.arcsinh, lax.asinh, True)
tanh = _one_to_one_unop(np.tanh, lax.tanh, True)
arcsinh = _one_to_one_unop(np.arcsinh, lax.asinh, True)
arctanh = _one_to_one_unop(np.arctanh, lax.atanh, True)
sqrt = _one_to_one_unop(np.sqrt, lax.sqrt, True)
cbrt = _one_to_one_unop(np.cbrt, lax.cbrt, True)
add = _maybe_bool_binop(np.add, lax.add, lax.bitwise_or)
bitwise_and = _one_to_one_binop(np.bitwise_and, lax.bitwise_and)
bitwise_or = _one_to_one_binop(np.bitwise_or, lax.bitwise_or)
bitwise_xor = _one_to_one_binop(np.bitwise_xor, lax.bitwise_xor)
left_shift = _one_to_one_binop(np.left_shift, lax.shift_left)
equal = _one_to_one_binop(np.equal, lax.eq)
multiply = _maybe_bool_binop(np.multiply, lax.mul, lax.bitwise_and)
not_equal = _one_to_one_binop(np.not_equal, lax.ne)
subtract = _one_to_one_binop(np.subtract, lax.sub)
arctan2 = _one_to_one_binop(np.arctan2, lax.atan2, True)
minimum = _one_to_one_binop(np.minimum, lax.min)
maximum = _one_to_one_binop(np.maximum, lax.max)
float_power = _one_to_one_binop(np.float_power, lax.pow, True)
nextafter = _one_to_one_binop(np.nextafter, lax.nextafter, True, True)
@_wraps(np.arccosh)
@jit
def arccosh(x):
# Note: arccosh is multi-valued for complex input, and lax.acosh uses a different
# convention than np.arccosh.
out = lax.acosh(*_promote_args_inexact("arccosh", x))
if issubdtype(out.dtype, np.complexfloating):
out = where(real(out) < 0, lax.neg(out), out)
return out
def _comparison_op(numpy_fn, lax_fn):
# TODO(https://github.com/google/jax/issues/6713): decorate this function with
# jit, after fixing a surprising interaction with remat(..., concrete=True).
def fn(x1, x2):
x1, x2 = _promote_args(numpy_fn.__name__, x1, x2)
# Comparison on complex types are defined as a lexicographic ordering on
# the (real, imag) pair.
if issubdtype(_dtype(x1), complexfloating):
rx = lax.real(x1)
ry = lax.real(x2)
return lax.select(lax.eq(rx, ry), lax_fn(lax.imag(x1), lax.imag(x2)),
lax_fn(rx, ry))
return lax_fn(x1, x2)
return _wraps(numpy_fn)(fn)
greater_equal = _comparison_op(np.greater_equal, lax.ge)
greater = _comparison_op(np.greater, lax.gt)
less_equal = _comparison_op(np.less_equal, lax.le)
less = _comparison_op(np.less, lax.lt)
def _logical_op(np_op, bitwise_op):
@_wraps(np_op, update_doc=False)
@partial(jit, inline=True)
def op(*args):
zero = lambda x: lax.full_like(x, shape=(), fill_value=0)
args = (x if issubdtype(_dtype(x), bool_) else lax.ne(x, zero(x))
for x in args)
return bitwise_op(*_promote_args(np_op.__name__, *args))
return op
logical_and = _logical_op(np.logical_and, lax.bitwise_and)
logical_not = _logical_op(np.logical_not, lax.bitwise_not)
logical_or = _logical_op(np.logical_or, lax.bitwise_or)
logical_xor = _logical_op(np.logical_xor, lax.bitwise_xor)
@_wraps(np.right_shift)
@partial(jit, inline=True)
def right_shift(x1, x2):
x1, x2 = _promote_args(np.right_shift.__name__, x1, x2)
lax_fn = lax.shift_right_logical if \
np.issubdtype(x1.dtype, np.unsignedinteger) else lax.shift_right_arithmetic
return lax_fn(x1, x2)
@_wraps(np.absolute)
@partial(jit, inline=True)
def absolute(x):
_check_arraylike('absolute', x)
dt = _dtype(x)
return x if dt == bool_ or issubdtype(dt, unsignedinteger) else lax.abs(x)
abs = _wraps(np.abs)(absolute)
@_wraps(np.rint)
@jit
def rint(x):
_check_arraylike('rint', x)
dtype = _dtype(x)
if issubdtype(dtype, integer):
return lax.convert_element_type(x, float_)
if issubdtype(dtype, complexfloating):
return lax.complex(rint(lax.real(x)), rint(lax.imag(x)))
return lax.round(x, lax.RoundingMethod.TO_NEAREST_EVEN)
@_wraps(np.sign)
@jit
def sign(x):
_check_arraylike('sign', x)
dtype = _dtype(x)
if issubdtype(dtype, complexfloating):
re = lax.real(x)
return lax.complex(
lax.sign(where(re != 0, re, lax.imag(x))), _constant_like(re, 0))
return lax.sign(x)
@_wraps(np.copysign)
@jit
def copysign(x1, x2):
x1, x2 = _promote_args_inexact("copysign", x1, x2)
if issubdtype(_dtype(x1), complexfloating):
raise TypeError("copysign does not support complex-valued inputs")
return where(signbit(x2), -lax.abs(x1), lax.abs(x1))
@_wraps(np.true_divide)
@partial(jit, inline=True)
def true_divide(x1, x2):
x1, x2 = _promote_args_inexact("true_divide", x1, x2)
return lax.div(x1, x2)
divide = true_divide
@_wraps(np.floor_divide)
@jit
def floor_divide(x1, x2):
x1, x2 = _promote_args("floor_divide", x1, x2)
dtype = _dtype(x1)
if issubdtype(dtype, integer):
quotient = lax.div(x1, x2)
select = logical_and(lax.sign(x1) != lax.sign(x2), lax.rem(x1, x2) != 0)
# TODO(mattjj): investigate why subtracting a scalar was causing promotion
return where(select, quotient - np.array(1, _dtype(quotient)), quotient)
elif issubdtype(dtype, complexfloating):
x1r = lax.real(x1)
x1i = lax.imag(x1)
x2r = lax.real(x2)
x2i = lax.imag(x2)
which = lax.ge(lax.abs(x2r), lax.abs(x2i))
rat1 = where(which, lax._const(x2i, 1), lax.div(x2r, x2i))
rat2 = where(which, lax.div(x2i, x2r), lax._const(x2i, 1))
out = lax.floor(lax.div(lax.add(lax.mul(x1r, rat1), lax.mul(x1i, rat2)),
lax.add(lax.mul(x2r, rat1), lax.mul(x2i, rat2))))
return lax.convert_element_type(out, dtype)
else:
return _float_divmod(x1, x2)[0]
@_wraps(np.divmod)
@jit
def divmod(x1, x2):
x1, x2 = _promote_args("divmod", x1, x2)
if issubdtype(_dtype(x1), integer):
return floor_divide(x1, x2), remainder(x1, x2)
else:
return _float_divmod(x1, x2)
def _float_divmod(x1, x2):
# see float_divmod in floatobject.c of CPython
mod = lax.rem(x1, x2)
div = lax.div(lax.sub(x1, mod), x2)
ind = lax.bitwise_and(mod != 0, lax.sign(x2) != lax.sign(mod))
mod = lax.select(ind, mod + x2, mod)
div = lax.select(ind, div - _constant_like(div, 1), div)
return lax.round(div), mod
@partial(jit, inline=True)
def _power(x1, x2):
x1, x2 = _promote_args("power", x1, x2)
dtype = _dtype(x1)
if not issubdtype(dtype, integer):
return lax.pow(x1, x2)
# Integer power => use binary exponentiation.
# TODO(phawkins): add integer pow support to XLA.
bits = 6 # Anything more would overflow for any x1 > 1
zero = _constant_like(x2, 0)
one = _constant_like(x2, 1)
# Initialize acc carefully such that pow(0, x2) is zero for x2 != 0
acc = where(lax.bitwise_and(lax.eq(x1, zero), lax.ne(x2, zero)), zero, one)
for _ in range(bits):
acc = where(lax.bitwise_and(x2, one), lax.mul(acc, x1), acc)
x1 = lax.mul(x1, x1)
x2 = lax.shift_right_logical(x2, one)
return acc
@_wraps(np.power)
def power(x1, x2):
# Special case for concrete integer scalars: use binary exponentiation.
# Using lax.pow may be imprecise for floating-point values; the goal of this
# code path is to make sure we end up with a precise output for the common
# pattern ``x ** 2`` or similar.
if isinstance(core.get_aval(x2), ConcreteArray):
try:
x2 = operator.index(x2)
except TypeError:
pass
else:
return lax.integer_pow(x1, x2)
return _power(x1, x2)
@custom_jvp
@_wraps(np.logaddexp)
@jit
def logaddexp(x1, x2):
x1, x2 = _promote_args_inexact("logaddexp", x1, x2)
amax = lax.max(x1, x2)
if issubdtype(x1.dtype, np.floating):
delta = lax.sub(x1, x2)
return lax.select(isnan(delta),
lax.add(x1, x2), # NaNs or infinities of the same sign.
lax.add(amax, lax.log1p(lax.exp(lax.neg(lax.abs(delta))))))
else:
delta = lax.sub(lax.add(x1, x2), lax.mul(amax, _constant_like(amax, 2)))
out = lax.add(amax, lax.log1p(lax.exp(delta)))
return lax.complex(lax.real(out), _wrap_between(lax.imag(out), np.pi))
def _wrap_between(x, _a):
"""Wraps `x` between `[-a, a]`."""
a = _constant_like(x, _a)
two_a = _constant_like(x, 2 * _a)
zero = _constant_like(x, 0)
rem = lax.rem(lax.add(x, a), two_a)
rem = lax.select(lax.lt(rem, zero), lax.add(rem, two_a), rem)
return lax.sub(rem, a)
@logaddexp.defjvp
def _logaddexp_jvp(primals, tangents):
x1, x2 = primals
t1, t2 = tangents
x1, x2, t1, t2 = _promote_args_inexact("logaddexp_jvp", x1, x2, t1, t2)
primal_out = logaddexp(x1, x2)
tangent_out = lax.add(lax.mul(t1, exp(lax.sub(_replace_inf(x1), _replace_inf(primal_out)))),
lax.mul(t2, exp(lax.sub(_replace_inf(x2), _replace_inf(primal_out)))))
return primal_out, tangent_out
def _replace_inf(x):
return lax.select(isposinf(real(x)), zeros_like(x), x)
@custom_jvp
@_wraps(np.logaddexp2)
@jit
def logaddexp2(x1, x2):
x1, x2 = _promote_args_inexact("logaddexp2", x1, x2)
amax = lax.max(x1, x2)
if issubdtype(x1.dtype, np.floating):
delta = lax.sub(x1, x2)
return lax.select(isnan(delta),
lax.add(x1, x2), # NaNs or infinities of the same sign.
lax.add(amax, lax.div(lax.log1p(exp2(lax.neg(lax.abs(delta)))),
_constant_like(x1, np.log(2)))))
else:
delta = lax.sub(lax.add(x1, x2), lax.mul(amax, _constant_like(amax, 2)))
out = lax.add(amax, lax.div(lax.log1p(exp2(delta)), _constant_like(x1, np.log(2))))
return lax.complex(lax.real(out), _wrap_between(lax.imag(out), np.pi / np.log(2)))
@logaddexp2.defjvp
def _logaddexp2_jvp(primals, tangents):
x1, x2 = primals
t1, t2 = tangents
x1, x2, t1, t2 = _promote_args_inexact("logaddexp2_jvp", x1, x2, t1, t2)
primal_out = logaddexp2(x1, x2)
tangent_out = lax.add(lax.mul(t1, exp2(lax.sub(_replace_inf(x1), _replace_inf(primal_out)))),
lax.mul(t2, exp2(lax.sub(_replace_inf(x2), _replace_inf(primal_out)))))
return primal_out, tangent_out
@_wraps(np.log2)
@partial(jit, inline=True)
def log2(x):
x, = _promote_args_inexact("log2", x)
return lax.div(lax.log(x), lax.log(_constant_like(x, 2)))
@_wraps(np.log10)
@partial(jit, inline=True)
def log10(x):
x, = _promote_args_inexact("log10", x)
return lax.div(lax.log(x), lax.log(_constant_like(x, 10)))
@_wraps(np.exp2)
@partial(jit, inline=True)
def exp2(x):
x, = _promote_args_inexact("exp2", x)
return lax.exp(lax.mul(lax.log(_constant_like(x, 2)), x))
@_wraps(np.signbit)
@jit
def signbit(x):
x, = _promote_args("signbit", x)
dtype = _dtype(x)
if issubdtype(dtype, integer):
return lax.lt(x, _constant_like(x, 0))
elif issubdtype(dtype, bool_):
return full_like(x, False, dtype=bool_)
elif not issubdtype(dtype, floating):
raise ValueError(
"jax.numpy.signbit is not well defined for %s" % dtype)
# TPU supports BF16 but not S16 types, so as a workaround, convert BF16 to
# F32.
if dtype == bfloat16:
dtype = float32
x = lax.convert_element_type(x, float32)
info = finfo(dtype)
if info.bits not in _INT_DTYPES:
raise NotImplementedError(
"jax.numpy.signbit only supports 16, 32, and 64-bit types.")
int_type = _INT_DTYPES[info.bits]
x = lax.bitcast_convert_type(x, int_type)
return lax.convert_element_type(x >> (info.nexp + info.nmant), np.bool_)
@_wraps(np.trapz)
@partial(jit, static_argnames=('axis',))
def trapz(y, x=None, dx=1.0, axis: int = -1):
_check_arraylike('trapz', y)
y = moveaxis(y, axis, -1)
if x is not None:
if ndim(x) == 1:
dx = diff(x)
else:
dx = moveaxis(diff(x, axis=axis), axis, -1)
return 0.5 * (dx * (y[..., 1:] + y[..., :-1])).sum(-1)
@_wraps(np.trunc)
@jit
def trunc(x):
_check_arraylike('trunc', x)
return where(lax.lt(x, lax._const(x, 0)), ceil(x), floor(x))
@partial(jit, static_argnums=(2, 3, 4))
def _conv(x, y, mode, op, precision):
if ndim(x) != 1 or ndim(y) != 1:
raise ValueError(f"{op}() only support 1-dimensional inputs.")
x, y = _promote_dtypes_inexact(x, y)
if len(x) == 0 or len(y) == 0:
raise ValueError(f"{op}: inputs cannot be empty, got shapes {x.shape} and {y.shape}.")
out_order = slice(None)
if op == 'correlate':
y = conj(y)
if len(x) < len(y):
x, y = y, x
out_order = slice(None, None, -1)
elif op == 'convolve':
if len(x) < len(y):
x, y = y, x
y = flip(y)
if mode == 'valid':
padding = [(0, 0)]
elif mode == 'same':
padding = [(y.shape[0] // 2, y.shape[0] - y.shape[0] // 2 - 1)]
elif mode == 'full':
padding = [(y.shape[0] - 1, y.shape[0] - 1)]
else:
raise ValueError("mode must be one of ['full', 'same', 'valid']")
result = lax.conv_general_dilated(x[None, None, :], y[None, None, :], (1,),
padding, precision=precision)
return result[0, 0, out_order]
@_wraps(np.convolve, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('mode', 'precision'))
def convolve(a, v, mode='full', *, precision=None):
_check_arraylike("convolve", a, v)
return _conv(a, v, mode, 'convolve', precision)
@_wraps(np.correlate, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('mode', 'precision'))
def correlate(a, v, mode='valid', *, precision=None):
_check_arraylike("correlate", a, v)
return _conv(a, v, mode, 'correlate', precision)
def _normalize_float(x):
info = finfo(_dtype(x))
cond = lax.abs(x) < info.tiny
x1 = where(cond, x * lax._const(x, 1 << info.nmant), x)
x2 = where(cond, lax._const(np.int32, -info.nmant), lax._const(np.int32, 0))
int_type = _INT_DTYPES[info.bits]
return lax.bitcast_convert_type(x1, int_type), x2
@_wraps(np.ldexp)
@jit
def ldexp(x1, x2):
_check_arraylike("ldexp", x1, x2)
dtype = dtypes.canonicalize_dtype(_result_dtype(np.ldexp, x1, x2))
x1, x2 = _promote_shapes("ldexp", x1, x2)
x1 = lax.convert_element_type(x1, dtype)
info = finfo(dtype)
mask = (1 << info.nexp) - 1
bias = ((1 << info.nexp) - 1) >> 1
int_type = _INT_DTYPES[info.bits]
x, e = _normalize_float(x1)
x2 += e + ((x >> info.nmant) & mask) - bias
# find underflow/overflow before denormalization
underflow_cond = x2 < -(bias + info.nmant)
overflow_cond = x2 > bias
m = ones_like(x, dtype=dtype)
# denormals
cond = x2 < -bias + 1
x2 = where(cond, x2 + info.nmant, x2)
m = where(cond, m / (1 << info.nmant), m)
x2 = lax.convert_element_type(x2, np.int32)
x &= ~(mask << info.nmant)
x |= ((lax.convert_element_type(x2, int_type) + bias) << info.nmant)
x = lax.convert_element_type(m, dtype) * lax.bitcast_convert_type(x, dtype)
# underflow
x = where(underflow_cond, zeros_like(x, dtype=dtype), x)
# overflow
x = where(overflow_cond, lax.sign(x1) * full_like(x, np.inf), x)
# ldexp(x1, x2) = x1 for x1 = inf, -inf, nan, 0
return where(isinf(x1) | isnan(x1) | (x1 == 0), x1, x)
@_wraps(np.frexp)
@jit
def frexp(x):
_check_arraylike("frexp", x)
x = asarray(x)
if issubdtype(x.dtype, complexfloating):
raise TypeError("frexp does not support complex-valued inputs")
elif not issubdtype(x.dtype, floating):
x = lax.convert_element_type(x, float_)
dtype = _dtype(x)
info = finfo(dtype)
mask = (1 << info.nexp) - 1
bias = ((1 << info.nexp) - 1) >> 1
x1, x2 = _normalize_float(x)
x2 += ((x1 >> info.nmant) & mask) - bias + 1
x1 &= ~(mask << info.nmant)
x1 |= (bias - 1) << info.nmant
x1 = lax.bitcast_convert_type(x1, dtype)
cond = isinf(x) | isnan(x) | (x == 0)
x2 = where(cond, zeros_like(x2), x2)
return where(cond, x, x1), lax.convert_element_type(x2, int32)
@_wraps(np.remainder)
@jit
def remainder(x1, x2):
x1, x2 = _promote_args("remainder", x1, x2)
zero = _constant_like(x1, 0)
trunc_mod = lax.rem(x1, x2)
trunc_mod_not_zero = lax.ne(trunc_mod, zero)
do_plus = lax.bitwise_and(
lax.ne(lax.lt(trunc_mod, zero), lax.lt(x2, zero)), trunc_mod_not_zero)
return lax.select(do_plus, lax.add(trunc_mod, x2), trunc_mod)
mod = _wraps(np.mod)(remainder)
@_wraps(np.fmod)
@jit
def fmod(x1, x2):
_check_arraylike("fmod", x1, x2)
if issubdtype(result_type(x1, x2), integer):
x2 = where(x2 == 0, 1, x2)
return lax.rem(*_promote_args("fmod", x1, x2))
@_wraps(np.square)
@partial(jit, inline=True)
def square(x):
_check_arraylike("square", x)
return lax.integer_pow(x, 2)
@_wraps(np.deg2rad)
@partial(jit, inline=True)
def deg2rad(x):
x, = _promote_args_inexact("deg2rad", x)
return lax.mul(x, lax._const(x, pi / 180))
@_wraps(np.rad2deg)
@partial(jit, inline=True)
def rad2deg(x):
x, = _promote_args_inexact("rad2deg", x)
return lax.mul(x, lax._const(x, 180 / pi))
degrees = rad2deg
radians = deg2rad
@_wraps(np.histogram_bin_edges)
def histogram_bin_edges(a, bins=10, range=None, weights=None):
if isinstance(bins, str):
raise NotImplementedError("string values for `bins` not implemented.")
_check_arraylike("histogram_bin_edges", a, bins)
a = ravel(a)
b = asarray(bins)
if b.ndim == 1:
return b
if range is None:
range = [a.min(), a.max()]
assert len(range) == 2
range = asarray(range)
range = (where(ptp(range) == 0, range[0] - 0.5, range[0]),
where(ptp(range) == 0, range[1] + 0.5, range[1]))
dtype = _dtype(a)
if issubdtype(dtype, integer):
dtype = promote_types(dtype, float32)
return linspace(range[0], range[1], bins + 1, dtype=dtype)
@_wraps(np.histogram)
def histogram(a, bins=10, range=None, weights=None, density=None):
_check_arraylike("histogram", a, bins)
if weights is not None and a.shape != weights.shape:
raise ValueError("weights should have the same shape as a.")
a = ravel(a)
if weights is not None:
weights = ravel(weights)
else:
weights = ones_like(a)
bin_edges = histogram_bin_edges(a, bins, range, weights)
bin_idx = searchsorted(bin_edges, a, side='right')
bin_idx = where(a == bin_edges[-1], len(bin_edges) - 1, bin_idx)
counts = bincount(bin_idx, weights, length=len(bin_edges))[1:]
if density:
bin_widths = diff(bin_edges)
counts = counts / bin_widths / counts.sum()
return counts, bin_edges
@_wraps(np.histogram2d)
def histogram2d(x, y, bins=10, range=None, weights=None, density=None):
_check_arraylike("histogram2d", x, y)
try:
N = len(bins)
except TypeError:
N = 1
if N != 1 and N != 2:
x_edges = y_edges = asarray(bins)
bins = [x_edges, y_edges]
sample = transpose(asarray([x, y]))
hist, edges = histogramdd(sample, bins, range, weights, density)
return hist, edges[0], edges[1]
@_wraps(np.histogramdd)
def histogramdd(sample, bins=10, range=None, weights=None, density=None):
_check_arraylike("histogramdd", sample)
N, D = shape(sample)
if weights is not None and weights.shape != (N,):
raise ValueError("should have one weight for each sample.")
if range is not None and (
len(range) != D or _any(r is not None and len(r) != 2 for r in range)):
raise ValueError(f"For sample.shape={(N, D)}, range must be a sequence "
f"of {D} pairs or Nones; got range={range}")
try:
num_bins = len(bins)
if num_bins != D:
raise ValueError("should be a bin for each dimension.")
except TypeError:
# when bin_size is integer, the same bin is used for each dimension
bins = D * [bins]
bin_idx_by_dim = D*[None]
nbins = np.empty(D, int)
bin_edges_by_dim = D*[None]
dedges = D*[None]
for i in builtins.range(D):
range_i = None if range is None else range[i]
bin_edges = histogram_bin_edges(sample[:, i], bins[i], range_i, weights)
bin_idx = searchsorted(bin_edges, sample[:, i], side='right')
bin_idx = where(sample[:, i] == bin_edges[-1], bin_idx - 1, bin_idx)
bin_idx_by_dim[i] = bin_idx
nbins[i] = len(bin_edges) + 1
bin_edges_by_dim[i] = bin_edges
dedges[i] = diff(bin_edges_by_dim[i])
xy = ravel_multi_index(bin_idx_by_dim, nbins, mode='clip')
hist = bincount(xy, weights, length=nbins.prod())
hist = reshape(hist, nbins)
core = D*(slice(1, -1),)
hist = hist[core]
if density:
hist /= hist.sum()
for norm in ix_(*dedges):
hist /= norm
return hist, bin_edges_by_dim
@_wraps(np.heaviside)
@jit
def heaviside(x1, x2):
_check_arraylike("heaviside", x1, x2)
x1, x2 = _promote_dtypes_inexact(x1, x2)
zero = lax._const(x1, 0)
return where(lax.lt(x1, zero), zero,
where(lax.gt(x1, zero), lax._const(x1, 1), x2))
@_wraps(np.hypot)
@jit
def hypot(x1, x2):
_check_arraylike("hypot", x1, x2)
x1, x2 = _promote_dtypes_inexact(x1, x2)
x1 = lax.abs(x1)
x2 = lax.abs(x2)
x1, x2 = maximum(x1, x2), minimum(x1, x2)
return lax.select(x1 == 0, x1, x1 * lax.sqrt(1 + lax.square(lax.div(x2, lax.select(x1 == 0, ones_like(x1), x1)))))
@_wraps(np.reciprocal)
@partial(jit, inline=True)
def reciprocal(x):
_check_arraylike("reciprocal", x)
x, = _promote_dtypes_inexact(x)
return lax.integer_pow(x, -1)
@_wraps(np.sinc, update_doc=False)
@jit
def sinc(x):
_check_arraylike("sinc", x)
x, = _promote_dtypes_inexact(x)
eq_zero = lax.eq(x, lax._const(x, 0))
pi_x = lax.mul(lax._const(x, pi), x)
safe_pi_x = where(eq_zero, lax._const(x, 1), pi_x)
return where(eq_zero, _sinc_maclaurin(0, pi_x),
lax.div(lax.sin(safe_pi_x), safe_pi_x))
@partial(custom_jvp, nondiff_argnums=(0,))
def _sinc_maclaurin(k, x):
# compute the kth derivative of x -> sin(x)/x evaluated at zero (since we
# compute the monomial term in the jvp rule)
if k % 2:
return lax.full_like(x, 0)
else:
return lax.full_like(x, (-1) ** (k // 2) / (k + 1))
@_sinc_maclaurin.defjvp
def _sinc_maclaurin_jvp(k, primals, tangents):
(x,), (t,) = primals, tangents
return _sinc_maclaurin(k, x), _sinc_maclaurin(k + 1, x) * t
_ARRAY_VIEW_DOC = """
The JAX version of this function may in some cases return a copy rather than a
view of the input.
"""
@_wraps(np.transpose, lax_description=_ARRAY_VIEW_DOC)
def transpose(a, axes=None):
_check_arraylike("transpose", a)
axes = np.arange(ndim(a))[::-1] if axes is None else axes
return lax.transpose(a, axes)
@_wraps(np.rot90, lax_description=_ARRAY_VIEW_DOC)
@partial(jit, static_argnames=('k', 'axes'))
def rot90(m, k=1, axes=(0, 1)):
_check_arraylike("rot90", m)
ax1, ax2 = axes
ax1 = _canonicalize_axis(ax1, ndim(m))
ax2 = _canonicalize_axis(ax2, ndim(m))
if ax1 == ax2:
raise ValueError("Axes must be different") # same as numpy error
k = k % 4
if k == 0:
return m
elif k == 2:
return flip(flip(m, ax1), ax2)
else:
perm = list(range(m.ndim))
perm[ax1], perm[ax2] = perm[ax2], perm[ax1]
if k == 1:
return transpose(flip(m, ax2), perm)
else:
return flip(transpose(m, perm), ax2)
@_wraps(np.flip, lax_description=_ARRAY_VIEW_DOC)
def flip(m, axis: Optional[Union[int, Tuple[int, ...]]] = None):
return _flip(m, _ensure_optional_axes(axis))
@partial(jit, static_argnames=('axis',))
def _flip(m, axis: Optional[Union[int, Tuple[int, ...]]] = None):
_check_arraylike("flip", m)
if axis is None:
return lax.rev(m, list(range(len(shape(m)))))
axis = _ensure_index_tuple(axis)
return lax.rev(m, [_canonicalize_axis(ax, ndim(m)) for ax in axis])
@_wraps(np.fliplr, lax_description=_ARRAY_VIEW_DOC)
def fliplr(m):
return _flip(m, 1)
@_wraps(np.flipud, lax_description=_ARRAY_VIEW_DOC)
def flipud(m):
return _flip(m, 0)
@_wraps(np.conjugate)
@partial(jit, inline=True)
def conjugate(x):
_check_arraylike("conjugate", x)
return lax.conj(x) if iscomplexobj(x) else x
conj = conjugate
@_wraps(np.imag)
@partial(jit, inline=True)
def imag(val):
_check_arraylike("imag", val)
return lax.imag(val) if iscomplexobj(val) else zeros_like(val)
@_wraps(np.real)
@partial(jit, inline=True)
def real(val):
_check_arraylike("real", val)
return lax.real(val) if iscomplexobj(val) else val
@_wraps(np.iscomplex)
@jit
def iscomplex(x):
i = imag(x)
return lax.ne(i, lax._const(i, 0))
@_wraps(np.isreal)
@jit
def isreal(x):
i = imag(x)
return lax.eq(i, lax._const(i, 0))
@_wraps(np.angle)
@jit
def angle(z):
re = real(z)
im = imag(z)
dtype = _dtype(re)
if not issubdtype(dtype, inexact) or (
issubdtype(_dtype(z), floating) and ndim(z) == 0):
dtype = dtypes.canonicalize_dtype(float_)
re = lax.convert_element_type(re, dtype)
im = lax.convert_element_type(im, dtype)
return lax.atan2(im, re)
@_wraps(np.diff)
@partial(jit, static_argnames=('n', 'axis'))
def diff(a, n=1, axis: int = -1, prepend=None, append=None):
_check_arraylike("diff", a)
n = core.concrete_or_error(operator.index, n, "'n' argument of jnp.diff")
axis = core.concrete_or_error(operator.index, axis, "'axis' argument of jnp.diff")
if n == 0:
return a
if n < 0:
raise ValueError(f"order must be non-negative but got {n}")
if ndim(a) == 0:
raise ValueError(f"diff requires input that is at least one dimensional; got {a}")
nd = a.ndim
axis = _canonicalize_axis(axis, nd)
combined = []
if prepend is not None:
_check_arraylike("diff", prepend)
if isscalar(prepend):
shape = list(a.shape)
shape[axis] = 1
prepend = broadcast_to(prepend, tuple(shape))
combined.append(prepend)
combined.append(a)
if append is not None:
_check_arraylike("diff", append)
if isscalar(append):
shape = list(a.shape)
shape[axis] = 1
append = broadcast_to(append, tuple(shape))
combined.append(append)
if len(combined) > 1:
a = concatenate(combined, axis)
slice1 = [slice(None)] * nd
slice2 = [slice(None)] * nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1_tuple = tuple(slice1)
slice2_tuple = tuple(slice2)
op = not_equal if a.dtype == np.bool_ else subtract
for _ in range(n):
a = op(a[slice1_tuple], a[slice2_tuple])
return a
_EDIFF1D_DOC = """\
Unlike NumPy's implementation of ediff1d, :py:func:`jax.numpy.ediff1d` will not
issue an error if casting ``to_end`` or ``to_begin`` to the type of ``ary``
loses precision.
"""
@_wraps(np.ediff1d, lax_description=_EDIFF1D_DOC)
@jit
def ediff1d(ary, to_end=None, to_begin=None):
_check_arraylike("ediff1d", ary)
ary = ravel(ary)
result = lax.sub(ary[1:], ary[:-1])
if to_begin is not None:
_check_arraylike("ediff1d", to_begin)
result = concatenate((ravel(asarray(to_begin, dtype=ary.dtype)), result))
if to_end is not None:
_check_arraylike("ediff1d", to_end)
result = concatenate((result, ravel(asarray(to_end, dtype=ary.dtype))))
return result
@_wraps(np.gradient, skip_params=['edge_order'])
@partial(jit, static_argnames=('axis', 'edge_order'))
def gradient(f, *varargs, axis: Optional[Union[int, Tuple[int, ...]]] = None,
edge_order=None):
if edge_order is not None:
raise NotImplementedError("The 'edge_order' argument to jnp.gradient is not supported.")
def gradient_along_axis(a, h, axis):
sliced = partial(lax.slice_in_dim, a, axis=axis)
a_grad = concatenate((
(sliced(1, 2) - sliced(0, 1)), # upper edge
(sliced(2, None) - sliced(None, -2)) * 0.5, # inner
(sliced(-1, None) - sliced(-2, -1)), # lower edge
), axis)
return a_grad / h
a = f
axis_tuple: Tuple[int, ...]
if axis is None:
axis_tuple = tuple(range(a.ndim))
else:
if isinstance(axis, int):
axis = (axis,)
elif not isinstance(axis, tuple) and not isinstance(axis, list):
raise ValueError("Give `axis` either as int or iterable")
elif len(axis) == 0:
return []
axis_tuple = tuple(_canonicalize_axis(i, a.ndim) for i in axis)
if _min([s for i, s in enumerate(a.shape) if i in axis_tuple]) < 2:
raise ValueError("Shape of array too small to calculate "
"a numerical gradient, "
"at least 2 elements are required.")
len_axes = len(axis_tuple)
n = len(varargs)
if n == 0 or varargs is None:
# no spacing
dx = [1.0] * len_axes
elif n == 1:
# single value for all axes
dx = list(varargs) * len_axes
elif n == len_axes:
dx = list(varargs)
else:
TypeError("Invalid number of spacing arguments %d" % n)
if ndim(dx[0]) != 0:
raise NotImplementedError("Non-constant spacing not implemented")
# TODO: use jax.lax loop tools if possible
a_grad = [gradient_along_axis(a, h, ax) for ax, h in zip(axis_tuple, dx)]
if len(axis_tuple) == 1:
a_grad = a_grad[0]
return a_grad
@_wraps(np.isrealobj)
def isrealobj(x):
return not iscomplexobj(x)
_POLYFIT_DOC = """\
Unlike NumPy's implementation of polyfit, :py:func:`jax.numpy.polyfit` will not warn on rank reduction, which indicates an ill conditioned matrix
Also, it works best on rcond <= 10e-3 values.
"""
@_wraps(np.polyfit, lax_description=_POLYFIT_DOC)
@partial(jit, static_argnames=('deg', 'rcond', 'full', 'cov'))
def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
_check_arraylike("polyfit", x, y)
deg = core.concrete_or_error(int, deg, "deg must be int")
order = deg + 1
# check arguments
if deg < 0:
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2:
raise TypeError("expected 1D or 2D array for y")
if x.shape[0] != y.shape[0]:
raise TypeError("expected x and y to have same length")
# set rcond
if rcond is None:
rcond = len(x)*finfo(x.dtype).eps
rcond = core.concrete_or_error(float, rcond, "rcond must be float")
# set up least squares equation for powers of x
lhs = vander(x, order)
rhs = y
# apply weighting
if w is not None:
_check_arraylike("polyfit", w)
w, = _promote_dtypes_inexact(w)
if w.ndim != 1:
raise TypeError("expected a 1-d array for weights")
if w.shape[0] != y.shape[0]:
raise TypeError("expected w and y to have the same length")
lhs *= w[:, newaxis]
if rhs.ndim == 2:
rhs *= w[:, newaxis]
else:
rhs *= w
# scale lhs to improve condition number and solve
scale = sqrt((lhs*lhs).sum(axis=0))
lhs /= scale[newaxis,:]
from jax._src.numpy import linalg
c, resids, rank, s = linalg.lstsq(lhs, rhs, rcond)
c = (c.T/scale).T # broadcast scale coefficients
if full:
return c, resids, rank, s, rcond
elif cov:
Vbase = linalg.inv(dot(lhs.T, lhs))
Vbase /= outer(scale, scale)
if cov == "unscaled":
fac = 1
else:
if len(x) <= order:
raise ValueError("the number of data points must exceed order "
"to scale the covariance matrix")
fac = resids / (len(x) - order)
fac = fac[0] #making np.array() of shape (1,) to int
if y.ndim == 1:
return c, Vbase * fac
else:
return c, Vbase[:,:, newaxis] * fac
else:
return c
@_wraps(np.reshape, lax_description=_ARRAY_VIEW_DOC)
def reshape(a, newshape, order="C"):
_stackable(a) or _check_arraylike("reshape", a)
try:
return a.reshape(newshape, order=order) # forward to method for ndarrays
except AttributeError:
return _reshape(a, newshape, order=order)
def _compute_newshape(a, newshape):
"""Fixes a -1 value in newshape, if present."""
# other errors, like having more than one -1, are caught downstream, in
# reshape_shape_rule.
try: iter(newshape)
except: iterable = False
else: iterable = True
newshape = core.canonicalize_shape(newshape if iterable else [newshape])
return tuple(- core.divide_shape_sizes(np.shape(a), newshape)
if core.symbolic_equal_dim(d, -1) else d
for d in newshape)
def _reshape(a, *args, order="C"):
newshape = _compute_newshape(a, args[0] if len(args) == 1 else args)
if order == "C":
return lax.reshape(a, newshape, None)
elif order == "F":
dims = np.arange(ndim(a))[::-1]
return lax.reshape(a, newshape[::-1], dims).T
elif order == "A":
raise NotImplementedError("np.reshape order=A is not implemented.")
else:
raise ValueError("Unexpected value for 'order' argument: {}.".format(order))
def _transpose(a, *args):
if not args:
axis = None
elif len(args) == 1:
axis = args[0] if args[0] is None else _ensure_index_tuple(args[0])
else:
axis = _ensure_index_tuple(args)
return transpose(a, axis)
@_wraps(np.ravel, lax_description=_ARRAY_VIEW_DOC)
@partial(jit, static_argnames=('order',), inline=True)
def ravel(a, order="C"):
_stackable(a) or _check_arraylike("ravel", a)
if order == "K":
raise NotImplementedError("Ravel not implemented for order='K'.")
return reshape(a, (size(a),), order)
@_wraps(np.ravel_multi_index)
def ravel_multi_index(multi_index, dims, mode='raise', order='C'):
assert len(multi_index) == len(dims), f"len(multi_index)={len(multi_index)} != len(dims)={len(dims)}"
dims = tuple(core.concrete_or_error(int, d, "in `dims` argument of ravel_multi_index().") for d in dims)
_check_arraylike("ravel_multi_index", *multi_index)
for index in multi_index:
if mode == 'raise':
core.concrete_or_error(array, index,
"The error occurred because ravel_multi_index was jit-compiled"
" with mode='raise'. Use mode='wrap' or mode='clip' instead.")
if not issubdtype(_dtype(index), integer):
raise TypeError("only int indices permitted")
if mode == "raise":
if _any(any((i < 0) | (i >= d)) for i, d in zip(multi_index, dims)):
raise ValueError("invalid entry in coordinates array")
elif mode == "clip":
multi_index = [clip(i, 0, d - 1) for i, d in zip(multi_index, dims)]
elif mode == "wrap":
multi_index = [i % d for i, d in zip(multi_index, dims)]
else:
raise ValueError(f"invalid mode={mode!r}. Expected 'raise', 'wrap', or 'clip'")
if order == "F":
strides = np.cumprod((1,) + dims[:-1])
elif order == "C":
strides = np.cumprod((1,) + dims[1:][::-1])[::-1]
else:
raise ValueError(f"invalid order={order!r}. Expected 'C' or 'F'")
result = array(0, dtype=dtypes.canonicalize_dtype(int_))
for i, s in zip(multi_index, strides):
result = result + i * s
return result
_UNRAVEL_INDEX_DOC = """\
Unlike numpy's implementation of unravel_index, negative indices are accepted
and out-of-bounds indices are clipped.
"""
@_wraps(np.unravel_index, lax_description=_UNRAVEL_INDEX_DOC)
def unravel_index(indices, shape):
_check_arraylike("unravel_index", indices)
sizes = append(array(shape), 1)
cumulative_sizes = cumprod(sizes[::-1])[::-1]
total_size = cumulative_sizes[0]
# Clip so raveling and unraveling an oob index will not change the behavior
clipped_indices = clip(indices, -total_size, total_size - 1)
# Add enough trailing dims to avoid conflict with clipped_indices
cumulative_sizes = expand_dims(cumulative_sizes, range(1, 1 + _ndim(indices)))
clipped_indices = expand_dims(clipped_indices, axis=0)
idx = clipped_indices % cumulative_sizes[:-1] // cumulative_sizes[1:]
# TODO(jakevdp): return tuple(idx) once it behaves properly (#3821)
return tuple(lax.index_in_dim(idx, i, keepdims=False) for i in range(idx.shape[0]))
@_wraps(np.resize)
@partial(jit, static_argnames=('new_shape',))
def resize(a, new_shape):
_check_arraylike("resize", a)
new_shape = _ensure_index_tuple(new_shape)
if _any(dim_length < 0 for dim_length in new_shape):
raise ValueError("all elements of `new_shape` must be non-negative")
a = ravel(a)
new_size = _prod(new_shape)
if a.size == 0 or new_size == 0:
return zeros_like(a, shape=new_shape)
repeats = ceil_of_ratio(new_size, a.size)
a = tile(a, repeats)[:new_size]
return reshape(a, new_shape)
@_wraps(np.squeeze, lax_description=_ARRAY_VIEW_DOC)
def squeeze(a, axis: Optional[Union[int, Tuple[int, ...]]] = None):
return _squeeze(a, _ensure_index_tuple(axis) if axis is not None else None)
@partial(jit, static_argnames=('axis',), inline=True)
def _squeeze(a, axis):
_check_arraylike("squeeze", a)
if axis is None:
a_shape = shape(a)
axis = tuple(i for i, d in enumerate(a_shape) if d == 1)
return lax.squeeze(a, axis)
@_wraps(np.expand_dims)
def expand_dims(a, axis: Union[int, Sequence[int]]):
_check_arraylike("expand_dims", a)
return lax.expand_dims(a, _ensure_index_tuple(axis))
@_wraps(np.swapaxes, lax_description=_ARRAY_VIEW_DOC)
@partial(jit, static_argnames=('axis1', 'axis2'), inline=True)
def swapaxes(a, axis1: int, axis2: int):
_check_arraylike("swapaxes", a)
perm = np.arange(ndim(a))
perm[axis1], perm[axis2] = perm[axis2], perm[axis1]
return lax.transpose(a, perm)
@_wraps(np.moveaxis, lax_description=_ARRAY_VIEW_DOC)
def moveaxis(a, source: Union[int, Sequence[int]],
destination: Union[int, Sequence[int]]):
return _moveaxis(a, _ensure_index_tuple(source),
_ensure_index_tuple(destination))
@partial(jit, static_argnames=('source', 'destination'), inline=True)
def _moveaxis(a, source: Tuple[int, ...], destination: Tuple[int, ...]):
_check_arraylike("moveaxis", a)
source = tuple(_canonicalize_axis(i, ndim(a)) for i in source)
destination = tuple(_canonicalize_axis(i, ndim(a)) for i in destination)
if len(source) != len(destination):
raise ValueError("Inconsistent number of elements: {} vs {}"
.format(len(source), len(destination)))
perm = [i for i in range(ndim(a)) if i not in source]
for dest, src in sorted(zip(destination, source)):
perm.insert(dest, src)
return lax.transpose(a, perm)
@_wraps(np.isclose)
@partial(jit, static_argnames=('equal_nan',))
def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
a, b = _promote_args("isclose", a, b)
dtype = _dtype(a)
if issubdtype(dtype, inexact):
if issubdtype(dtype, complexfloating):
dtype = _complex_elem_type(dtype)
rtol = lax.convert_element_type(rtol, dtype)
atol = lax.convert_element_type(atol, dtype)
out = lax.le(
lax.abs(lax.sub(a, b)),
lax.add(atol, lax.mul(rtol, lax.abs(b))))
# This corrects the comparisons for infinite and nan values
a_inf = isinf(a)
b_inf = isinf(b)
any_inf = logical_or(a_inf, b_inf)
both_inf = logical_and(a_inf, b_inf)
# Make all elements where either a or b are infinite to False
out = logical_and(out, logical_not(any_inf))
# Make all elements where both a or b are the same inf to True
same_value = lax.eq(a, b)
same_inf = logical_and(both_inf, same_value)
out = logical_or(out, same_inf)
# Make all elements where either a or b is NaN to False
a_nan = isnan(a)
b_nan = isnan(b)
any_nan = logical_or(a_nan, b_nan)
out = logical_and(out, logical_not(any_nan))
if equal_nan:
# Make all elements where both a and b is NaN to True
both_nan = logical_and(a_nan, b_nan)
out = logical_or(out, both_nan)
return out
else:
return lax.eq(a, b)
@_wraps(np.interp)
@partial(jit, static_argnames=('period',))
def interp(x, xp, fp, left=None, right=None, period=None):
if shape(xp) != shape(fp) or ndim(xp) != 1:
raise ValueError("xp and fp must be one-dimensional arrays of equal size")
x, xp, fp = _promote_dtypes_inexact(x, xp, fp)
if period is not None:
if period == 0:
raise ValueError(f"period must be a non-zero value; got {period}")
period = abs(period)
x = x % period
xp = xp % period
xp, fp = lax.sort_key_val(xp, fp)
xp = concatenate([xp[-1:] - period, xp, xp[:1] + period])
fp = concatenate([fp[-1:], fp, fp[:1]])
i = clip(searchsorted(xp, x, side='right'), 1, len(xp) - 1)
df = fp[i] - fp[i - 1]
dx = xp[i] - xp[i - 1]
delta = x - xp[i - 1]
f = where((dx == 0), fp[i], fp[i - 1] + (delta / dx) * df)
if period is None:
f = where(x < xp[0], fp[0] if left is None else left, f)
f = where(x > xp[-1], fp[-1] if right is None else right, f)
return f
@_wraps(np.in1d, lax_description="""
In the JAX version, the `assume_unique` argument is not referenced.
""")
@partial(jit, static_argnames=('assume_unique', 'invert',))
def in1d(ar1, ar2, assume_unique=False, invert=False):
_check_arraylike("in1d", ar1, ar2)
ar1 = ravel(ar1)
ar2 = ravel(ar2)
# Note: an algorithm based on searchsorted has better scaling, but in practice
# is very slow on accelerators because it relies on lax control flow. If XLA
# ever supports binary search natively, we should switch to this:
# ar2 = jnp.sort(ar2)
# ind = jnp.searchsorted(ar2, ar1)
# if invert:
# return ar1 != ar2[ind]
# else:
# return ar1 == ar2[ind]
if invert:
return (ar1[:, None] != ar2[None, :]).all(-1)
else:
return (ar1[:, None] == ar2[None, :]).any(-1)
_SETDIFF1D_DOC = """\
Because the size of the output of ``setdiff1d`` is data-dependent, the function is not
typically compatible with JIT. The JAX version adds the optional `size` argument which
specifies the size of the output array: it must be specified statically for ``jnp.setdiff1d``
to be compiled with non-static operands. If specified, the first `size` unique elements will
be returned; if there are fewer unique elements than `size` indicates, the return value will
be padded with the `fill_value`, which defaults to zero."""
@_wraps(np.setdiff1d, lax_description=_SETDIFF1D_DOC)
def setdiff1d(ar1, ar2, assume_unique=False, *, size=None, fill_value=None):
_check_arraylike("setdiff1d", ar1, ar2)
if size is None:
ar1 = core.concrete_or_error(None, ar1, "The error arose in setdiff1d()")
else:
size = core.concrete_or_error(operator.index, size, "The error arose in setdiff1d()")
ar1 = asarray(ar1)
fill_value = asarray(0 if fill_value is None else fill_value, dtype=ar1.dtype)
if ar1.size == 0:
return full_like(ar1, fill_value, shape=size or 0)
if not assume_unique:
ar1 = unique(ar1, size=size and ar1.size)
mask = in1d(ar1, ar2, invert=True)
if size is None:
return ar1[mask]
else:
if not (assume_unique or size is None):
# Set mask to zero at locations corresponding to unique() padding.
n_unique = ar1.size + 1 - (ar1 == ar1[0]).sum()
mask = where(arange(ar1.size) < n_unique, mask, False)
return where(arange(size) < mask.sum(), ar1[where(mask, size=size)], fill_value)
_UNION1D_DOC = """\
Because the size of the output of ``union1d`` is data-dependent, the function is not
typically compatible with JIT. The JAX version adds the optional `size` argument which
specifies the size of the output array: it must be specified statically for ``jnp.union1d``
to be compiled with non-static operands. If specified, the first `size` unique elements
will be returned; if there are fewer unique elements than `size` indicates, the return
value will be padded with `fill_value`, which defaults to the minimum value of the union."""
@_wraps(np.union1d, lax_description=_UNION1D_DOC)
def union1d(ar1, ar2, *, size=None, fill_value=None):
_check_arraylike("union1d", ar1, ar2)
if size is None:
ar1 = core.concrete_or_error(None, ar1, "The error arose in union1d()")
ar2 = core.concrete_or_error(None, ar2, "The error arose in union1d()")
else:
size = core.concrete_or_error(operator.index, size, "The error arose in union1d()")
return unique(concatenate((ar1, ar2), axis=None), size=size, fill_value=fill_value)
@_wraps(np.setxor1d, lax_description="""
In the JAX version, the input arrays are explicitly flattened regardless
of assume_unique value.
""")
def setxor1d(ar1, ar2, assume_unique=False):
_check_arraylike("setxor1d", ar1, ar2)
ar1 = core.concrete_or_error(None, ar1, "The error arose in setxor1d()")
ar2 = core.concrete_or_error(None, ar2, "The error arose in setxor1d()")
ar1 = ravel(ar1)
ar2 = ravel(ar2)
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux = sort(aux)
flag = concatenate((array([True]), aux[1:] != aux[:-1], array([True])))
return aux[flag[1:] & flag[:-1]]
@partial(jit, static_argnums=2)
def _intersect1d_sorted_mask(ar1, ar2, return_indices=False):
"""
Helper function for intersect1d which is jit-able
"""
ar = concatenate((ar1, ar2))
if return_indices:
iota = lax.broadcasted_iota(np.int64, shape(ar), dimension=0)
aux, indices = lax.sort_key_val(ar, iota)
else:
aux = sort(ar)
mask = aux[1:] == aux[:-1]
if return_indices:
return aux, mask, indices
else:
return aux, mask
@_wraps(np.intersect1d)
def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
_check_arraylike("intersect1d", ar1, ar2)
ar1 = core.concrete_or_error(None, ar1, "The error arose in intersect1d()")
ar2 = core.concrete_or_error(None, ar2, "The error arose in intersect1d()")
if not assume_unique:
if return_indices:
ar1, ind1 = unique(ar1, return_index=True)
ar2, ind2 = unique(ar2, return_index=True)
else:
ar1 = unique(ar1)
ar2 = unique(ar2)
else:
ar1 = ravel(ar1)
ar2 = ravel(ar2)
if return_indices:
aux, mask, aux_sort_indices = _intersect1d_sorted_mask(ar1, ar2, return_indices)
else:
aux, mask = _intersect1d_sorted_mask(ar1, ar2, return_indices)
int1d = aux[:-1][mask]
if return_indices:
ar1_indices = aux_sort_indices[:-1][mask]
ar2_indices = aux_sort_indices[1:][mask] - ar1.size
if not assume_unique:
ar1_indices = ind1[ar1_indices]
ar2_indices = ind2[ar2_indices]
return int1d, ar1_indices, ar2_indices
else:
return int1d
@_wraps(np.isin, lax_description="""
In the JAX version, the `assume_unique` argument is not referenced.
""")
def isin(element, test_elements, assume_unique=False, invert=False):
result = in1d(element, test_elements, assume_unique=assume_unique, invert=invert)
return result.reshape(shape(element))
# The `jit` on `where` exists to avoid materializing constants in cases like
# `np.where(np.zeros(1000), 7, 4)`. In op-by-op mode, we don't want to
# materialize the broadcast forms of scalar arguments.
@jit
def _where(condition, x=None, y=None):
if x is None or y is None:
raise ValueError("Either both or neither of the x and y arguments should "
"be provided to jax.numpy.where, got {} and {}."
.format(x, y))
if not issubdtype(_dtype(condition), bool_):
condition = lax.ne(condition, zeros_like(condition))
x, y = _promote_dtypes(x, y)
condition, x, y = broadcast_arrays(condition, x, y)
return lax.select(condition, x, y) if not core.is_empty_shape(np.shape(x)) else x
_WHERE_DOC = """\
At present, JAX does not support JIT-compilation of the single-argument form
of :py:func:`jax.numpy.where` because its output shape is data-dependent. The
three-argument form does not have a data-dependent shape and can be JIT-compiled
successfully. Alternatively, you can specify the optional ``size`` keyword:
if specified, the first ``size`` True elements will be returned; if there
are fewer True elements than ``size`` indicates, the index arrays will be
padded with ``fill_value`` (default is 0.)
"""
@_wraps(np.where, update_doc=False, lax_description=_WHERE_DOC)
def where(condition, x=None, y=None, *, size=None, fill_value=None):
if x is None and y is None:
_check_arraylike("where", condition)
return nonzero(condition, size=size, fill_value=fill_value)
else:
if size is not None or fill_value is not None:
raise ValueError("size and fill_value arguments cannot be used in three-term where function.")
return _where(condition, x, y)
@_wraps(np.select)
def select(condlist, choicelist, default=0):
if len(condlist) != len(choicelist):
msg = "condlist must have length equal to choicelist ({} vs {})"
raise ValueError(msg.format(len(condlist), len(choicelist)))
if len(condlist) == 0:
raise ValueError("condlist must be non-empty")
choices = _promote_dtypes(default, *choicelist)
choicelist = choices[1:]
output = choices[0]
for cond, choice in zip(condlist[::-1], choicelist[::-1]):
output = where(cond, choice, output)
return output
@_wraps(np.bincount, lax_description="""\
Jax adds the optional `length` parameter which specifies the output length, and
defaults to ``x.max() + 1``. It must be specified for bincount to be compiled
with non-static operands. Values larger than the specified length will be discarded.
If `length` is specified, `minlength` will be ignored.
Additionally, while ``np.bincount`` raises an error if the input array contains
negative values, ``jax.numpy.bincount`` clips negative values to zero.
""")
def bincount(x, weights=None, minlength=0, *, length=None):
_check_arraylike("bincount", x)
if not issubdtype(_dtype(x), integer):
msg = f"x argument to bincount must have an integer type; got {x.dtype}"
raise TypeError(msg)
if ndim(x) != 1:
raise ValueError("only 1-dimensional input supported.")
minlength = core.concrete_or_error(operator.index, minlength,
"The error occurred because of argument 'minlength' of jnp.bincount.")
if length is None:
x = core.concrete_or_error(asarray, x,
"The error occured because of argument 'x' of jnp.bincount. "
"To avoid this error, pass a static `length` argument.")
length = _max(minlength, x.size and x.max() + 1)
else:
length = core.concrete_or_error(operator.index, length,
"The error occurred because of argument 'length' of jnp.bincount.")
if weights is None:
weights = np.array(1, dtype=int_)
elif shape(x) != shape(weights):
raise ValueError("shape of weights must match shape of x.")
return zeros(length, _dtype(weights)).at[clip(x, 0)].add(weights)
@_wraps(getattr(np, "broadcast_shapes", None))
def broadcast_shapes(*shapes):
if not shapes:
return ()
shapes = [(shape,) if np.ndim(shape) == 0 else tuple(shape) for shape in shapes]
return lax.broadcast_shapes(*shapes)
@partial(jit, inline=True)
def broadcast_arrays(*args):
"""Like Numpy's broadcast_arrays but doesn't return views."""
shapes = [shape(arg) for arg in args]
if len(set(shapes)) == 1:
return [arg if isinstance(arg, ndarray) or isscalar(arg) else array(arg)
for arg in args]
result_shape = lax.broadcast_shapes(*shapes)
return [broadcast_to(arg, result_shape) for arg in args]
@_wraps(np.broadcast_to, lax_description="""\
The JAX version does not necessarily return a view of the input.
""")
def broadcast_to(arr, shape):
if hasattr(arr, "broadcast_to"):
return arr.broadcast_to(shape)
arr = arr if isinstance(arr, ndarray) else array(arr)
shape = (shape,) if ndim(shape) == 0 else shape
shape = canonicalize_shape(shape) # check that shape is concrete
arr_shape = _shape(arr)
if core.symbolic_equal_shape(arr_shape, shape):
return arr
else:
nlead = len(shape) - len(arr_shape)
shape_tail = shape[nlead:]
compatible = _all(core.symbolic_equal_one_of_dim(arr_d, [1, shape_d])
for arr_d, shape_d in safe_zip(arr_shape, shape_tail))
if nlead < 0 or not compatible:
msg = "Incompatible shapes for broadcasting: {} and requested shape {}"
raise ValueError(msg.format(arr_shape, shape))
diff, = np.where(tuple(not core.symbolic_equal_dim(arr_d, shape_d)
for arr_d, shape_d in safe_zip(arr_shape, shape_tail)))
new_dims = tuple(range(nlead)) + tuple(nlead + diff)
kept_dims = tuple(np.delete(np.arange(len(shape)), new_dims))
return lax.broadcast_in_dim(squeeze(arr, tuple(diff)), shape, kept_dims)
def _split(op, ary, indices_or_sections, axis=0):
axis = core.concrete_or_error(int, axis, f"in jax.numpy.{op} argument `axis`")
size = ary.shape[axis]
if isinstance(indices_or_sections, (tuple, list)):
indices_or_sections = np.array(
[core.concrete_or_error(np.int64, i_s, f"in jax.numpy.{op} argument 1")
for i_s in indices_or_sections], np.int64)
split_indices = np.concatenate([[np.int64(0)], indices_or_sections,
[np.int64(size)]])
elif (isinstance(indices_or_sections, (np.ndarray, ndarray)) and
indices_or_sections.ndim > 0):
indices_or_sections = np.array(
[core.concrete_or_error(np.int64, i_s, f"in jax.numpy.{op} argument 1")
for i_s in indices_or_sections], np.int64)
split_indices = np.concatenate([[np.int64(0)], indices_or_sections,
[np.int64(size)]])
else:
indices_or_sections = core.concrete_or_error(np.int64, indices_or_sections,
f"in jax.numpy.{op} argument 1")
part_size, r = _divmod(size, indices_or_sections)
if r == 0:
split_indices = np.arange(indices_or_sections + 1,
dtype=np.int64) * part_size
elif op == "array_split":
split_indices = np.concatenate(
[np.arange(r + 1, dtype=np.int64) * (part_size + 1),
np.arange(indices_or_sections - r, dtype=np.int64) * part_size
+ ((r + 1) * (part_size + 1) - 1)])
else:
raise ValueError("array split does not result in an equal division")
starts, ends = [0] * ndim(ary), shape(ary)
_subval = lambda x, i, v: subvals(x, [(i, v)])
return [lax.slice(ary, _subval(starts, axis, start), _subval(ends, axis, end))
for start, end in zip(split_indices[:-1], split_indices[1:])]
@_wraps(np.split, lax_description=_ARRAY_VIEW_DOC)
def split(ary, indices_or_sections, axis: int = 0):
return _split("split", ary, indices_or_sections, axis=axis)
def _split_on_axis(np_fun, axis):
@_wraps(np_fun, update_doc=False)
def f(ary, indices_or_sections):
return split(ary, indices_or_sections, axis=axis)
return f
vsplit = _split_on_axis(np.vsplit, axis=0)
hsplit = _split_on_axis(np.hsplit, axis=1)
dsplit = _split_on_axis(np.dsplit, axis=2)
@_wraps(np.array_split)
def array_split(ary, indices_or_sections, axis: int = 0):
return _split("array_split", ary, indices_or_sections, axis=axis)
@_wraps(np.clip, skip_params=['out'])
@jit
def clip(a, a_min=None, a_max=None, out=None):
_check_arraylike("clip", a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.clip is not supported.")
if a_min is None and a_max is None:
raise ValueError("At most one of a_min and a_max may be None")
if a_min is not None:
a = maximum(a_min, a)
if a_max is not None:
a = minimum(a_max, a)
return a
@_wraps(np.around, skip_params=['out'])
@partial(jit, static_argnames=('decimals',))
def round(a, decimals=0, out=None):
_check_arraylike("round", a)
decimals = core.concrete_or_error(operator.index, decimals, "'decimals' argument of jnp.round")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.round is not supported.")
dtype = _dtype(a)
if issubdtype(dtype, integer):
if decimals < 0:
raise NotImplementedError(
"integer np.round not implemented for decimals < 0")
return a # no-op on integer types
def _round_float(x):
if decimals == 0:
return lax.round(x, lax.RoundingMethod.TO_NEAREST_EVEN)
# TODO(phawkins): the strategy of rescaling the value isn't necessarily a
# good one since we may be left with an incorrectly rounded value at the
# end due to precision problems. As a workaround for float16, convert to
# float32,
x = lax.convert_element_type(x, np.float32) if dtype == np.float16 else x
factor = _constant_like(x, 10 ** decimals)
out = lax.div(lax.round(lax.mul(x, factor),
lax.RoundingMethod.TO_NEAREST_EVEN), factor)
return lax.convert_element_type(out, dtype) if dtype == np.float16 else out
if issubdtype(dtype, complexfloating):
return lax.complex(_round_float(lax.real(a)), _round_float(lax.imag(a)))
else:
return _round_float(a)
around = round
round_ = round
@_wraps(np.fix, skip_params=['out'])
@jit
def fix(x, out=None):
_check_arraylike("fix", x)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.fix is not supported.")
zero = lax._const(x, 0)
return where(lax.ge(x, zero), floor(x), ceil(x))
@_wraps(np.modf, skip_params=['out'])
@jit
def modf(x, out=None):
_check_arraylike("modf", x)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.modf is not supported.")
whole = fix(x)
return x - whole, whole
@_wraps(np.isfinite)
@jit
def isfinite(x):
_check_arraylike("isfinite", x)
dtype = _dtype(x)
if issubdtype(dtype, floating):
return lax.is_finite(x)
elif issubdtype(dtype, complexfloating):
return lax.bitwise_and(lax.is_finite(real(x)), lax.is_finite(imag(x)))
else:
return full_like(x, True, dtype=bool_)
@_wraps(np.isinf)
@jit
def isinf(x):
_check_arraylike("isinf", x)
dtype = _dtype(x)
if issubdtype(dtype, floating):
return lax.eq(lax.abs(x), _constant_like(x, inf))
elif issubdtype(dtype, complexfloating):
re = lax.real(x)
im = lax.imag(x)
return lax.bitwise_or(lax.eq(lax.abs(re), _constant_like(re, inf)),
lax.eq(lax.abs(im), _constant_like(im, inf)))
else:
return full_like(x, False, dtype=bool_)
def _isposneginf(infinity, x, out):
if out is not None:
raise NotImplementedError("The 'out' argument to isneginf/isposinf is not supported.")
dtype = _dtype(x)
if issubdtype(dtype, floating):
return lax.eq(x, _constant_like(x, infinity))
elif issubdtype(dtype, complexfloating):
raise ValueError("isposinf/isneginf are not well defined for complex types")
else:
return full_like(x, False, dtype=bool_)
isposinf = _wraps(np.isposinf, skip_params=['out'])(
lambda x, out=None: _isposneginf(inf, x, out)
)
isneginf = _wraps(np.isneginf, skip_params=['out'])(
lambda x, out=None: _isposneginf(-inf, x, out)
)
@_wraps(np.isnan)
@jit
def isnan(x):
_check_arraylike("isnan", x)
return lax.ne(x, x)
@_wraps(np.nan_to_num)
@jit
def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
del copy
_check_arraylike("nan_to_num", x)
dtype = _dtype(x)
if issubdtype(dtype, complexfloating):
return lax.complex(
nan_to_num(lax.real(x), nan=nan, posinf=posinf, neginf=neginf),
nan_to_num(lax.imag(x), nan=nan, posinf=posinf, neginf=neginf))
info = finfo(dtypes.canonicalize_dtype(dtype))
posinf = info.max if posinf is None else posinf
neginf = info.min if neginf is None else neginf
x = where(isnan(x), array(nan, dtype=x.dtype), x)
x = where(isposinf(x), array(posinf, dtype=x.dtype), x)
x = where(isneginf(x), array(neginf, dtype=x.dtype), x)
return x
### Reducers
def _reduction(a, name, np_fun, op, init_val, has_identity=True,
preproc=None, bool_op=None, upcast_f16_for_computation=False,
axis=None, dtype=None, out=None, keepdims=False, initial=None,
where_=None, parallel_reduce=None):
bool_op = bool_op or op
# Note: we must accept out=None as an argument, because numpy reductions delegate to
# object methods. For example `np.sum(x)` will call `x.sum()` if the `sum()` method
# exists, passing along all its arguments.
if out is not None:
raise NotImplementedError(f"The 'out' argument to jnp.{name} is not supported.")
_check_arraylike(name, a)
lax._check_user_dtype_supported(dtype, name)
axis = core.concrete_or_error(None, axis, f"axis argument to jnp.{name}().")
if initial is None and not has_identity:
if not _all(core.greater_equal_dim(d, 1) for d in np.shape(a)):
raise ValueError(f"zero-size array to reduction operation {name} which has no identity")
if where_ is not None:
raise ValueError(f"reduction operation {name} does not have an identity, so to use a "
f"where mask one has to specify 'initial'")
a = a if isinstance(a, ndarray) else asarray(a)
a = preproc(a) if preproc else a
pos_dims, dims = _reduction_dims(a, axis)
result_dtype = dtypes.canonicalize_dtype(dtype or _dtype(np_fun(np.ones((), dtype=_dtype(a)))))
if upcast_f16_for_computation and issubdtype(result_dtype, inexact):
computation_dtype = promote_types(result_dtype, float32)
else:
computation_dtype = result_dtype
a = lax.convert_element_type(a, computation_dtype)
op = op if computation_dtype != np.bool_ else bool_op
# NB: in XLA, init_val must be an identity for the op, so the user-specified
# initial value must be applied afterward.
init_val = _reduction_init_val(a, init_val)
if where_ is not None:
a = where(where_, a, init_val)
if pos_dims is not dims:
if parallel_reduce is None:
raise NotImplementedError(f"Named reductions not implemented for jnp.{name}()")
result = parallel_reduce(a, dims)
else:
result = lax.reduce(a, init_val, op, dims)
if initial is not None:
result = op(lax.convert_element_type(initial, a.dtype), result)
if keepdims:
result = expand_dims(result, pos_dims)
return lax.convert_element_type(result, dtype or result_dtype)
def _canonicalize_axis_allow_named(x, rank):
return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
def _reduction_dims(a, axis):
if axis is None:
return (tuple(range(ndim(a))),) * 2
elif not isinstance(axis, (np.ndarray, tuple, list)):
axis = (axis,)
canon_axis = tuple(_canonicalize_axis_allow_named(x, ndim(a))
for x in axis)
if len(canon_axis) != len(set(canon_axis)):
raise ValueError(f"duplicate value in 'axis': {axis}")
canon_pos_axis = tuple(x for x in canon_axis if isinstance(x, int))
if len(canon_pos_axis) != len(canon_axis):
return canon_pos_axis, canon_axis
else:
return canon_axis, canon_axis
def _reduction_init_val(a, init_val):
# This function uses np.* functions because lax pattern matches against the
# specific concrete values of the reduction inputs.
a_dtype = dtypes.canonicalize_dtype(_dtype(a))
if a_dtype == 'bool':
return np.array(init_val > 0, dtype=a_dtype)
try:
return np.array(init_val, dtype=a_dtype)
except OverflowError:
assert issubdtype(a_dtype, integer)
sign, info = np.sign(init_val), iinfo(a_dtype)
return np.array(info.min if sign < 0 else info.max, dtype=a_dtype)
def _cast_to_bool(operand):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=np.ComplexWarning)
return lax.convert_element_type(operand, bool_)
def _ensure_optional_axes(x):
def force(x):
if x is None:
return None
try:
return operator.index(x)
except TypeError:
return tuple(i if isinstance(i, str) else operator.index(i) for i in x)
return core.concrete_or_error(
force, x, "The axis argument must be known statically.")
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'), inline=True)
def _reduce_sum(a, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None, keepdims=None, initial=None, where=None):
return _reduction(a, "sum", np.sum, lax.add, 0,
bool_op=lax.bitwise_or, upcast_f16_for_computation=True,
axis=axis, dtype=dtype, out=out, keepdims=keepdims,
initial=initial, where_=where, parallel_reduce=lax.psum)
@_wraps(np.sum, skip_params=['out'])
def sum(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None, initial=None, where=None):
return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
keepdims=keepdims, initial=initial, where=where)
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'), inline=True)
def _reduce_prod(a, axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None, keepdims=None, initial=None, where=None):
return _reduction(a, "prod", np.prod, lax.mul, 1,
bool_op=lax.bitwise_and, upcast_f16_for_computation=True,
axis=axis, dtype=dtype, out=out, keepdims=keepdims,
initial=initial, where_=where)
@_wraps(np.prod, skip_params=['out'])
def prod(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None, initial=None, where=None):
return _reduce_prod(a, axis=_ensure_optional_axes(axis), dtype=dtype,
out=out, keepdims=keepdims, initial=initial, where=where)
@partial(jit, static_argnames=('axis', 'keepdims'), inline=True)
def _reduce_max(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None):
return _reduction(a, "max", np.max, lax.max, -np.inf, has_identity=False,
axis=axis, out=out, keepdims=keepdims,
initial=initial, where_=where, parallel_reduce=lax.pmax)
@_wraps(np.max, skip_params=['out'])
def max(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None):
return _reduce_max(a, axis=_ensure_optional_axes(axis), out=out,
keepdims=keepdims, initial=initial, where=where)
@partial(jit, static_argnames=('axis', 'keepdims'), inline=True)
def _reduce_min(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None):
return _reduction(a, "min", np.min, lax.min, np.inf, has_identity=False,
axis=axis, out=out, keepdims=keepdims,
initial=initial, where_=where, parallel_reduce=lax.pmin)
@_wraps(np.min, skip_params=['out'])
def min(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, initial=None, where=None):
return _reduce_min(a, axis=_ensure_optional_axes(axis), out=out,
keepdims=keepdims, initial=initial, where=where)
@partial(jit, static_argnames=('axis', 'keepdims'), inline=True)
def _reduce_all(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, *, where=None):
return _reduction(a, "all", np.all, lax.bitwise_and, True, preproc=_cast_to_bool,
axis=axis, out=out, keepdims=keepdims, where_=where)
@_wraps(np.all, skip_params=['out'])
def all(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, *, where=None):
return _reduce_all(a, axis=_ensure_optional_axes(axis), out=out,
keepdims=keepdims, where=where)
@partial(jit, static_argnames=('axis', 'keepdims'), inline=True)
def _reduce_any(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, *, where=None):
return _reduction(a, "any", np.any, lax.bitwise_or, False, preproc=_cast_to_bool,
axis=axis, out=out, keepdims=keepdims, where_=where)
@_wraps(np.any, skip_params=['out'])
def any(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None, *, where=None):
return _reduce_any(a, axis=_ensure_optional_axes(axis), out=out,
keepdims=keepdims, where=where)
product = prod
amin = min
amax = max
alltrue = all
sometrue = any
def _axis_size(a, axis):
if not isinstance(axis, (tuple, list)):
axis = (axis,)
size = 1
a_shape = shape(a)
for a in axis:
size *= maybe_named_axis(a, lambda i: a_shape[i], lambda name: lax.psum(1, name))
return size
@_wraps(np.mean, skip_params=['out'])
def mean(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=False, *, where=None):
return _mean(a, _ensure_optional_axes(axis), dtype, out, keepdims,
where=where)
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'), inline=True)
def _mean(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=False, *, where=None):
_check_arraylike("mean", a)
lax._check_user_dtype_supported(dtype, "mean")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.mean is not supported.")
if where is None:
if axis is None:
normalizer = core.dimension_as_value(size(a))
else:
normalizer = core.dimension_as_value(_axis_size(a, axis))
else:
normalizer = sum(broadcast_to(where, shape(a)), axis, dtype=dtype, keepdims=keepdims)
if dtype is None:
if issubdtype(_dtype(a), bool_) or issubdtype(_dtype(a), integer):
dtype = float_
else:
dtype = _dtype(a)
dtype = dtypes.canonicalize_dtype(dtype)
return lax.div(
sum(a, axis, dtype=dtype, keepdims=keepdims, where=where),
lax.convert_element_type(normalizer, dtype))
@_wraps(np.average)
def average(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, weights=None,
returned=False):
return _average(a, _ensure_optional_axes(axis), weights, returned)
@partial(jit, static_argnames=('axis', 'returned'), inline=True)
def _average(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, weights=None,
returned=False):
a = asarray(a)
if weights is None: # Treat all weights as 1
avg = mean(a, axis=axis)
if axis is None:
weights_sum = full((), core.dimension_as_value(size(a)), dtype=avg.dtype)
else:
weights_sum = full_like(avg, core.dimension_as_value(a.shape[axis]), dtype=avg.dtype)
else:
weights = asarray(weights)
if issubdtype(a.dtype, inexact):
out_dtype = result_type(a.dtype, weights.dtype)
else:
out_dtype = result_type(a.dtype, weights.dtype, float_)
out_dtype = dtypes.canonicalize_dtype(out_dtype)
a_shape = shape(a)
a_ndim = len(a_shape)
weights_shape = shape(weights)
axis = None if axis is None else _canonicalize_axis(axis, a_ndim)
if a_shape != weights_shape:
# Make sure the dimensions work out
if axis is None:
raise ValueError("Axis must be specified when shapes of a and "
"weights differ.")
if len(weights_shape) != 1:
raise ValueError("1D weights expected when shapes of a and "
"weights differ.")
if not core.symbolic_equal_dim(weights_shape[0], a_shape[axis]):
raise ValueError("Length of weights not "
"compatible with specified axis.")
weights = broadcast_to(weights, (a_ndim - 1) * (1,) + weights_shape)
weights = moveaxis(weights, -1, axis)
weights_sum = sum(weights, axis=axis, dtype=out_dtype)
avg = sum(multiply(a, weights), axis=axis, dtype=out_dtype) / weights_sum
if returned:
if avg.shape != weights_sum.shape:
weights_sum = broadcast_to(weights_sum, avg.shape)
return avg, weights_sum
return avg
@_wraps(np.var, skip_params=['out'])
def var(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False, *, where=None):
return _var(a, _ensure_optional_axes(axis), dtype, out, ddof, keepdims,
where=where)
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def _var(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False, *, where=None):
_check_arraylike("var", a)
lax._check_user_dtype_supported(dtype, "var")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.var is not supported.")
a_dtype, dtype = _var_promote_types(_dtype(a), dtype)
a_mean = mean(a, axis, dtype=a_dtype, keepdims=True, where=where)
centered = a - a_mean
if issubdtype(centered.dtype, complexfloating):
centered = lax.real(lax.mul(centered, lax.conj(centered)))
else:
centered = lax.square(centered)
if where is None:
if axis is None:
normalizer = core.dimension_as_value(size(a))
else:
normalizer = core.dimension_as_value(_axis_size(a, axis))
else:
normalizer = sum(broadcast_to(where, shape(a)), axis, dtype=dtype, keepdims=keepdims)
normalizer = normalizer - ddof
result = sum(centered, axis, keepdims=keepdims, where=where)
out = lax.div(result, lax.convert_element_type(normalizer, result.dtype))
return lax.convert_element_type(out, dtype)
def _var_promote_types(a_dtype, dtype):
if dtype:
if (not issubdtype(dtype, complexfloating) and
issubdtype(a_dtype, complexfloating)):
msg = ("jax.numpy.var does not yet support real dtype parameters when "
"computing the variance of an array of complex values. The "
"semantics of numpy.var seem unclear in this case. Please comment "
"on https://github.com/google/jax/issues/2283 if this behavior is "
"important to you.")
raise ValueError(msg)
a_dtype = promote_types(a_dtype, dtype)
else:
if not issubdtype(a_dtype, inexact):
dtype = a_dtype = dtypes.canonicalize_dtype(float_)
else:
dtype = _complex_elem_type(a_dtype)
a_dtype = promote_types(a_dtype, float32)
return a_dtype, dtype
@_wraps(np.std, skip_params=['out'])
def std(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False, *, where=None):
return _std(a, _ensure_optional_axes(axis), dtype, out, ddof, keepdims,
where=where)
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def _std(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False, *, where=None):
_check_arraylike("std", a)
lax._check_user_dtype_supported(dtype, "std")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.std is not supported.")
return sqrt(var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, where=where))
@_wraps(np.ptp, skip_params=['out'])
def ptp(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=False):
return _ptp(a, _ensure_optional_axes(axis), out, keepdims)
@partial(jit, static_argnames=('axis', 'keepdims'))
def _ptp(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=False):
_check_arraylike("ptp", a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.ptp is not supported.")
x = amax(a, axis=axis, keepdims=keepdims)
y = amin(a, axis=axis, keepdims=keepdims)
return lax.sub(x, y)
@_wraps(np.allclose)
@partial(jit, static_argnames=('equal_nan',))
def allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
_check_arraylike("allclose", a, b)
return all(isclose(a, b, rtol, atol, equal_nan))
@_wraps(np.count_nonzero)
@partial(jit, static_argnames=('axis', 'keepdims'))
def count_nonzero(a, axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims=False):
_check_arraylike("count_nonzero", a)
return sum(lax.ne(a, _constant_like(a, 0)), axis=axis,
dtype=dtypes.canonicalize_dtype(np.int_), keepdims=keepdims)
_NONZERO_DOC = """\
Because the size of the output of ``nonzero`` is data-dependent, the function is not
typically compatible with JIT. The JAX version adds the optional `size` argument which
specifies the size of the output arrays: it must be specified statically for ``jnp.nonzero``
to be compiled with non-static operands. If specified, the first `size` nonzero elements
will be returned; if there are fewer nonzero elements than `size` indicates, the result
will be padded with ``fill_value``, which defaults to zero. ``fill_value`` may be a scalar,
or a tuple specifying the fill value in each dimension.
"""
@_wraps(np.nonzero, lax_description=_NONZERO_DOC)
def nonzero(a, *, size=None, fill_value=None):
a = atleast_1d(a)
mask = a != 0
if size is None:
size = mask.sum()
size = core.concrete_or_error(int, size,
"The size argument of jnp.nonzero must be statically specified "
"to use jnp.nonzero within JAX transformations.")
if a.size == 0 or size == 0:
return tuple(zeros(size, int) for dim in a.shape)
flat_indices = cumsum(bincount(cumsum(mask), length=size))
strides = (np.cumprod(a.shape[::-1])[::-1] // a.shape).astype(int_)
out = tuple((flat_indices // stride) % size for stride, size in zip(strides, a.shape))
if size is not None and fill_value is not None:
if not isinstance(fill_value, tuple):
fill_value = a.ndim * (fill_value,)
if _shape(fill_value) != (a.ndim,):
raise ValueError(f"fill_value must be a scalar or a tuple of length {a.ndim}; got {fill_value}")
fill_mask = arange(size) >= mask.sum()
out = tuple(where(fill_mask, fval, entry) for fval, entry in safe_zip(fill_value, out))
return out
@_wraps(np.flatnonzero, lax_description=_NONZERO_DOC)
def flatnonzero(a, *, size=None, fill_value=None):
return nonzero(ravel(a), size=size, fill_value=fill_value)[0]
def _nan_reduction(a, name, jnp_reduction, init_val, nan_if_all_nan,
axis=None, keepdims=None, **kwargs):
_check_arraylike(name, a)
if not issubdtype(_dtype(a), inexact):
return jnp_reduction(a, axis=axis, keepdims=keepdims, **kwargs)
out = jnp_reduction(where(isnan(a), _reduction_init_val(a, init_val), a),
axis=axis, keepdims=keepdims, **kwargs)
if nan_if_all_nan:
return where(all(isnan(a), axis=axis, keepdims=keepdims),
_constant_like(a, nan), out)
else:
return out
@_wraps(np.nanmin, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'keepdims'))
def nanmin(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None):
return _nan_reduction(a, 'nanmin', min, inf, nan_if_all_nan=True,
axis=axis, out=out, keepdims=keepdims)
@_wraps(np.nanmax, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'keepdims'))
def nanmax(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
keepdims=None):
return _nan_reduction(a, 'nanmax', max, -inf, nan_if_all_nan=True,
axis=axis, out=out, keepdims=keepdims)
@_wraps(np.nansum, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def nansum(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None, initial=None, where=None):
lax._check_user_dtype_supported(dtype, "nanprod")
return _nan_reduction(a, 'nansum', sum, 0, nan_if_all_nan=False,
axis=axis, dtype=dtype, out=out, keepdims=keepdims)
# Work around a sphinx documentation warning in NumPy 1.22.
nansum.__doc__ = nansum.__doc__.replace("\n\n\n", "\n\n")
@_wraps(np.nanprod, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def nanprod(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=None):
lax._check_user_dtype_supported(dtype, "nanprod")
return _nan_reduction(a, 'nanprod', prod, 1, nan_if_all_nan=False,
axis=axis, dtype=dtype, out=out, keepdims=keepdims)
@_wraps(np.nanmean, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def nanmean(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, keepdims=False):
_check_arraylike("nanmean", a)
lax._check_user_dtype_supported(dtype, "nanmean")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.nanmean is not supported.")
if issubdtype(_dtype(a), bool_) or issubdtype(_dtype(a), integer):
return mean(a, axis, dtype, out, keepdims)
if dtype is None:
dtype = _dtype(a)
nan_mask = logical_not(isnan(a))
normalizer = sum(nan_mask, axis=axis, dtype=int32, keepdims=keepdims)
normalizer = lax.convert_element_type(normalizer, dtype)
td = lax.div(nansum(a, axis, dtype=dtype, keepdims=keepdims), normalizer)
return td
@_wraps(np.nanvar, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def nanvar(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False):
_check_arraylike("nanvar", a)
lax._check_user_dtype_supported(dtype, "nanvar")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.nanvar is not supported.")
a_dtype, dtype = _var_promote_types(_dtype(a), dtype)
a_mean = nanmean(a, axis, dtype=a_dtype, keepdims=True)
centered = where(isnan(a), 0, a - a_mean) # double-where trick for gradients.
if issubdtype(centered.dtype, complexfloating):
centered = lax.real(lax.mul(centered, lax.conj(centered)))
else:
centered = lax.square(centered)
normalizer = sum(logical_not(isnan(a)), axis=axis, keepdims=keepdims)
normalizer = normalizer - ddof
normalizer_mask = lax.le(normalizer, 0)
result = sum(centered, axis, keepdims=keepdims)
result = where(normalizer_mask, nan, result)
divisor = where(normalizer_mask, 1, normalizer)
out = lax.div(result, lax.convert_element_type(divisor, result.dtype))
return lax.convert_element_type(out, dtype)
@_wraps(np.nanstd, skip_params=['out'])
@partial(jit, static_argnames=('axis', 'dtype', 'keepdims'))
def nanstd(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
out=None, ddof=0, keepdims=False):
_check_arraylike("nanstd", a)
lax._check_user_dtype_supported(dtype, "nanstd")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.nanstd is not supported.")
return sqrt(nanvar(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims))
def _make_cumulative_reduction(np_reduction, reduction, fill_nan=False, fill_value=0):
@_wraps(np_reduction, skip_params=['out'])
def cumulative_reduction(a,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None):
return _cumulative_reduction(a, _ensure_optional_axes(axis), dtype, out)
@partial(jit, static_argnames=('axis', 'dtype'))
def _cumulative_reduction(a,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None, out=None):
_check_arraylike(np_reduction.__name__, a)
if out is not None:
raise NotImplementedError(f"The 'out' argument to jnp.{np_reduction.__name__} "
f"is not supported.")
lax._check_user_dtype_supported(dtype, np_reduction.__name__)
if axis is None or isscalar(a):
a = ravel(a)
axis = 0
a_shape = list(shape(a))
num_dims = len(a_shape)
axis = _canonicalize_axis(axis, num_dims)
if fill_nan:
a = where(isnan(a), _constant_like(a, fill_value), a)
if not dtype and _dtype(a) == bool_:
dtype = int_
if dtype:
a = lax.convert_element_type(a, dtype)
return reduction(a, axis)
return cumulative_reduction
cumsum = _make_cumulative_reduction(np.cumsum, lax.cumsum, fill_nan=False)
cumprod = _make_cumulative_reduction(np.cumprod, lax.cumprod, fill_nan=False)
cumproduct = cumprod
nancumsum = _make_cumulative_reduction(np.nancumsum, lax.cumsum,
fill_nan=True, fill_value=0)
nancumprod = _make_cumulative_reduction(np.nancumprod, lax.cumprod,
fill_nan=True, fill_value=1)
@_wraps(np.unwrap)
@partial(jit, static_argnames=('axis',))
def unwrap(p, discont=pi, axis: int = -1):
_check_arraylike("unwrap", p)
dd = diff(p, axis=axis)
ddmod = mod(dd + pi, 2 * pi) - pi
ddmod = where((ddmod == -pi) & (dd > 0), pi, ddmod)
ph_correct = where(abs(dd) < discont, 0, ddmod - dd)
up = concatenate((
lax.slice_in_dim(p, 0, 1, axis=axis),
lax.slice_in_dim(p, 1, None, axis=axis) + cumsum(ph_correct, axis=axis)
), axis=axis)
return up
### Array-creation functions
def _check_no_padding(axis_padding, mode):
if (axis_padding[0] > 0 or axis_padding[1] > 0):
msg = "Cannot apply '{}' padding to empty axis"
raise ValueError(msg.format(mode))
def _pad_constant(array, pad_width, constant_values):
nd = ndim(array)
constant_values = broadcast_to(asarray(constant_values), (nd, 2))
constant_values = lax._convert_element_type(constant_values, array.dtype, dtypes.is_weakly_typed(array))
for i in range(nd):
widths = [(0, 0, 0)] * nd
widths[i] = (pad_width[i, 0], 0, 0)
array = lax.pad(array, constant_values[i, 0], widths)
widths[i] = (0, pad_width[i, 1], 0)
array = lax.pad(array, constant_values[i, 1], widths)
return array
def _pad_wrap(array, pad_width):
for i in range(ndim(array)):
if array.shape[i] == 0:
_check_no_padding(pad_width[i], "wrap")
continue
size = array.shape[i]
repeats, (left_remainder, right_remainder) = _divmod(pad_width[i], size)
total_repeats = repeats.sum() + 1
parts = []
if left_remainder:
parts += [lax.slice_in_dim(array, size - left_remainder, size, axis=i)]
parts += total_repeats * [array]
if right_remainder:
parts += [lax.slice_in_dim(array, 0, right_remainder, axis=i)]
array = lax.concatenate(parts, dimension=i)
return array
def _pad_symmetric_or_reflect(array, pad_width, mode, reflect_type):
assert mode in ("symmetric", "reflect")
assert reflect_type in ("even", "odd")
for i in range(ndim(array)):
if array.shape[i] == 0:
_check_no_padding(pad_width[i], mode)
continue
n = array.shape[i]
offset = 1 if (mode == "reflect" and n > 1) else 0
def build_padding(array, padding, before):
if before:
edge = lax.slice_in_dim(array, 0, 1, axis=i)
else:
edge = lax.slice_in_dim(array, -1, None, axis=i)
while padding > 0:
curr_pad = _min(padding, n - offset)
padding -= curr_pad
if before:
start = offset
stop = offset + curr_pad
else:
start = -(curr_pad + offset)
stop = None if (mode == "symmetric" or n == 1) else -1
x = lax.slice_in_dim(array, start, stop, axis=i)
x = flip(x, axis=i)
if reflect_type == 'odd':
x = 2 * edge - x
if n > 1:
if before:
edge = lax.slice_in_dim(x, 0, 1, axis=i)
else:
edge = lax.slice_in_dim(x, -1, None, axis=i)
if before:
array = lax.concatenate([x, array], dimension=i)
else:
array = lax.concatenate([array, x], dimension=i)
return array
array = build_padding(array, pad_width[i, 0], before=True)
array = build_padding(array, pad_width[i, 1], before=False)
return array
def _pad_edge(array, pad_width):
nd = ndim(array)
for i in range(nd):
if array.shape[i] == 0:
_check_no_padding(pad_width[i], "edge")
continue
n = array.shape[i]
npad_before, npad_after = pad_width[i]
edge_before = lax.slice_in_dim(array, 0, 1, axis=i)
pad_before = repeat(edge_before, npad_before, axis=i)
edge_after = lax.slice_in_dim(array, n-1, n, axis=i)
pad_after = repeat(edge_after, npad_after, axis=i)
array = lax.concatenate([pad_before, array, pad_after], dimension=i)
return array
def _pad_linear_ramp(array, pad_width, end_values):
for axis in range(ndim(array)):
edge_before = lax.slice_in_dim(array, 0, 1, axis=axis)
edge_after = lax.slice_in_dim(array, -1, None, axis=axis)
ramp_before = linspace(
start=end_values[axis][0],
stop=edge_before.squeeze(axis), # Dimension is replaced by linspace
num=pad_width[axis][0],
endpoint=False,
dtype=array.dtype,
axis=axis
)
ramp_before = lax._convert_element_type(ramp_before, weak_type=dtypes.is_weakly_typed(array))
ramp_after = linspace(
start=end_values[axis][1],
stop=edge_after.squeeze(axis), # Dimension is replaced by linspace
num=pad_width[axis][1],
endpoint=False,
dtype=array.dtype,
axis=axis
)
ramp_after = lax._convert_element_type(ramp_after, weak_type=dtypes.is_weakly_typed(array))
# Reverse linear space in appropriate dimension
ramp_after = flip(ramp_after, axis)
array = lax.concatenate([ramp_before, array, ramp_after], dimension=axis)
return array
def _pad_stats(array, pad_width, stat_length, stat_func):
nd = ndim(array)
for i in range(nd):
if stat_length is None:
stat_before = stat_func(array, axis=i, keepdims=True)
stat_after = stat_before
else:
array_length = array.shape[i]
length_before, length_after = stat_length[i]
if length_before == 0 or length_after == 0:
raise ValueError("stat_length of 0 yields no value for padding")
# Limit stat_length to length of array.
length_before = _min(length_before, array_length)
length_after = _min(length_after, array_length)
slice_before = lax.slice_in_dim(array, 0, length_before, axis=i)
slice_after = lax.slice_in_dim(array, -length_after, None, axis=i)
stat_before = stat_func(slice_before, axis=i, keepdims=True)
stat_after = stat_func(slice_after, axis=i, keepdims=True)
if np.issubdtype(array.dtype, np.integer):
stat_before = round(stat_before)
stat_after = round(stat_after)
stat_before = lax._convert_element_type(stat_before, array.dtype, dtypes.is_weakly_typed(array))
stat_after = lax._convert_element_type(stat_after, array.dtype, dtypes.is_weakly_typed(array))
npad_before, npad_after = pad_width[i]
pad_before = repeat(stat_before, npad_before, axis=i)
pad_after = repeat(stat_after, npad_after, axis=i)
array = lax.concatenate([pad_before, array, pad_after], dimension=i)
return array
def _pad_empty(array, pad_width):
# Note: jax.numpy.empty = jax.numpy.zeros
for i in range(ndim(array)):
shape_before = array.shape[:i] + (pad_width[i][0],) + array.shape[i + 1:]
pad_before = empty_like(array, shape=shape_before)
shape_after = array.shape[:i] + (pad_width[i][1],) + array.shape[i + 1:]
pad_after = empty_like(array, shape=shape_after)
array = lax.concatenate([pad_before, array, pad_after], dimension=i)
return array
def _pad_func(array, pad_width, func, **kwargs):
pad_width = _broadcast_to_pairs(pad_width, ndim(array), "pad_width")
padded = _pad_constant(array, np.array(pad_width), 0)
for axis in range(ndim(padded)):
padded = apply_along_axis(func, axis, padded, pad_width[axis], axis, kwargs)
return padded
def _broadcast_to_pairs(nvals, nd, name):
nvals = np.asarray(tree_map(
lambda x: core.concrete_or_error(np.array, x, context=f"{name} argument of jnp.pad"),
nvals))
if nvals.dtype.kind == 'O':
raise TypeError(f'`{name}` entries must be the same shape.')
if nvals.shape == (nd, 2):
# ((before_1, after_1), ..., (before_N, after_N))
return tuple(tuple(nval) for nval in nvals)
elif nvals.shape == (1, 2):
# ((before, after),)
return tuple(tuple(nvals[0]) for i in range(nd))
elif nvals.shape == (2,):
# (before, after) (not in the numpy docstring but works anyway)
return tuple(tuple(nvals) for i in range(nd))
elif nvals.shape == (1,):
# (pad,)
return tuple((nvals[0], nvals[0]) for i in range(nd))
elif nvals.shape == ():
# pad
return tuple((nvals.flat[0], nvals.flat[0]) for i in range(nd))
else:
raise ValueError(f"jnp.pad: {name} with nd={nd} has unsupported shape {nvals.shape}. "
f"Valid shapes are ({nd}, 2), (1, 2), (2,), (1,), or ().")
@partial(jit, static_argnums=(1, 2, 4, 5, 6))
def _pad(array, pad_width, mode, constant_values, stat_length, end_values, reflect_type):
array = asarray(array)
nd = ndim(array)
if nd == 0:
return array
stat_funcs = {"maximum": amax, "minimum": amin,
"mean": mean, "median": median}
pad_width = _broadcast_to_pairs(pad_width, nd, "pad_width")
pad_width = np.array(pad_width)
assert pad_width.shape == (nd, 2), pad_width
if np.any(pad_width < 0):
raise ValueError("index can't contain negative values")
if mode == "constant":
return _pad_constant(array, pad_width, constant_values)
elif mode == "wrap":
return _pad_wrap(array, pad_width)
elif mode in ("symmetric", "reflect"):
return _pad_symmetric_or_reflect(array, pad_width, mode, reflect_type)
elif mode == "edge":
return _pad_edge(array, pad_width)
elif mode == "linear_ramp":
end_values = _broadcast_to_pairs(end_values, nd, "end_values")
return _pad_linear_ramp(array, pad_width, end_values)
elif mode in stat_funcs:
if stat_length is not None:
stat_length = _broadcast_to_pairs(stat_length, nd, "stat_length")
return _pad_stats(array, pad_width, stat_length, stat_funcs[mode])
elif mode == "empty":
return _pad_empty(array, pad_width)
else:
assert False, ("Should not be reached since pad already handled unsupported and"
"not implemented modes")
[docs]@_wraps(np.pad, lax_description="""\
Unlike numpy, JAX "function" mode's argument (which is another function) should return
the modified array. This is because Jax arrays are immutable.
(In numpy, "function" mode's argument should modify a rank 1 array in-place.)
""")
def pad(array, pad_width, mode="constant", **kwargs):
_check_arraylike("pad", array)
pad_width = _broadcast_to_pairs(pad_width, ndim(array), "pad_width")
if pad_width and np.array(pad_width).dtype.kind != 'i':
raise TypeError('`pad_width` must be of integral type.')
if callable(mode):
return _pad_func(array, pad_width, mode, **kwargs)
allowed_kwargs = {
'empty': [], 'edge': [], 'wrap': [],
'constant': ['constant_values'],
'linear_ramp': ['end_values'],
'maximum': ['stat_length'],
'mean': ['stat_length'],
'median': ['stat_length'],
'minimum': ['stat_length'],
'reflect': ['reflect_type'],
'symmetric': ['reflect_type'],
}
try:
unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
except KeyError:
msg = "Unimplemented padding mode '{}' for np.pad."
raise NotImplementedError(msg.format(mode))
if unsupported_kwargs:
raise ValueError("unsupported keyword arguments for mode '{}': {}"
.format(mode, unsupported_kwargs))
# Set default value if not given.
constant_values = kwargs.get('constant_values', 0)
stat_length = kwargs.get('stat_length', None)
end_values = kwargs.get('end_values', 0)
reflect_type = kwargs.get('reflect_type', "even")
return _pad(array, pad_width, mode, constant_values, stat_length, end_values, reflect_type)
@_wraps(np.stack, skip_params=['out'])
def stack(arrays, axis: int = 0, out=None):
if not len(arrays):
raise ValueError("Need at least one array to stack.")
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.stack is not supported.")
if isinstance(arrays, (np.ndarray, ndarray)):
axis = _canonicalize_axis(axis, arrays.ndim)
return concatenate(expand_dims(arrays, axis + 1), axis=axis)
else:
_check_arraylike("stack", *arrays)
shape0 = shape(arrays[0])
axis = _canonicalize_axis(axis, len(shape0) + 1)
new_arrays = []
for a in arrays:
if shape(a) != shape0:
raise ValueError("All input arrays must have the same shape.")
new_arrays.append(expand_dims(a, axis))
return concatenate(new_arrays, axis=axis)
@_wraps(np.tile)
def tile(A, reps):
_stackable(A) or _check_arraylike("tile", A)
try:
iter(reps)
except TypeError:
reps = (reps,)
reps = tuple(operator.index(rep) if core.is_constant_dim(rep) else rep
for rep in reps)
A_shape = (1,) * (len(reps) - ndim(A)) + shape(A)
reps = (1,) * (len(A_shape) - len(reps)) + reps
result = broadcast_to(reshape(A, [j for i in A_shape for j in [1, i]]),
[k for pair in zip(reps, A_shape) for k in pair])
return reshape(result, tuple(np.multiply(A_shape, reps)))
def _concatenate_array(arr, axis: int):
# Fast path for concatenation when the input is an ndarray rather than a list.
arr = asarray(arr)
if arr.ndim == 0 or arr.shape[0] == 0:
raise ValueError("Need at least one array to concatenate.")
if axis is None:
return lax.reshape(arr, (arr.size,))
if arr.ndim == 1:
raise ValueError("Zero-dimensional arrays cannot be concatenated.")
axis = _canonicalize_axis(axis, arr.ndim - 1)
shape = arr.shape[1:axis + 1] + (arr.shape[0] * arr.shape[axis + 1],) + arr.shape[axis + 2:]
dimensions = [*range(1, axis + 1), 0, *range(axis + 1, arr.ndim)]
return lax.reshape(arr, shape, dimensions)
@_wraps(np.concatenate)
def concatenate(arrays, axis: int = 0):
if isinstance(arrays, (np.ndarray, ndarray)):
return _concatenate_array(arrays, axis)
_stackable(*arrays) or _check_arraylike("concatenate", *arrays)
if not len(arrays):
raise ValueError("Need at least one array to concatenate.")
if ndim(arrays[0]) == 0:
raise ValueError("Zero-dimensional arrays cannot be concatenated.")
if axis is None:
return concatenate([ravel(a) for a in arrays], axis=0)
if hasattr(arrays[0], "concatenate"):
return arrays[0].concatenate(arrays[1:], axis)
axis = _canonicalize_axis(axis, ndim(arrays[0]))
arrays = _promote_dtypes(*arrays)
# lax.concatenate can be slow to compile for wide concatenations, so form a
# tree of concatenations as a workaround especially for op-by-op mode.
# (https://github.com/google/jax/issues/653).
k = 16
if len(arrays) == 1:
return asarray(arrays[0])
else:
while len(arrays) > 1:
arrays = [lax.concatenate(arrays[i:i+k], axis)
for i in range(0, len(arrays), k)]
return arrays[0]
@_wraps(np.vstack)
def vstack(tup):
if isinstance(tup, (np.ndarray, ndarray)):
arrs = jax.vmap(atleast_2d)(tup)
else:
arrs = [atleast_2d(m) for m in tup]
return concatenate(arrs, axis=0)
row_stack = vstack
@_wraps(np.hstack)
def hstack(tup):
if isinstance(tup, (np.ndarray, ndarray)):
arrs = jax.vmap(atleast_1d)(tup)
arr0_ndim = arrs.ndim - 1
else:
arrs = [atleast_1d(m) for m in tup]
arr0_ndim = arrs[0].ndim
return concatenate(arrs, axis=0 if arr0_ndim == 1 else 1)
@_wraps(np.dstack)
def dstack(tup):
if isinstance(tup, (np.ndarray, ndarray)):
arrs = jax.vmap(atleast_3d)(tup)
else:
arrs = [atleast_3d(m) for m in tup]
return concatenate(arrs, axis=2)
@_wraps(np.column_stack)
def column_stack(tup):
if isinstance(tup, (np.ndarray, ndarray)):
arrs = jax.vmap(lambda x: atleast_2d(x).T)(tup) if tup.ndim < 3 else tup
else:
arrs = [atleast_2d(arr).T if arr.ndim < 2 else arr for arr in map(asarray, tup)]
return concatenate(arrs, 1)
@_wraps(np.choose, skip_params=['out'])
def choose(a, choices, out=None, mode='raise'):
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.choose is not supported.")
_check_arraylike('choose', a, *choices)
if not issubdtype(_dtype(a), integer):
raise ValueError("`a` array must be integer typed")
N = len(choices)
if mode == 'raise':
a = core.concrete_or_error(asarray, a,
"The error occurred because jnp.choose was jit-compiled"
" with mode='raise'. Use mode='wrap' or mode='clip' instead.")
if any((a < 0) | (a >= N)):
raise ValueError("invalid entry in choice array")
elif mode == 'wrap':
a = a % N
elif mode == 'clip':
a = clip(a, 0, N - 1)
else:
raise ValueError(f"mode={mode!r} not understood. Must be 'raise', 'wrap', or 'clip'")
a, *choices = broadcast_arrays(a, *choices)
return array(choices)[(a,) + indices(a.shape, sparse=True)]
def _atleast_nd(x, n):
m = ndim(x)
return lax.broadcast(x, (1,) * (n - m)) if m < n else x
def _block(xs):
if isinstance(xs, tuple):
raise ValueError("jax.numpy.block does not allow tuples, got {}"
.format(xs))
elif isinstance(xs, list):
if len(xs) == 0:
raise ValueError("jax.numpy.block does not allow empty list arguments")
xs, depths = unzip2([_block(x) for x in xs])
if _any(d != depths[0] for d in depths[1:]):
raise ValueError("Mismatched list depths in jax.numpy.block")
rank = _max(depths[0], _max(ndim(x) for x in xs))
xs = [_atleast_nd(x, rank) for x in xs]
return concatenate(xs, axis=-depths[0]), depths[0] + 1
else:
return asarray(xs), 1
@_wraps(np.block)
@jit
def block(arrays):
out, _ = _block(arrays)
return out
@_wraps(np.atleast_1d, update_doc=False, lax_description=_ARRAY_VIEW_DOC)
@jit
def atleast_1d(*arys):
if len(arys) == 1:
arr = asarray(arys[0])
return arr if ndim(arr) >= 1 else reshape(arr, -1)
else:
return [atleast_1d(arr) for arr in arys]
@_wraps(np.atleast_2d, update_doc=False, lax_description=_ARRAY_VIEW_DOC)
@jit
def atleast_2d(*arys):
if len(arys) == 1:
arr = asarray(arys[0])
if ndim(arr) >= 2:
return arr
elif ndim(arr) == 1:
return expand_dims(arr, axis=0)
else:
return expand_dims(arr, axis=(0, 1))
else:
return [atleast_2d(arr) for arr in arys]
@_wraps(np.atleast_3d, update_doc=False, lax_description=_ARRAY_VIEW_DOC)
@jit
def atleast_3d(*arys):
if len(arys) == 1:
arr = asarray(arys[0])
if ndim(arr) == 0:
arr = expand_dims(arr, axis=(0, 1, 2))
elif ndim(arr) == 1:
arr = expand_dims(arr, axis=(0, 2))
elif ndim(arr) == 2:
arr = expand_dims(arr, axis=2)
return arr
else:
return [atleast_3d(arr) for arr in arys]
_ARRAY_DOC = """
This function will create arrays on JAX's default device. For control of the
device placement of data, see :func:`jax.device_put`. More information is
available in the JAX FAQ at :ref:`faq-data-placement` (full FAQ at
https://jax.readthedocs.io/en/latest/faq.html).
"""
@_wraps(np.array, lax_description=_ARRAY_DOC)
def array(object, dtype=None, copy=True, order="K", ndmin=0):
if order is not None and order != "K":
raise NotImplementedError("Only implemented for order='K'")
# check if the given dtype is compatible with JAX
lax._check_user_dtype_supported(dtype, "array")
# Here we make a judgment call: we only return a weakly-typed array when the
# input object itself is weakly typed. That ensures asarray(x) is a no-op whenever
# x is weak, but avoids introducing weak types with something like array([1, 2, 3])
weak_type = dtype is None and dtypes.is_weakly_typed(object)
# For Python scalar literals, call coerce_to_array to catch any overflow errors.
# We don't use dtypes.is_python_scalar because we don't want this triggering for
# traced values. We do this here because it matters whether or not dtype is None.
# We don't assign the result because we want the raw object to be used for type
# inference below.
if isinstance(object, (bool, int, float, complex)):
_ = dtypes.coerce_to_array(object, dtype)
leaves = tree_leaves(object)
if dtype is None:
# Use lattice_result_type rather than result_type to avoid canonicalization.
# Otherwise, weakly-typed inputs would have their dtypes canonicalized.
try:
dtype = dtypes._lattice_result_type(*leaves)[0] if leaves else dtypes.float_
except TypeError:
# This happens if, e.g. one of the entries is a memoryview object.
# This is rare, so we only handle it if the normal path fails.
leaves = [_convert_to_array_if_dtype_fails(leaf) for leaf in leaves]
dtype = dtypes._lattice_result_type(*leaves)[0]
if not weak_type:
dtype = dtypes.canonicalize_dtype(dtype)
# We can't use the ndarray class because we need to handle internal buffers
# (See https://github.com/google/jax/issues/8950)
ndarray_types = (device_array.DeviceArray, core.Tracer)
if not _any(isinstance(leaf, ndarray_types) for leaf in leaves):
# TODO(jakevdp): falling back to numpy here fails to overflow for lists containing
# large integers; see discussion in https://github.com/google/jax/pull/6047.
# More correct would be to call coerce_to_array on each leaf, but this may have
# performance implications.
out = np.array(object, dtype=dtype, ndmin=ndmin, copy=False)
elif isinstance(object, ndarray_types):
if object.aval is None:
# object is a raw buffer; convert to device array on its current device.
aval = ShapedArray(object.xla_shape().dimensions(), object.dtype,
weak_type=bool(getattr(object, "weak_type", False)))
object = device_array.make_device_array(aval, object.device(), object)
out = _array_copy(object) if copy else object
elif isinstance(object, (list, tuple)):
if object:
out = stack([asarray(elt, dtype=dtype) for elt in object])
else:
out = np.array([], dtype=dtype)
else:
try:
view = memoryview(object)
except TypeError:
pass # `object` does not support the buffer interface.
else:
return array(np.asarray(view), dtype, copy, ndmin=ndmin)
raise TypeError("Unexpected input type for array: {}".format(type(object)))
out = lax._convert_element_type(out, dtype, weak_type=weak_type)
if ndmin > ndim(out):
out = lax.expand_dims(out, range(ndmin - ndim(out)))
return out
def _convert_to_array_if_dtype_fails(x):
try:
dtypes.dtype(x)
except TypeError:
return np.asarray(x)
else:
return x
@_wraps(np.asarray, lax_description=_ARRAY_DOC)
def asarray(a, dtype=None, order=None):
lax._check_user_dtype_supported(dtype, "asarray")
dtype = dtypes.canonicalize_dtype(dtype) if dtype is not None else dtype
return array(a, dtype=dtype, copy=False, order=order)
@_wraps(np.zeros_like)
def zeros_like(a, dtype=None, shape=None):
_check_arraylike("zeros_like", a)
lax._check_user_dtype_supported(dtype, "zeros_like")
if np.isscalar(shape):
shape = (shape,)
return lax.full_like(a, 0, dtype, shape)
@_wraps(np.ones_like)
def ones_like(a, dtype=None, shape=None):
_check_arraylike("ones_like", a)
lax._check_user_dtype_supported(dtype, "ones_like")
if np.isscalar(shape):
shape = (shape,)
return lax.full_like(a, 1, dtype, shape)
@_wraps(np.full)
def full(shape, fill_value, dtype=None):
lax._check_user_dtype_supported(dtype, "full")
_check_arraylike("full", fill_value)
if ndim(fill_value) == 0:
shape = (shape,) if ndim(shape) == 0 else shape
return lax.full(shape, fill_value, dtype)
else:
return broadcast_to(asarray(fill_value, dtype=dtype), shape)
@_wraps(np.full_like)
def full_like(a, fill_value, dtype=None, shape=None):
lax._check_user_dtype_supported(dtype, "full_like")
_check_arraylike("full_like", a, fill_value)
if shape is not None:
shape = (shape,) if ndim(shape) == 0 else shape
if ndim(fill_value) == 0:
return lax.full_like(a, fill_value, dtype, shape)
else:
shape = np.shape(a) if shape is None else shape
dtype = result_type(a) if dtype is None else dtype
return broadcast_to(asarray(fill_value, dtype=dtype), shape)
@_wraps(np.zeros)
def zeros(shape, dtype=None):
if isinstance(shape, types.GeneratorType):
raise TypeError("expected sequence object with len >= 0 or a single integer")
lax._check_user_dtype_supported(dtype, "zeros")
shape = canonicalize_shape((shape,) if ndim(shape) == 0 else shape)
return lax.full(shape, 0, _jnp_dtype(dtype))
@_wraps(np.ones)
def ones(shape, dtype=None):
if isinstance(shape, types.GeneratorType):
raise TypeError("expected sequence object with len >= 0 or a single integer")
lax._check_user_dtype_supported(dtype, "ones")
shape = canonicalize_shape((shape,) if ndim(shape) == 0 else shape)
return lax.full(shape, 1, _jnp_dtype(dtype))
@_wraps(np.array_equal)
def array_equal(a1, a2, equal_nan=False):
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
if shape(a1) != shape(a2):
return False
eq = asarray(a1 == a2)
if equal_nan:
eq = logical_or(eq, logical_and(isnan(a1), isnan(a2)))
return all(eq)
@_wraps(np.array_equiv)
def array_equiv(a1, a2):
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
try:
eq = equal(a1, a2)
except ValueError:
# shapes are not broadcastable
return False
return all(eq)
# We can't create uninitialized arrays in XLA; use zeros for empty.
empty_like = zeros_like
empty = zeros
@_wraps(np.eye)
def eye(N, M=None, k=0, dtype=None):
lax._check_user_dtype_supported(dtype, "eye")
N = core.canonicalize_dim(N, "'N' argument of jnp.eye()")
M = N if M is None else core.canonicalize_dim(M, "'M' argument of jnp.eye()")
if N < 0 or M < 0:
raise ValueError(f"negative dimensions are not allowed, got {N} and {M}")
k = operator.index(k)
return lax._eye(_jnp_dtype(dtype), (N, M), k)
@_wraps(np.identity)
def identity(n, dtype=None):
lax._check_user_dtype_supported(dtype, "identity")
return eye(n, dtype=dtype)
@_wraps(np.arange)
def arange(start: core.DimSize, stop: Optional[core.DimSize]=None,
step: Optional[core.DimSize]=None, dtype=None):
lax._check_user_dtype_supported(dtype, "arange")
require = partial(core.concrete_or_error, None)
msg = "It arose in jax.numpy.arange argument `{}`.".format
if _any(core.is_special_dim_size(d) for d in (start, stop, step)):
if stop is not None or step is not None:
raise ValueError(
"jax.numpy.arange supports non-constant arguments only in single-argument form. "
f"Found jax.numpy.arange(start={start}, stop={stop}, step={step})")
return lax.iota(int_, start)
if dtype is None:
dtype = result_type(start, *(x for x in [stop, step] if x is not None))
dtype = _jnp_dtype(dtype)
if stop is None and step is None:
start = require(start, msg("stop"))
start = np.ceil(start).astype(int)
return lax.iota(dtype, start)
else:
start = require(start, msg("start"))
stop = None if stop is None else require(stop, msg("stop"))
step = None if step is None else require(step, msg("step"))
return array(np.arange(start, stop=stop, step=step, dtype=dtype))
def _wrap_numpy_nullary_function(f):
"""Adapts `f` to return a DeviceArray instead of an np.ndarray.
`f` cannot have any non-static array arguments.
"""
@_wraps(f, update_doc=False)
def wrapper(*args, **kwargs):
args = [core.concrete_or_error(None, arg, f"the error occured in argument {i} jnp.{f.__name__}()")
for i, arg in enumerate(args)]
kwargs = {key: core.concrete_or_error(None, val, f"the error occured in argument '{key}' jnp.{f.__name__}()")
for key, val in kwargs.items()}
return asarray(f(*args, **kwargs))
return wrapper
@_wraps(np.linspace)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis: int = 0):
num = core.concrete_or_error(operator.index, num, "'num' argument of jnp.linspace")
axis = core.concrete_or_error(operator.index, axis, "'axis' argument of jnp.linspace")
return _linspace(start, stop, int(num), endpoint, retstep, dtype,
operator.index(axis))
@partial(jit, static_argnames=('num', 'endpoint', 'retstep', 'dtype', 'axis'))
def _linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
axis: int = 0):
"""Implementation of linspace differentiable in start and stop args."""
lax._check_user_dtype_supported(dtype, "linspace")
if num < 0:
raise ValueError(f"Number of samples, {num}, must be non-negative.")
_check_arraylike("linspace", start, stop)
if dtype is None:
dtype = result_type(start, stop, dtypes.canonicalize_dtype(float_))
dtype = _jnp_dtype(dtype)
computation_dtype = promote_types(dtype, dtypes.canonicalize_dtype(float_))
start = asarray(start, dtype=computation_dtype)
stop = asarray(stop, dtype=computation_dtype)
bounds_shape = list(lax.broadcast_shapes(shape(start), shape(stop)))
broadcast_start = broadcast_to(start, bounds_shape)
broadcast_stop = broadcast_to(stop, bounds_shape)
axis = len(bounds_shape) + axis + 1 if axis < 0 else axis
bounds_shape.insert(axis, 1)
div = (num - 1) if endpoint else num
if num > 1:
delta = lax.convert_element_type(stop - start, computation_dtype) / div
iota_shape = [1,] * len(bounds_shape)
iota_shape[axis] = div
# This approach recovers the endpoints with float32 arithmetic,
# but can lead to rounding errors for integer outputs.
real_dtype = finfo(computation_dtype).dtype
step = reshape(lax.iota(real_dtype, div), iota_shape) / div
out = (reshape(broadcast_start, bounds_shape) * (1 - step) +
reshape(broadcast_stop, bounds_shape) * step)
if endpoint:
out = lax.concatenate([out, lax.expand_dims(broadcast_stop, (axis,))],
_canonicalize_axis(axis, out.ndim))
elif num == 1:
delta = nan if endpoint else stop - start
out = reshape(broadcast_start, bounds_shape)
else: # num == 0 degenerate case, match numpy behavior
empty_shape = list(lax.broadcast_shapes(shape(start), shape(stop)))
empty_shape.insert(axis, 0)
delta = nan
out = reshape(array([], dtype=dtype), empty_shape)
if issubdtype(dtype, integer) and not issubdtype(out.dtype, integer):
out = lax.floor(out)
if retstep:
return lax.convert_element_type(out, dtype), delta
else:
return lax.convert_element_type(out, dtype)
@_wraps(np.logspace)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis: int = 0):
num = core.concrete_or_error(operator.index, num, "'num' argument of jnp.logspace")
axis = core.concrete_or_error(operator.index, axis, "'axis' argument of jnp.logspace")
return _logspace(start, stop, int(num), endpoint, base, dtype,
operator.index(axis))
@partial(jit, static_argnames=('num', 'endpoint', 'dtype', 'axis'))
def _logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
axis: int = 0):
"""Implementation of logspace differentiable in start and stop args."""
lax._check_user_dtype_supported(dtype, "logspace")
if dtype is None:
dtype = result_type(start, stop, dtypes.canonicalize_dtype(float_))
dtype = _jnp_dtype(dtype)
computation_dtype = promote_types(dtype, dtypes.canonicalize_dtype(float_))
_check_arraylike("logspace", start, stop)
start = asarray(start, dtype=computation_dtype)
stop = asarray(stop, dtype=computation_dtype)
lin = linspace(start, stop, num,
endpoint=endpoint, retstep=False, dtype=None, axis=axis)
return lax.convert_element_type(power(base, lin), dtype)
@_wraps(np.geomspace)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis: int = 0):
num = core.concrete_or_error(operator.index, num, "'num' argument of jnp.geomspace")
axis = core.concrete_or_error(operator.index, axis, "'axis' argument of jnp.geomspace")
return _geomspace(start, stop, int(num), endpoint, dtype,
operator.index(axis))
@partial(jit, static_argnames=('num', 'endpoint', 'dtype', 'axis'))
def _geomspace(start, stop, num=50, endpoint=True, dtype=None, axis: int = 0):
"""Implementation of geomspace differentiable in start and stop args."""
lax._check_user_dtype_supported(dtype, "geomspace")
if dtype is None:
dtype = result_type(start, stop, dtypes.canonicalize_dtype(float_))
dtype = _jnp_dtype(dtype)
computation_dtype = promote_types(dtype, dtypes.canonicalize_dtype(float_))
_check_arraylike("geomspace", start, stop)
start = asarray(start, dtype=computation_dtype)
stop = asarray(stop, dtype=computation_dtype)
# follow the numpy geomspace convention for negative and complex endpoints
signflip = 1 - (1 - sign(real(start))) * (1 - sign(real(stop))) // 2
res = signflip * logspace(log10(signflip * start),
log10(signflip * stop), num,
endpoint=endpoint, base=10.0,
dtype=computation_dtype, axis=0)
if axis != 0:
res = moveaxis(res, 0, axis)
return lax.convert_element_type(res, dtype)
@_wraps(np.meshgrid, lax_description=_ARRAY_VIEW_DOC)
def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
_check_arraylike("meshgrid", *xi)
args = [asarray(x) for x in xi]
if not copy:
raise ValueError("jax.numpy.meshgrid only supports copy=True")
if indexing not in ["xy", "ij"]:
raise ValueError(f"Valid values for indexing are 'xy' and 'ij', got {indexing}")
if _any(a.ndim != 1 for a in args):
raise ValueError("Arguments to jax.numpy.meshgrid must be 1D, got shapes "
f"{[a.shape for a in args]}")
if indexing == "xy" and len(args) >= 2:
args[0], args[1] = args[1], args[0]
shape = [1 if sparse else a.shape[0] for a in args]
_a_shape = lambda i, a: [*shape[:i], a.shape[0], *shape[i + 1:]] if sparse else shape
output = [lax.broadcast_in_dim(a, _a_shape(i, a), (i,)) for i, a, in enumerate(args)]
if indexing == "xy" and len(args) >= 2:
output[0], output[1] = output[1], output[0]
return output
def _make_1d_grid_from_slice(s: slice, op_name: str):
start = core.concrete_or_error(None, s.start,
f"slice start of jnp.{op_name}") or 0
stop = core.concrete_or_error(None, s.stop,
f"slice stop of jnp.{op_name}")
step = core.concrete_or_error(None, s.step,
f"slice step of jnp.{op_name}") or 1
if np.iscomplex(step):
newobj = linspace(start, stop, int(_abs(step)))
else:
newobj = arange(start, stop, step)
return newobj
class _IndexGrid:
def __getitem__(self, key):
single_slice = isinstance(key, slice)
if single_slice:
key = (key,)
output = []
for k in key:
output.append(_make_1d_grid_from_slice(k, op_name=self.op_name))
if single_slice:
return output[0]
output = meshgrid(*output, indexing='ij', sparse=self.sparse)
return output if self.sparse else stack(output, 0)
class _Mgrid(_IndexGrid):
"""Return dense multi-dimensional "meshgrid".
LAX-backend implementation of :obj:`numpy.mgrid`. This is a convenience wrapper for
functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=False``.
See Also:
jnp.ogrid: open/sparse version of jnp.mgrid
Examples:
Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`:
>>> jnp.mgrid[0:4:1]
DeviceArray([0, 1, 2, 3], dtype=int32)
Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`:
>>> jnp.mgrid[0:1:4j]
DeviceArray([0. , 0.33333334, 0.6666667 , 1. ], dtype=float32)
Multiple slices can be used to create broadcasted grids of indices:
>>> jnp.mgrid[:2, :3]
DeviceArray([[[0, 0, 0],
[1, 1, 1]],
[[0, 1, 2],
[0, 1, 2]]], dtype=int32)
"""
sparse = False
op_name = "mgrid"
mgrid = _Mgrid()
class _Ogrid(_IndexGrid):
"""Return open multi-dimensional "meshgrid".
LAX-backend implementation of :obj:`numpy.ogrid`. This is a convenience wrapper for
functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=True``.
See Also:
jnp.mgrid: dense version of jnp.ogrid
Examples:
Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`:
>>> jnp.ogrid[0:4:1]
DeviceArray([0, 1, 2, 3], dtype=int32)
Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`:
>>> jnp.ogrid[0:1:4j]
DeviceArray([0. , 0.33333334, 0.6666667 , 1. ], dtype=float32)
Multiple slices can be used to create sparse grids of indices:
>>> jnp.ogrid[:2, :3]
[DeviceArray([[0],
[1]], dtype=int32),
DeviceArray([[0, 1, 2]], dtype=int32)]
"""
sparse = True
op_name = "ogrid"
ogrid = _Ogrid()
class _AxisConcat:
"""Concatenates slices, scalars and array-like objects along a given axis."""
def __getitem__(self, key):
if not isinstance(key, tuple):
key = (key,)
params = [self.axis, self.ndmin, self.trans1d, -1]
if isinstance(key[0], str):
# split off the directive
directive, *key = key
# check two special cases: matrix directives
if directive == "r":
params[-1] = 0
elif directive == "c":
params[-1] = 1
else:
vec = directive.split(",")
k = len(vec)
if k < 4:
vec += params[k:]
else:
# ignore everything after the first three comma-separated ints
vec = vec[:3] + params[-1]
try:
params = list(map(int, vec))
except ValueError as err:
raise ValueError(
"could not understand directive {!r}".format(directive)
) from err
axis, ndmin, trans1d, matrix = params
output = []
for item in key:
if isinstance(item, slice):
newobj = _make_1d_grid_from_slice(item, op_name=self.op_name)
elif isinstance(item, str):
raise ValueError("string directive must be placed at the beginning")
else:
newobj = item
newobj = array(newobj, copy=False, ndmin=ndmin)
if trans1d != -1 and ndmin - ndim(item) > 0:
shape_obj = list(range(ndmin))
# Calculate number of left shifts, with overflow protection by mod
num_lshifts = ndmin - _abs(ndmin + trans1d + 1) % ndmin
shape_obj = tuple(shape_obj[num_lshifts:] + shape_obj[:num_lshifts])
newobj = transpose(newobj, shape_obj)
output.append(newobj)
res = concatenate(tuple(output), axis=axis)
if matrix != -1 and res.ndim == 1:
# insert 2nd dim at axis 0 or 1
res = expand_dims(res, matrix)
return res
def __len__(self):
return 0
class RClass(_AxisConcat):
"""Concatenate slices, scalars and array-like objects along the first axis.
LAX-backend implementation of :obj:`numpy.r_`.
See Also:
``jnp.c_``: Concatenates slices, scalars and array-like objects along the last axis.
Examples:
Passing slices in the form ``[start:stop:step]`` generates ``jnp.arange`` objects:
>>> jnp.r_[-1:5:1, 0, 0, jnp.array([1,2,3])]
DeviceArray([-1, 0, 1, 2, 3, 4, 0, 0, 1, 2, 3], dtype=int32)
An imaginary value for ``step`` will create a ``jnp.linspace`` object instead,
which includes the right endpoint:
>>> jnp.r_[-1:1:6j, 0, jnp.array([1,2,3])]
DeviceArray([-1. , -0.6 , -0.20000002, 0.20000005,
0.6 , 1. , 0. , 1. ,
2. , 3. ], dtype=float32)
Use a string directive of the form ``"axis,dims,trans1d"`` as the first argument to
specify concatenation axis, minimum number of dimensions, and the position of the
upgraded array's original dimensions in the resulting array's shape tuple:
>>> jnp.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, 2D output
DeviceArray([[1, 2, 3],
[4, 5, 6]], dtype=int32)
>>> jnp.r_['0,2,0', [1,2,3], [4,5,6]] # push last input axis to the front
DeviceArray([[1],
[2],
[3],
[4],
[5],
[6]], dtype=int32)
Negative values for ``trans1d`` offset the last axis towards the start
of the shape tuple:
>>> jnp.r_['0,2,-2', [1,2,3], [4,5,6]]
DeviceArray([[1],
[2],
[3],
[4],
[5],
[6]], dtype=int32)
Use the special directives ``"r"`` or ``"c"`` as the first argument on flat inputs
to create an array with an extra row or column axis, respectively:
>>> jnp.r_['r',[1,2,3], [4,5,6]]
DeviceArray([[1, 2, 3, 4, 5, 6]], dtype=int32)
>>> jnp.r_['c',[1,2,3], [4,5,6]]
DeviceArray([[1],
[2],
[3],
[4],
[5],
[6]], dtype=int32)
For higher-dimensional inputs (``dim >= 2``), both directives ``"r"`` and ``"c"``
give the same result.
"""
axis = 0
ndmin = 1
trans1d = -1
op_name = "r_"
r_ = RClass()
class CClass(_AxisConcat):
"""Concatenate slices, scalars and array-like objects along the last axis.
LAX-backend implementation of :obj:`numpy.c_`.
See Also:
``jnp.r_``: Concatenates slices, scalars and array-like objects along the first axis.
Examples:
>>> a = jnp.arange(6).reshape((2,3))
>>> jnp.c_[a,a]
DeviceArray([[0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5]], dtype=int32)
Use a string directive of the form ``"axis:dims:trans1d"`` as the first argument to specify
concatenation axis, minimum number of dimensions, and the position of the upgraded array's
original dimensions in the resulting array's shape tuple:
>>> jnp.c_['0,2', [1,2,3], [4,5,6]]
DeviceArray([[1],
[2],
[3],
[4],
[5],
[6]], dtype=int32)
>>> jnp.c_['0,2,-1', [1,2,3], [4,5,6]]
DeviceArray([[1, 2, 3],
[4, 5, 6]], dtype=int32)
Use the special directives ``"r"`` or ``"c"`` as the first argument on flat inputs
to create an array with inputs stacked along the last axis:
>>> jnp.c_['r',[1,2,3], [4,5,6]]
DeviceArray([[1, 4],
[2, 5],
[3, 6]], dtype=int32)
"""
axis = -1
ndmin = 2
trans1d = 0
op_name = "c_"
c_ = CClass()
s_ = np.s_
index_exp = np.index_exp
@_wraps(np.i0)
@jit
def i0(x):
x_orig = x
x, = _promote_args_inexact("i0", x)
if not issubdtype(x.dtype, np.floating):
raise ValueError(f"Unsupported input type to jax.numpy.i0: {_dtype(x_orig)}")
x = lax.abs(x)
return lax.mul(lax.exp(x), lax.bessel_i0e(x))
@_wraps(np.ix_)
def ix_(*args):
_check_arraylike("ix", *args)
n = len(args)
output = []
for i, a in enumerate(args):
a = asarray(a)
if len(a.shape) != 1:
msg = "Arguments to jax.numpy.ix_ must be 1-dimensional, got shape {}"
raise ValueError(msg.format(a.shape))
if _dtype(a) == bool_:
raise NotImplementedError(
"Boolean arguments to jax.numpy.ix_ are not implemented")
shape = [1] * n
shape[i] = a.shape[0]
if a.size == 0:
# Numpy uses an integer index type for empty arrays.
output.append(lax.full(shape, np.zeros((), np.intp)))
else:
output.append(lax.broadcast_in_dim(a, shape, (i,)))
return tuple(output)
@_wraps(np.indices)
def indices(dimensions, dtype=int32, sparse=False):
dimensions = tuple(
core.concrete_or_error(int, d, "dimensions argument of jnp.indices")
for d in dimensions)
N = len(dimensions)
output = []
s = dimensions
for i, dim in enumerate(dimensions):
idx = lax.iota(dtype, dim)
if sparse:
s = (1,)*i + (dim,) + (1,)*(N - i - 1)
output.append(lax.broadcast_in_dim(idx, s, (i,)))
if sparse:
return tuple(output)
return stack(output, 0) if output else array([], dtype=dtype)
_TOTAL_REPEAT_LENGTH_DOC = """\
Jax adds the optional `total_repeat_length` parameter which specifies the total
number of repeat, and defaults to sum(repeats). It must be specified for repeat
to be compilable. If `sum(repeats)` is larger than the specified
`total_repeat_length` the remaining values will be discarded. In the case of
`sum(repeats)` being smaller than the specified target length, the final value
will be repeated.
"""
@_wraps(np.repeat, lax_description=_TOTAL_REPEAT_LENGTH_DOC)
def repeat(a, repeats, axis: Optional[int] = None, *, total_repeat_length=None):
_check_arraylike("repeat", a, repeats)
if axis is None:
a = ravel(a)
axis = 0
axis = core.concrete_or_error(operator.index, axis, "'axis' argument of jnp.repeat()")
assert isinstance(axis, int) # to appease mypy
# If total_repeat_length is not given, can't compile, use a default.
if total_repeat_length is None:
repeats = core.concrete_or_error(np.array, repeats,
"When jit-compiling jnp.repeat, the total number of repeats must be static. "
"To fix this, either specify a static value for `repeats`, or pass a static "
"value to `total_repeat_length`.")
# Fast path for when repeats is a scalar.
if np.ndim(repeats) == 0 and ndim(a) != 0:
input_shape = a.shape
aux_axis = axis if axis < 0 else axis + 1
a = expand_dims(a, aux_axis)
reps = [1] * len(a.shape)
reps[aux_axis] = repeats
a = tile(a, reps)
result_shape = list(input_shape)
result_shape[axis] *= repeats
return reshape(a, result_shape)
repeats = np.ravel(repeats)
if ndim(a) != 0:
repeats = np.broadcast_to(repeats, [a.shape[axis]])
total_repeat_length = np.sum(repeats)
else:
repeats = ravel(repeats)
if ndim(a) != 0:
repeats = broadcast_to(repeats, [a.shape[axis]])
# Special case when a is a scalar.
if ndim(a) == 0:
if repeats.shape == (1,):
return full([total_repeat_length], a)
else:
raise ValueError('`repeat` with a scalar parameter `a` is only '
'implemented for scalar values of the parameter `repeats`.')
# Special case if total_repeat_length is zero.
if total_repeat_length == 0:
result_shape = list(a.shape)
result_shape[axis] = 0
return reshape(array([], dtype=a.dtype), result_shape)
# If repeats is on a zero sized axis, then return the array.
if a.shape[axis] == 0:
return a
# This implementation of repeat avoid having to instantiate a large.
#Â intermediate tensor.
# Modify repeats from e.g. [1,2,0,5] -> [0,1,2,0] for exclusive repeat.
exclusive_repeats = roll(repeats, shift=1).at[0].set(0)
# Cumsum to get indices of new number in repeated tensor, e.g. [0, 1, 3, 3]
scatter_indices = cumsum(exclusive_repeats)
# Scatter these onto a zero buffer, e.g. [1,1,0,2,0,0,0,0]
block_split_indicators = zeros([total_repeat_length], dtype=int32)
block_split_indicators = block_split_indicators.at[scatter_indices].add(1)
# Cumsum again to get scatter indices for repeat, e.g. [0,1,1,3,3,3,3,3]
gather_indices = cumsum(block_split_indicators) - 1
return take(a, gather_indices, axis=axis)
@_wraps(np.tri)
def tri(N, M=None, k=0, dtype=None):
lax._check_user_dtype_supported(dtype, "tri")
M = M if M is not None else N
dtype = dtype or float32
return lax._tri(dtype, (N, M), k)
@_wraps(np.tril)
@partial(jit, static_argnames=('k',))
def tril(m, k=0):
_check_arraylike("tril", m)
m_shape = shape(m)
if len(m_shape) < 2:
raise ValueError("Argument to jax.numpy.tril must be at least 2D")
mask = tri(*m_shape[-2:], k=k, dtype=bool)
return lax.select(lax.broadcast(mask, m_shape[:-2]), m, zeros_like(m))
@_wraps(np.triu, update_doc=False)
@partial(jit, static_argnames=('k',))
def triu(m, k=0):
_check_arraylike("triu", m)
m_shape = shape(m)
if len(m_shape) < 2:
raise ValueError("Argument to jax.numpy.triu must be at least 2D")
mask = tri(*m_shape[-2:], k=k - 1, dtype=bool)
return lax.select(lax.broadcast(mask, m_shape[:-2]), zeros_like(m), m)
@_wraps(np.trace, skip_params=['out'])
@partial(jit, static_argnames=('offset', 'axis1', 'axis2', 'dtype'))
def trace(a, offset=0, axis1: int = 0, axis2: int = 1, dtype=None, out=None):
_check_arraylike("trace", a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.trace is not supported.")
lax._check_user_dtype_supported(dtype, "trace")
axis1 = _canonicalize_axis(axis1, ndim(a))
axis2 = _canonicalize_axis(axis2, ndim(a))
a_shape = shape(a)
if dtype is None:
dtype = _dtype(a)
if issubdtype(dtype, integer):
default_int = dtypes.canonicalize_dtype(np.int_)
if iinfo(dtype).bits < iinfo(default_int).bits:
dtype = default_int
# Move the axis? dimensions to the end.
perm = [i for i in range(len(a_shape)) if i != axis1 and i != axis2]
perm = perm + [axis1, axis2]
a = lax.transpose(a, perm)
# Mask out the diagonal and reduce.
a = where(eye(a_shape[axis1], a_shape[axis2], k=offset, dtype=bool),
a, zeros_like(a))
return sum(a, axis=(-2, -1), dtype=dtype)
def _wrap_indices_function(f):
@_wraps(f, update_doc=False)
def wrapper(*args, **kwargs):
args = [core.concrete_or_error(
None, arg, f"argument {i} of jnp.{f.__name__}()")
for i, arg in enumerate(args)]
kwargs = {key: core.concrete_or_error(
None, val, f"argument '{key}' of jnp.{f.__name__}()")
for key, val in kwargs.items()}
return tuple(asarray(x) for x in f(*args, **kwargs))
return wrapper
tril_indices = _wrap_indices_function(np.tril_indices)
triu_indices = _wrap_indices_function(np.triu_indices)
mask_indices = _wrap_indices_function(np.mask_indices)
@_wraps(np.triu_indices_from)
def triu_indices_from(arr, k=0):
return triu_indices(arr.shape[-2], k=k, m=arr.shape[-1])
@_wraps(np.tril_indices_from)
def tril_indices_from(arr, k=0):
return tril_indices(arr.shape[-2], k=k, m=arr.shape[-1])
@_wraps(np.diag_indices)
def diag_indices(n, ndim=2):
n = core.concrete_or_error(operator.index, n, "'n' argument of jnp.diag_indices()")
ndim = core.concrete_or_error(operator.index, ndim, "'ndim' argument of jnp.diag_indices()")
if n < 0:
raise ValueError("n argument to diag_indices must be nonnegative, got {}"
.format(n))
if ndim < 0:
raise ValueError("ndim argument to diag_indices must be nonnegative, got {}"
.format(ndim))
return (lax.iota(int_, n),) * ndim
@_wraps(np.diag_indices_from)
def diag_indices_from(arr):
_check_arraylike("diag_indices_from", arr)
if not arr.ndim >= 2:
raise ValueError("input array must be at least 2-d")
if len(set(arr.shape)) != 1:
raise ValueError("All dimensions of input must be of equal length")
return diag_indices(arr.shape[0], ndim=arr.ndim)
@_wraps(np.diagonal, lax_description=_ARRAY_VIEW_DOC)
@partial(jit, static_argnames=('offset', 'axis1', 'axis2'))
def diagonal(a, offset=0, axis1: int = 0, axis2: int = 1):
_check_arraylike("diagonal", a)
a_shape = shape(a)
a_ndims = len(a_shape)
offset = core.concrete_or_error(operator.index, offset, "'offset' argument of jnp.diagonal()")
# Move the two dimensions to the end.
axis1 = _canonicalize_axis(axis1, a_ndims)
axis2 = _canonicalize_axis(axis2, a_ndims)
perm = [i for i in range(a_ndims) if i != axis1 and i != axis2]
perm = perm + [axis1, axis2]
a = lax.transpose(a, perm)
# Mask out the diagonal and reduce over one of the axes
a = where(eye(a_shape[axis1], a_shape[axis2], k=offset, dtype=bool),
a, zeros_like(a))
reduce_axis = -2 if offset < 0 else -1
d = sum(a, axis=reduce_axis, dtype=_dtype(a))
# Slice out the correct diagonal size.
diag_size = _max(0, _min(a_shape[axis1] + _min(offset, 0),
a_shape[axis2] - _max(offset, 0)))
return lax.slice_in_dim(d, 0, diag_size, axis=-1)
@_wraps(np.diag, lax_description=_ARRAY_VIEW_DOC)
def diag(v, k=0):
return _diag(v, int(k))
@partial(jit, static_argnames=('k',))
def _diag(v, k):
_check_arraylike("diag", v)
v_shape = shape(v)
if len(v_shape) == 1:
zero = lambda x: lax.full_like(x, shape=(), fill_value=0)
n = v_shape[0] + _abs(k)
v = lax.pad(v, zero(v), ((_max(0, k), _max(0, -k), 0),))
return where(eye(n, k=k, dtype=bool), v, zeros_like(v))
elif len(v_shape) == 2:
return diagonal(v, offset=k)
else:
raise ValueError("diag input must be 1d or 2d")
_SCALAR_VALUE_DOC = """\
This differs from np.diagflat for some scalar values of v,
jax always returns a two-dimensional array, whereas numpy may
return a scalar depending on the type of v.
"""
@_wraps(np.diagflat, lax_description=_SCALAR_VALUE_DOC)
def diagflat(v, k=0):
_check_arraylike("diagflat", v)
v = ravel(v)
v_length = len(v)
adj_length = v_length + _abs(k)
res = zeros(adj_length*adj_length, dtype=v.dtype)
i = arange(0, adj_length-_abs(k))
if (k >= 0):
fi = i+k+i*adj_length
else:
fi = i+(i-k)*adj_length
res = res.at[fi].set(v)
res = res.reshape(adj_length, adj_length)
return res
_POLY_DOC = """\
This differs from np.poly when an integer array is given.
np.poly returns a result with dtype float64 in this case.
jax returns a result with an inexact type, but not necessarily
float64.
This also differs from np.poly when the input array strictly
contains pairs of complex conjugates, e.g. [1j, -1j, 1-1j, 1+1j].
np.poly returns an array with a real dtype in such cases.
jax returns an array with a complex dtype in such cases.
"""
@_wraps(np.poly, lax_description=_POLY_DOC)
@jit
def poly(seq_of_zeros):
_check_arraylike('poly', seq_of_zeros)
seq_of_zeros, = _promote_dtypes_inexact(seq_of_zeros)
seq_of_zeros = atleast_1d(seq_of_zeros)
sh = seq_of_zeros.shape
if len(sh) == 2 and sh[0] == sh[1] and sh[0] != 0:
# import at runtime to avoid circular import
from jax._src.numpy import linalg
seq_of_zeros = linalg.eigvals(seq_of_zeros)
if seq_of_zeros.ndim != 1:
raise ValueError("input must be 1d or non-empty square 2d array.")
dt = seq_of_zeros.dtype
if len(seq_of_zeros) == 0:
return ones((), dtype=dt)
a = ones((1,), dtype=dt)
for k in range(len(seq_of_zeros)):
a = convolve(a, array([1, -seq_of_zeros[k]], dtype=dt), mode='full')
return a
@_wraps(np.polyval, lax_description="""\
The ``unroll`` parameter is JAX specific. It does not effect correctness but can
have a major impact on performance for evaluating high-order polynomials. The
parameter controls the number of unrolled steps with ``lax.scan`` inside the
``polyval`` implementation. Consider setting ``unroll=128`` (or even higher) to
improve runtime performance on accelerators, at the cost of increased
compilation time.
""")
@partial(jax.jit, static_argnames=['unroll'])
def polyval(p, x, *, unroll=16):
_check_arraylike("polyval", p, x)
p, x = _promote_dtypes_inexact(p, x)
shape = lax.broadcast_shapes(p.shape[1:], x.shape)
y = lax.full_like(x, 0, shape=shape, dtype=x.dtype)
y, _ = lax.scan(lambda y, p: (y * x + p, None), y, p, unroll=unroll)
return y
@_wraps(np.polyadd)
@jit
def polyadd(a1, a2):
_check_arraylike("polyadd", a1, a2)
a1, a2 = _promote_dtypes(a1, a2)
if a2.shape[0] <= a1.shape[0]:
return a1.at[-a2.shape[0]:].add(a2)
else:
return a2.at[-a1.shape[0]:].add(a1)
@_wraps(np.polyint)
@partial(jit, static_argnames=('m',))
def polyint(p, m=1, k=None):
m = core.concrete_or_error(operator.index, m, "'m' argument of jnp.polyint")
k = 0 if k is None else k
_check_arraylike("polyint", p, k)
p, k = _promote_dtypes_inexact(p, k)
if m < 0:
raise ValueError("Order of integral must be positive (see polyder)")
k = atleast_1d(k)
if len(k) == 1:
k = full((m,), k[0])
if k.shape != (m,):
raise ValueError("k must be a scalar or a rank-1 array of length 1 or m.")
if m == 0:
return p
else:
coeff = maximum(1, arange(len(p) + m, 0, -1)[newaxis, :] - 1 - arange(m)[:, newaxis]).prod(0)
return true_divide(concatenate((p, k)), coeff)
@_wraps(np.polyder)
@partial(jit, static_argnames=('m',))
def polyder(p, m=1):
_check_arraylike("polyder", p)
m = core.concrete_or_error(operator.index, m, "'m' argument of jnp.polyder")
p, = _promote_dtypes_inexact(p)
if m < 0:
raise ValueError("Order of derivative must be positive")
if m == 0:
return p
coeff = (arange(len(p), m, -1)[newaxis, :] - 1 - arange(m)[:, newaxis]).prod(0)
return p[:-m] * coeff
@_wraps(np.trim_zeros)
def trim_zeros(filt, trim='fb'):
filt = core.concrete_or_error(asarray, filt,
"Error arose in the `filt` argument of trim_zeros()")
nz = (filt == 0)
if all(nz):
return empty(0, _dtype(filt))
start = argmin(nz) if 'f' in trim.lower() else 0
end = argmin(nz[::-1]) if 'b' in trim.lower() else 0
return filt[start:len(filt) - end]
_LEADING_ZEROS_DOC = """\
Setting trim_leading_zeros=True makes the output match that of numpy.
But prevents the function from being able to be used in compiled code.
"""
@_wraps(np.polymul, lax_description=_LEADING_ZEROS_DOC)
def polymul(a1, a2, *, trim_leading_zeros=False):
_check_arraylike("polymul", a1, a2)
a1, a2 = _promote_dtypes_inexact(a1, a2)
if trim_leading_zeros and (len(a1) > 1 or len(a2) > 1):
a1, a2 = trim_zeros(a1, trim='f'), trim_zeros(a2, trim='f')
if len(a1) == 0:
a1 = asarray([0.])
if len(a2) == 0:
a2 = asarray([0.])
val = convolve(a1, a2, mode='full')
return val
@_wraps(np.polysub)
@jit
def polysub(a1, a2):
_check_arraylike("polysub", a1, a2)
a1, a2 = _promote_dtypes(a1, a2)
return polyadd(a1, -a2)
@_wraps(np.append)
@partial(jit, static_argnames=('axis',))
def append(arr, values, axis: Optional[int] = None):
if axis is None:
return concatenate([ravel(arr), ravel(values)], 0)
else:
return concatenate([arr, values], axis=axis)
@_wraps(np.delete)
def delete(arr, obj, axis=None):
_check_arraylike("delete", arr)
if axis is None:
arr = ravel(arr)
axis = 0
axis = _canonicalize_axis(axis, arr.ndim)
# Case 1: obj is a static integer.
try:
obj = operator.index(obj)
obj = _canonicalize_axis(obj, arr.shape[axis])
except TypeError:
pass
else:
idx = tuple(slice(None) for i in range(axis))
return concatenate([arr[idx + (slice(0, obj),)], arr[idx + (slice(obj + 1, None),)]], axis=axis)
# Case 2: obj is a static slice.
if isinstance(obj, slice):
# TODO(jakevdp): we should be able to do this dynamically with care.
indices = np.delete(np.arange(arr.shape[axis]), obj)
return take(arr, indices, axis=axis)
# Case 3: obj is an array
# NB: pass both arrays to check for appropriate error message.
_check_arraylike("delete", arr, obj)
obj = core.concrete_or_error(np.asarray, obj, "'obj' array argument of jnp.delete()")
if issubdtype(obj.dtype, integer):
# TODO(jakevdp): in theory this could be done dynamically if obj has no duplicates,
# but this would require the complement of lax.gather.
mask = np.ones(arr.shape[axis], dtype=bool)
mask[obj] = False
elif obj.dtype == bool:
if obj.shape != (arr.shape[axis],):
raise ValueError("np.delete(arr, obj): for boolean indices, obj must be one-dimensional "
"with length matching specified axis.")
mask = ~obj
else:
raise ValueError(f"np.delete(arr, obj): got obj.dtype={obj.dtype}; must be integer or bool.")
return arr[tuple(slice(None) for i in range(axis)) + (mask,)]
@_wraps(np.insert)
def insert(arr, obj, values, axis=None):
_check_arraylike("insert", arr, 0 if isinstance(obj, slice) else obj, values)
arr = asarray(arr)
values = asarray(values)
if axis is None:
arr = ravel(arr)
axis = 0
axis = core.concrete_or_error(None, axis, "axis argument of jnp.insert()")
axis = _canonicalize_axis(axis, arr.ndim)
if isinstance(obj, slice):
indices = arange(*obj.indices(arr.shape[axis]))
else:
indices = asarray(obj)
if indices.ndim > 1:
raise ValueError("jnp.insert(): obj must be a slice, a one-dimensional "
f"array, or a scalar; got {obj}")
if not np.issubdtype(indices.dtype, np.integer):
if indices.size == 0 and not isinstance(obj, ndarray):
indices = indices.astype(int)
else:
# Note: np.insert allows boolean inputs but the behavior is deprecated.
raise ValueError("jnp.insert(): index array must be "
f"integer typed; got {obj}")
values = array(values, ndmin=arr.ndim, dtype=arr.dtype, copy=False)
if indices.size == 1:
index = ravel(indices)[0]
if indices.ndim == 0:
values = moveaxis(values, 0, axis)
indices = full(values.shape[axis], index)
n_input = arr.shape[axis]
n_insert = broadcast_shapes(indices.shape, values.shape[axis])[0]
out_shape = list(arr.shape)
out_shape[axis] += n_insert
out = zeros_like(arr, shape=tuple(out_shape))
indices = where(indices < 0, indices + n_input, indices)
indices = clip(indices, 0, n_input)
values_ind = indices.at[argsort(indices)].add(arange(n_insert))
arr_mask = ones(n_input + n_insert, dtype=bool).at[values_ind].set(False)
arr_ind = where(arr_mask, size=n_input)[0]
out = out.at[(slice(None),) * axis + (values_ind,)].set(values)
out = out.at[(slice(None),) * axis + (arr_ind,)].set(arr)
return out
@_wraps(np.apply_along_axis)
def apply_along_axis(func1d, axis: int, arr, *args, **kwargs):
num_dims = ndim(arr)
axis = _canonicalize_axis(axis, num_dims)
func = lambda arr: func1d(arr, *args, **kwargs)
for i in range(1, num_dims - axis):
func = jax.vmap(func, in_axes=i, out_axes=-1)
for i in range(axis):
func = jax.vmap(func, in_axes=0, out_axes=0)
return func(arr)
@_wraps(np.apply_over_axes)
def apply_over_axes(func, a, axes):
for axis in axes:
b = func(a, axis=axis)
if b.ndim == a.ndim:
a = b
elif b.ndim == a.ndim - 1:
a = expand_dims(b, axis)
else:
raise ValueError("function is not returning an array of the correct shape")
return a
### Tensor contraction operations
@_wraps(np.dot, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('precision',), inline=True)
def dot(a, b, *, precision=None): # pylint: disable=missing-docstring
_check_arraylike("dot", a, b)
a, b = _promote_dtypes(a, b)
a_ndim, b_ndim = ndim(a), ndim(b)
if a_ndim == 0 or b_ndim == 0:
return lax.mul(a, b)
if _max(a_ndim, b_ndim) <= 2:
return lax.dot(a, b, precision=precision)
if b_ndim == 1:
contract_dims = ((a_ndim - 1,), (0,))
else:
contract_dims = ((a_ndim - 1,), (b_ndim - 2,))
batch_dims = ((), ())
return lax.dot_general(a, b, (contract_dims, batch_dims), precision)
@_wraps(np.matmul, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('precision',), inline=True)
def matmul(a, b, *, precision=None): # pylint: disable=missing-docstring
_check_arraylike("matmul", a, b)
for i, x in enumerate((a, b)):
if ndim(x) < 1:
msg = (f"matmul input operand {i} must have ndim at least 1, "
f"but it has ndim {ndim(x)}")
raise ValueError(msg)
a, b = _promote_dtypes(a, b)
a_is_mat, b_is_mat = (ndim(a) > 1), (ndim(b) > 1)
a_batch_dims = shape(a)[:-2] if a_is_mat else ()
b_batch_dims = shape(b)[:-2] if b_is_mat else ()
num_batch_dims = _max(len(a_batch_dims), len(b_batch_dims))
a_batch_dims = (None,) * (num_batch_dims - len(a_batch_dims)) + a_batch_dims
b_batch_dims = (None,) * (num_batch_dims - len(b_batch_dims)) + b_batch_dims
# Dimensions to squeeze from the inputs.
a_squeeze = []
b_squeeze = []
# Positions of batch dimensions in squeezed inputs.
a_batch = []
b_batch = []
# Desired index in final output of each kind of dimension, in the order that
# lax.dot_general will emit them.
idx_batch = []
idx_a_other = [] # other = non-batch, non-contracting.
idx_b_other = []
for i, (ba, bb) in enumerate(zip(a_batch_dims, b_batch_dims)):
if ba is None:
idx_b_other.append(i)
elif bb is None:
idx_a_other.append(i)
elif core.symbolic_equal_dim(ba, 1):
idx_b_other.append(i)
a_squeeze.append(len(idx_batch) + len(idx_a_other) + len(a_squeeze))
elif core.symbolic_equal_dim(bb, 1):
idx_a_other.append(i)
b_squeeze.append(len(idx_batch) + len(idx_b_other) + len(b_squeeze))
elif core.symbolic_equal_dim(ba, bb):
a_batch.append(len(idx_batch) + len(idx_a_other))
b_batch.append(len(idx_batch) + len(idx_b_other))
idx_batch.append(i)
else:
raise ValueError("Incompatible shapes for matmul arguments: {} and {}"
.format(shape(a), shape(b)))
if a_is_mat: idx_a_other.append(num_batch_dims)
if b_is_mat: idx_b_other.append(num_batch_dims + a_is_mat)
perm = np.argsort(np.concatenate([idx_batch, idx_a_other, idx_b_other]))
a = lax.squeeze(a, tuple(a_squeeze))
b = lax.squeeze(b, tuple(b_squeeze))
out = lax.dot_general(
a, b, (((ndim(a) - 1,), (ndim(b) - 1 - b_is_mat,)), (a_batch, b_batch)),
precision=precision)
return lax.transpose(out, perm)
@_wraps(np.vdot, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('precision',), inline=True)
def vdot(a, b, *, precision=None):
_check_arraylike("vdot", a, b)
if issubdtype(_dtype(a), complexfloating):
a = conj(a)
return dot(a.ravel(), b.ravel(), precision=precision)
@_wraps(np.tensordot, lax_description=_PRECISION_DOC)
def tensordot(a, b, axes=2, *, precision=None):
_check_arraylike("tensordot", a, b)
a_ndim = ndim(a)
b_ndim = ndim(b)
a, b = _promote_dtypes(a, b)
if type(axes) is int:
if axes > _min(a_ndim, b_ndim):
msg = "Number of tensordot axes (axes {}) exceeds input ranks ({} and {})"
raise TypeError(msg.format(axes, a.shape, b.shape))
contracting_dims = tuple(range(a_ndim - axes, a_ndim)), tuple(range(axes))
elif type(axes) in (list, tuple) and len(axes) == 2:
ax1, ax2 = axes
if type(ax1) == type(ax2) == int:
contracting_dims = ((_canonicalize_axis(ax1, a_ndim),),
(_canonicalize_axis(ax2, b_ndim),))
elif type(ax1) in (list, tuple) and type(ax2) in (list, tuple):
if len(ax1) != len(ax2):
msg = "tensordot requires axes lists to have equal length, got {} and {}."
raise TypeError(msg.format(ax1, ax2))
contracting_dims = (tuple(_canonicalize_axis(i, a_ndim) for i in ax1),
tuple(_canonicalize_axis(i, b_ndim) for i in ax2))
else:
msg = ("tensordot requires both axes lists to be either ints, tuples or "
"lists, got {} and {}")
raise TypeError(msg.format(ax1, ax2))
else:
msg = ("tensordot axes argument must be an int, a pair of ints, or a pair "
"of lists/tuples of ints.")
raise TypeError(msg)
return lax.dot_general(a, b, (contracting_dims, ((), ())),
precision=precision)
_EINSUM_DOC = _PRECISION_DOC + """\
A tuple ``precision`` does not necessarily map to multiple arguments of ``einsum()``;
rather, the specified ``precision`` is forwarded to each ``dot_general`` call used in
the implementation.
"""
@_wraps(np.einsum, lax_description=_EINSUM_DOC, skip_params=['out'])
def einsum(*operands, out=None, optimize='optimal', precision=None,
_use_xeinsum=False):
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.einsum is not supported.")
if (_use_xeinsum or isinstance(operands[0], str) and '{' in operands[0] and
len(operands[1:]) == 2):
return lax.xeinsum(*operands)
optimize = 'optimal' if optimize is True else optimize
# using einsum_call=True here is an internal api for opt_einsum
# Allow handling of shape polymorphism
non_constant_dim_types = {
type(d) for op in operands if not isinstance(op, str)
for d in np.shape(op) if not core.is_constant_dim(d)
}
if not non_constant_dim_types:
einsum_contract_path_fn = opt_einsum.contract_path
else:
einsum_contract_path_fn = _polymorphic_einsum_contract_path_handlers[next(iter(non_constant_dim_types))]
operands, contractions = einsum_contract_path_fn(
*operands, einsum_call=True, use_blas=True, optimize=optimize)
contractions = tuple((a, frozenset(b), c) for a, b, c, *_ in contractions)
return _einsum(operands, contractions, precision)
# Enable other modules to override einsum_contact_path.
# Indexed by the type of the non constant dimension
_polymorphic_einsum_contract_path_handlers = {} # type: ignore
@_wraps(np.einsum_path)
def einsum_path(subscripts, *operands, optimize='greedy'):
# using einsum_call=True here is an internal api for opt_einsum
return opt_einsum.contract_path(subscripts, *operands, optimize=optimize)
def _removechars(s, chars):
return s.translate(str.maketrans(dict.fromkeys(chars)))
@partial(jit, static_argnums=(1, 2))
def _einsum(operands: Sequence,
contractions: Sequence[Tuple[Tuple[int, ...], FrozenSet[str], str]],
precision):
operands = list(_promote_dtypes(*operands))
def sum(x, axes):
return lax.reduce(x, np.array(0, x.dtype),
lax.add if x.dtype != bool_ else lax.bitwise_or, axes)
def sum_uniques(operand, names, uniques):
if uniques:
axes = [names.index(name) for name in uniques]
operand = sum(operand, axes)
names = _removechars(names, uniques)
return operand, names
def sum_repeats(operand, names, counts, keep_names):
for name, count in counts.items():
if count > 1:
axes = [i for i, n in enumerate(names) if n == name]
eye = lax._delta(operand.dtype, operand.shape, axes)
if name not in keep_names:
operand = sum(operand * eye, axes)
names = names.replace(name, '')
else:
operand = sum(operand * eye, axes[:-1])
names = names.replace(name, '', count - 1)
return operand, names
def filter_singleton_dims(operand, names, other_shape, other_names):
s = shape(operand)
new_shape = []
new_names = []
for i, d in enumerate(names):
other_i = other_names.find(d)
if not core.symbolic_equal_dim(s[i], 1) or other_i == -1 or core.symbolic_equal_dim(other_shape[other_i], 1):
new_shape.append(s[i])
new_names.append(d)
return reshape(operand, tuple(new_shape)), "".join(new_names)
for operand_indices, contracted_names_set, einstr in contractions:
contracted_names = sorted(contracted_names_set)
input_str, result_names = einstr.split('->')
input_names = input_str.split(',')
# switch on the number of operands to be processed in this loop iteration.
# every case here sets 'operand' and 'names'.
if len(operand_indices) == 1:
operand = operands.pop(operand_indices[0])
names, = input_names
counts = collections.Counter(names)
# sum out unique contracted indices with a single reduce-sum
uniques = [name for name in contracted_names if counts[name] == 1]
operand, names = sum_uniques(operand, names, uniques)
# for every repeated index, do a contraction against an identity matrix
operand, names = sum_repeats(operand, names, counts, result_names)
elif len(operand_indices) == 2:
lhs, rhs = map(operands.pop, operand_indices)
lhs_names, rhs_names = input_names
# handle cases where one side of a contracting or batch dimension is 1
# but its counterpart is not.
lhs, lhs_names = filter_singleton_dims(lhs, lhs_names, shape(rhs),
rhs_names)
rhs, rhs_names = filter_singleton_dims(rhs, rhs_names, shape(lhs),
lhs_names)
lhs_counts = collections.Counter(lhs_names)
rhs_counts = collections.Counter(rhs_names)
# sum out unique contracted indices in lhs and rhs
lhs_uniques = [name for name in contracted_names
if lhs_counts[name] == 1 and rhs_counts[name] == 0]
lhs, lhs_names = sum_uniques(lhs, lhs_names, lhs_uniques)
rhs_uniques = [name for name in contracted_names
if rhs_counts[name] == 1 and lhs_counts[name] == 0]
rhs, rhs_names = sum_uniques(rhs, rhs_names, rhs_uniques)
# for every repeated index, contract against an identity matrix
lhs, lhs_names = sum_repeats(lhs, lhs_names, lhs_counts,
result_names + rhs_names)
rhs, rhs_names = sum_repeats(rhs, rhs_names, rhs_counts,
result_names + lhs_names)
lhs_or_rhs_names = set(lhs_names) | set(rhs_names)
contracted_names = [x for x in contracted_names if x in lhs_or_rhs_names]
lhs_and_rhs_names = set(lhs_names) & set(rhs_names)
batch_names = [x for x in result_names if x in lhs_and_rhs_names]
lhs_batch, rhs_batch = unzip2((lhs_names.find(n), rhs_names.find(n))
for n in batch_names)
# NOTE(mattjj): this can fail non-deterministically in python3, maybe
# due to opt_einsum
assert _all(
name in lhs_names and name in rhs_names and
lhs.shape[lhs_names.index(name)] == rhs.shape[rhs_names.index(name)]
for name in contracted_names)
# contract using lax.dot_general
batch_names_str = ''.join(batch_names)
lhs_cont, rhs_cont = unzip2((lhs_names.index(n), rhs_names.index(n))
for n in contracted_names)
deleted_names = batch_names_str + ''.join(contracted_names)
remaining_lhs_names = _removechars(lhs_names, deleted_names)
remaining_rhs_names = _removechars(rhs_names, deleted_names)
# Try both orders of lhs and rhs, in the hope that one of them means we
# don't need an explicit transpose. opt_einsum likes to contract from
# right to left, so we expect (rhs,lhs) to have the best chance of not
# needing a transpose.
names = batch_names_str + remaining_rhs_names + remaining_lhs_names
if names == result_names:
dimension_numbers = ((rhs_cont, lhs_cont), (rhs_batch, lhs_batch))
operand = lax.dot_general(rhs, lhs, dimension_numbers, precision)
else:
names = batch_names_str + remaining_lhs_names + remaining_rhs_names
dimension_numbers = ((lhs_cont, rhs_cont), (lhs_batch, rhs_batch))
operand = lax.dot_general(lhs, rhs, dimension_numbers, precision)
else:
raise NotImplementedError # if this is actually reachable, open an issue!
# the resulting 'operand' with axis labels 'names' should be a permutation
# of the desired result
assert len(names) == len(result_names) == len(set(names))
assert set(names) == set(result_names)
if names != result_names:
perm = tuple([names.index(name) for name in result_names])
operand = lax.transpose(operand, perm)
operands.append(operand) # used in next iteration
return operands[0]
def _movechars(s, src, dst):
"""Helper for einsum string munging, like moveaxis on identifier strings."""
chars = [c for i, c in enumerate(s) if i not in src]
for i, j in sorted(zip(dst, src)):
chars.insert(i, s[j])
return ''.join(chars)
@_wraps(np.inner, lax_description=_PRECISION_DOC)
@partial(jit, static_argnames=('precision',), inline=True)
def inner(a, b, *, precision=None):
if ndim(a) == 0 or ndim(b) == 0:
return a * b
return tensordot(a, b, (-1, -1), precision=precision)
@_wraps(np.outer, skip_params=['out'])
@partial(jit, inline=True)
def outer(a, b, out=None):
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.outer is not supported.")
a, b = _promote_dtypes(a, b)
return ravel(a)[:, None] * ravel(b)[None, :]
@_wraps(np.cross)
@partial(jit, static_argnames=('axisa', 'axisb', 'axisc', 'axis'))
def cross(a, b, axisa: int = -1, axisb: int = -1, axisc: int = -1,
axis: Optional[int] = None):
if axis is not None:
axisa = axis
axisb = axis
axisc = axis
a = moveaxis(a, axisa, -1)
b = moveaxis(b, axisb, -1)
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
raise ValueError("Dimension must be either 2 or 3 for cross product")
if a.shape[-1] == 2 and b.shape[-1] == 2:
return a[..., 0] * b[..., 1] - a[..., 1] * b[..., 0]
a0 = a[..., 0]
a1 = a[..., 1]
a2 = a[..., 2] if a.shape[-1] == 3 else zeros_like(a0)
b0 = b[..., 0]
b1 = b[..., 1]
b2 = b[..., 2] if b.shape[-1] == 3 else zeros_like(b0)
c = array([a1 * b2 - a2 * b1, a2 * b0 - a0 * b2, a0 * b1 - a1 * b0])
return moveaxis(c, 0, axisc)
@_wraps(np.kron)
@jit
def kron(a, b):
a, b = _promote_dtypes(a, b)
if ndim(a) < ndim(b):
a = expand_dims(a, range(ndim(b) - ndim(a)))
elif ndim(b) < ndim(a):
b = expand_dims(b, range(ndim(a) - ndim(b)))
a_reshaped = expand_dims(a, range(1, 2 * ndim(a), 2))
b_reshaped = expand_dims(b, range(0, 2 * ndim(b), 2))
out_shape = tuple(np.multiply(shape(a), shape(b)))
return reshape(lax.mul(a_reshaped, b_reshaped), out_shape)
@_wraps(np.vander)
@partial(jit, static_argnames=('N', 'increasing'))
def vander(x, N=None, increasing=False):
_check_arraylike("vander", x)
x = asarray(x)
if x.ndim != 1:
raise ValueError("x must be a one-dimensional array")
N = x.shape[0] if N is None else core.concrete_or_error(
operator.index, N, "'N' argument of jnp.vander()")
if N < 0:
raise ValueError("N must be nonnegative")
iota = lax.iota(x.dtype, N)
if not increasing:
iota = lax.sub(lax._const(iota, N - 1), iota)
return power(x[..., None], expand_dims(iota, tuple(range(x.ndim))))
### Misc
_ARGWHERE_DOC = """\
Because the size of the output of ``argwhere`` is data-dependent, the function is not
typically compatible with JIT. The JAX version adds the optional ``size`` argument, which
specifies the size of the leading dimension of the output - it must be specified statically
for ``jnp.argwhere`` to be compiled with non-static operands. If ``size`` is specified,
the indices of the first ``size`` True elements will be returned; if there are fewer
nonzero elements than `size` indicates, the index arrays will be zero-padded.
"""
@_wraps(np.argwhere, lax_description=_ARGWHERE_DOC)
def argwhere(a, *, size=None, fill_value=None):
result = transpose(vstack(nonzero(a, size=size, fill_value=fill_value)))
if ndim(a) == 0:
return result[:0].reshape(result.shape[0], 0)
return result.reshape(result.shape[0], ndim(a))
@_wraps(np.argmax, skip_params=['out'])
def argmax(a, axis: Optional[int] = None, out=None):
return _argmax(a, None if axis is None else operator.index(axis))
@partial(jit, static_argnames=('axis',), inline=True)
def _argmax(a, axis: Optional[int] = None, out=None):
_check_arraylike("argmax", a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.argmax is not supported.")
if axis is None:
a = ravel(a)
axis = 0
if a.shape[axis] == 0:
raise ValueError("attempt to get argmax of an empty sequence")
return lax.argmax(a, _canonicalize_axis(axis, a.ndim), dtypes.canonicalize_dtype(int_))
@_wraps(np.argmin, skip_params=['out'])
def argmin(a, axis: Optional[int] = None, out=None):
return _argmin(a, None if axis is None else operator.index(axis))
@partial(jit, static_argnames=('axis',), inline=True)
def _argmin(a, axis: Optional[int] = None, out=None):
_check_arraylike("argmin", a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.argmin is not supported.")
if axis is None:
a = ravel(a)
axis = 0
if a.shape[axis] == 0:
raise ValueError("attempt to get argmin of an empty sequence")
return lax.argmin(a, _canonicalize_axis(axis, a.ndim), dtypes.canonicalize_dtype(int_))
_NANARG_DOC = """\
Warning: jax.numpy.arg{} returns -1 for all-NaN slices and does not raise
an error.
"""
@_wraps(np.nanargmax, lax_description=_NANARG_DOC.format("max"))
def nanargmax(a, axis: Optional[int] = None):
return _nanargmax(a, None if axis is None else operator.index(axis))
@partial(jit, static_argnames=('axis',))
def _nanargmax(a, axis: Optional[int] = None):
_check_arraylike("nanargmax", a)
if not issubdtype(_dtype(a), inexact):
return argmax(a, axis=axis)
nan_mask = isnan(a)
a = where(nan_mask, -inf, a)
res = argmax(a, axis=axis)
return where(all(nan_mask, axis=axis), -1, res)
@_wraps(np.nanargmin, lax_description=_NANARG_DOC.format("min"))
def nanargmin(a, axis: Optional[int] = None):
return _nanargmin(a, None if axis is None else operator.index(axis))
@partial(jit, static_argnames=('axis',))
def _nanargmin(a, axis: Optional[int] = None):
_check_arraylike("nanargmin", a)
if not issubdtype(_dtype(a), inexact):
return argmin(a, axis=axis)
nan_mask = isnan(a)
a = where(nan_mask, inf, a)
res = argmin(a, axis=axis)
return where(all(nan_mask, axis=axis), -1, res)
@_wraps(np.sort)
@partial(jit, static_argnames=('axis', 'kind', 'order'))
def sort(a, axis: Optional[int] = -1, kind='quicksort', order=None):
_check_arraylike("sort", a)
if kind != 'quicksort':
warnings.warn("'kind' argument to sort is ignored.")
if order is not None:
raise ValueError("'order' argument to sort is not supported.")
if axis is None:
return lax.sort(a.ravel(), dimension=0)
else:
return lax.sort(a, dimension=_canonicalize_axis(axis, ndim(a)))
@_wraps(np.sort_complex)
@jit
def sort_complex(a):
_check_arraylike("sort_complex", a)
a = lax.sort(a, dimension=0)
return lax.convert_element_type(a, result_type(a, dtypes.canonicalize_dtype(complex_)))
@_wraps(np.lexsort)
@partial(jit, static_argnames=('axis',))
def lexsort(keys, axis=-1):
keys = tuple(keys)
if len(keys) == 0:
raise TypeError("need sequence of keys with len > 0 in lexsort")
if len({shape(key) for key in keys}) > 1:
raise ValueError("all keys need to be the same shape")
if ndim(keys[0]) == 0:
return array(0, dtype=dtypes.canonicalize_dtype(int_))
axis = _canonicalize_axis(axis, ndim(keys[0]))
use_64bit_index = keys[0].shape[axis] >= (1 << 31)
iota = lax.broadcasted_iota(int64 if use_64bit_index else int_, shape(keys[0]), axis)
return lax.sort((*keys[::-1], iota), dimension=axis, num_keys=len(keys))[-1]
_ARGSORT_DOC = """
Only :code:`kind='stable'` is supported. Other :code:`kind` values will produce
a warning and be treated as if they were :code:`'stable'`.
"""
@_wraps(np.argsort, lax_description=_ARGSORT_DOC)
@partial(jit, static_argnames=('axis', 'kind', 'order'))
def argsort(a, axis: Optional[int] = -1, kind='stable', order=None):
_check_arraylike("argsort", a)
if kind != 'stable':
warnings.warn("'kind' argument to argsort is ignored; only 'stable' sorts "
"are supported.")
if order is not None:
raise ValueError("'order' argument to argsort is not supported.")
if axis is None:
return argsort(a.ravel(), 0)
else:
axis_num = _canonicalize_axis(axis, ndim(a))
use_64bit_index = a.shape[axis_num] >= (1 << 31)
iota = lax.broadcasted_iota(int64 if use_64bit_index else int_, shape(a), axis_num)
_, perm = lax.sort_key_val(a, iota, dimension=axis_num)
return perm
@_wraps(np.msort)
def msort(a):
return sort(a, axis=0)
@partial(jit, static_argnums=(2,))
def _roll(a, shift, axis):
a_shape = shape(a)
if axis is None:
return lax.reshape(_roll(ravel(a), shift, axis=0), a_shape)
shift = asarray(shift)
a_ndim = len(a_shape)
axis = np.asarray(axis)
b_shape = lax.broadcast_shapes(shift.shape, axis.shape, (1,))
if len(b_shape) != 1:
msg = "'shift' and 'axis' arguments to roll must be scalars or 1D arrays"
raise ValueError(msg)
for x, i in zip(broadcast_to(shift, b_shape),
np.broadcast_to(axis, b_shape)):
i = _canonicalize_axis(i, a_ndim)
x = remainder(x, (a_shape[i] or 1))
a = lax.concatenate((a, a), i)
a = lax.dynamic_slice_in_dim(a, a_shape[i] - x, a_shape[i], axis=i)
return a
@_wraps(np.roll)
def roll(a, shift, axis: Optional[Union[int, Sequence[int]]] = None):
_check_arraylike("roll", a,)
if isinstance(axis, list):
axis = tuple(axis)
return _roll(a, shift, axis)
@_wraps(np.rollaxis, lax_description=_ARRAY_VIEW_DOC)
@partial(jit, static_argnames=('axis', 'start'))
def rollaxis(a, axis: int, start=0):
_check_arraylike("rollaxis", a)
start = core.concrete_or_error(operator.index, start, "'start' argument of jnp.rollaxis()")
a_ndim = ndim(a)
axis = _canonicalize_axis(axis, a_ndim)
if not (-a_ndim <= start <= a_ndim):
raise ValueError(f"start={start} must satisfy {-a_ndim}<=start<={a_ndim}")
if start < 0:
start += a_ndim
if start > axis:
start -= 1
return moveaxis(a, axis, start)
@_wraps(np.packbits)
@partial(jit, static_argnames=('axis', 'bitorder'))
def packbits(a, axis: Optional[int] = None, bitorder='big'):
_check_arraylike("packbits", a)
if not (issubdtype(_dtype(a), integer) or issubdtype(_dtype(a), bool_)):
raise TypeError('Expected an input array of integer or boolean data type')
if bitorder not in ['little', 'big']:
raise ValueError("'order' must be either 'little' or 'big'")
a = greater(a, 0).astype('uint8')
bits = arange(8, dtype='uint8')
if bitorder == 'big':
bits = bits[::-1]
if axis is None:
a = ravel(a)
axis = 0
a = swapaxes(a, axis, -1)
remainder = a.shape[-1] % 8
if remainder:
a = lax.pad(a, np.uint8(0),
(a.ndim - 1) * [(0, 0, 0)] + [(0, 8 - remainder, 0)])
a = a.reshape(a.shape[:-1] + (a.shape[-1] // 8, 8))
bits = expand_dims(bits, tuple(range(a.ndim - 1)))
packed = (a << bits).sum(-1).astype('uint8')
return swapaxes(packed, axis, -1)
@_wraps(np.unpackbits)
@partial(jit, static_argnames=('axis', 'count', 'bitorder'))
def unpackbits(a, axis: Optional[int] = None, count=None, bitorder='big'):
_check_arraylike("unpackbits", a)
if _dtype(a) != uint8:
raise TypeError("Expected an input array of unsigned byte data type")
if bitorder not in ['little', 'big']:
raise ValueError("'order' must be either 'little' or 'big'")
bits = asarray(1) << arange(8, dtype='uint8')
if bitorder == 'big':
bits = bits[::-1]
if axis is None:
a = ravel(a)
axis = 0
a = swapaxes(a, axis, -1)
unpacked = ((a[..., None] & expand_dims(bits, tuple(range(a.ndim)))) > 0).astype('uint8')
unpacked = unpacked.reshape(unpacked.shape[:-2] + (-1,))[..., :count]
return swapaxes(unpacked, axis, -1)
@_wraps(np.take, skip_params=['out'])
def take(a, indices, axis: Optional[int] = None, out=None, mode=None):
return _take(a, indices, None if axis is None else operator.index(axis), out,
mode)
@partial(jit, static_argnames=('axis', 'mode'))
def _take(a, indices, axis: Optional[int] = None, out=None, mode=None):
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.take is not supported.")
_check_arraylike("take", a, indices)
a = asarray(a)
indices = asarray(indices)
if axis is None:
a = ravel(a)
axis_idx = 0
else:
axis_idx = _canonicalize_axis(axis, ndim(a))
if mode is None:
# TODO(phawkins): change default mode to "fill" and delete this case.
# lax.gather() does not support negative indices, so we wrap them here
indices = where(indices < 0, indices + a.shape[axis_idx], indices)
gather_mode = lax.GatherScatterMode.CLIP
elif mode == "raise":
# TODO(phawkins): we have no way to report out of bounds errors yet.
raise NotImplementedError("The 'raise' mode to jnp.take is not supported.")
elif mode == "wrap":
indices = mod(indices, _constant_like(indices, a.shape[axis_idx]))
gather_mode = lax.GatherScatterMode.PROMISE_IN_BOUNDS
elif mode == "fill":
# Undocumented non-standard mode corresponding to the fill_or_drop mode on
# lax.gather()
gather_mode = lax.GatherScatterMode.FILL_OR_DROP
# lax.gather() does not support negative indices, so we wrap them here
indices = where(indices < 0, indices + a.shape[axis_idx], indices)
elif mode == "clip":
gather_mode = lax.GatherScatterMode.CLIP
else:
raise ValueError("Invalid mode '{}' for np.take".format(mode))
index_dims = len(shape(indices))
slice_sizes = list(shape(a))
if slice_sizes[axis_idx] == 0:
if indices.size != 0:
raise IndexError("Cannot do a non-empty jnp.take() from an empty axis.")
return a
if indices.size == 0:
out_shape = (slice_sizes[:axis_idx] + list(indices.shape) +
slice_sizes[axis_idx + 1:])
return full_like(a, 0, shape=out_shape)
slice_sizes[axis_idx] = 1
dnums = lax.GatherDimensionNumbers(
offset_dims=tuple(
list(range(axis_idx)) +
list(range(axis_idx + index_dims, len(a.shape) + index_dims - 1))),
collapsed_slice_dims=(axis_idx,),
start_index_map=(axis_idx,))
return lax.gather(a, indices[..., None], dimension_numbers=dnums,
slice_sizes=tuple(slice_sizes),
mode=gather_mode)
def _normalize_index(index, axis_size):
"""Normalizes an index value in the range [-N, N) to the range [0, N)."""
if core.is_constant_dim(axis_size):
axis_size_val = _constant_like(index, axis_size)
else:
axis_size_val = lax.convert_element_type(core.dimension_as_value(axis_size),
_dtype(index))
return lax.select(
lax.lt(index, _constant_like(index, 0)),
lax.add(index, axis_size_val),
index)
@_wraps(np.take_along_axis, update_doc=False)
@partial(jit, static_argnames=('axis',))
def take_along_axis(arr, indices, axis: Optional[int]):
_check_arraylike("take_along_axis", arr, indices)
if axis is None:
if ndim(indices) != 1:
msg = "take_along_axis indices must be 1D if axis=None, got shape {}"
raise ValueError(msg.format(indices.shape))
return take_along_axis(arr.ravel(), indices, 0)
rank = ndim(arr)
if rank != ndim(indices):
msg = "indices and arr must have the same number of dimensions; {} vs. {}"
raise ValueError(msg.format(ndim(indices), ndim(arr)))
axis = _canonicalize_axis(axis, rank)
def replace(tup, val):
lst = list(tup)
lst[axis] = val
return tuple(lst)
use_64bit_index = _any([not core.is_constant_dim(d) or d >= (1 << 31) for d in arr.shape])
index_dtype = int64 if use_64bit_index else int32
indices = lax.convert_element_type(indices, index_dtype)
bcast_shape = lax.broadcast_shapes(replace(arr.shape, 1), replace(indices.shape, 1))
indices = broadcast_to(indices, replace(bcast_shape, indices.shape[axis]))
arr = broadcast_to(arr, replace(bcast_shape, arr.shape[axis]))
axis_size = arr.shape[axis]
arr_shape = replace(arr.shape, 1)
idx_shape = indices.shape
out_shape = lax.broadcast_shapes(idx_shape, arr_shape)
index_dims = [i for i, idx in enumerate(idx_shape) if i == axis or idx != 1]
gather_index_shape = tuple(np.array(out_shape)[index_dims]) + (1,)
gather_indices = []
slice_sizes = []
offset_dims = []
start_index_map = []
collapsed_slice_dims = []
j = 0
for i in range(rank):
if i == axis:
indices = _normalize_index(indices, axis_size)
gather_indices.append(lax.reshape(indices, gather_index_shape))
slice_sizes.append(1)
start_index_map.append(i)
collapsed_slice_dims.append(i)
j += 1
elif idx_shape[i] != 1:
iota = lax.iota(_dtype(indices), out_shape[i])
iota = lax.broadcast_in_dim(iota, gather_index_shape, (j,))
gather_indices.append(iota)
slice_sizes.append(1)
start_index_map.append(i)
collapsed_slice_dims.append(i)
j += 1
else:
# If idx_shape[i] == 1, we can just take the entirety of the arr's axis
# and avoid forming an iota index.
offset_dims.append(i)
slice_sizes.append(arr_shape[i])
gather_indices = lax.concatenate(gather_indices, dimension=j)
dnums = lax.GatherDimensionNumbers(
offset_dims=tuple(offset_dims),
collapsed_slice_dims=tuple(collapsed_slice_dims),
start_index_map=tuple(start_index_map))
return lax.gather(arr, gather_indices, dnums, tuple(slice_sizes))
### SetOps
@partial(jit, static_argnums=1)
def _unique_sorted_mask(ar, axis):
aux = moveaxis(ar, axis, 0)
if issubdtype(aux.dtype, np.complexfloating):
# Work around issue in sorting of complex numbers with Nan only in the
# imaginary component. This can be removed if sorting in this situation
# is fixed to match numpy.
aux = where(isnan(aux), lax._const(aux, nan), aux)
size, *out_shape = aux.shape
if _prod(out_shape) == 0:
size = 1
perm = zeros(1, dtype=int)
else:
perm = lexsort(aux.reshape(size, _prod(out_shape)).T[::-1])
aux = aux[perm]
if aux.size:
if issubdtype(aux.dtype, inexact):
# This is appropriate for both float and complex due to the documented behavior of np.unique:
# See https://github.com/numpy/numpy/blob/v1.22.0/numpy/lib/arraysetops.py#L212-L220
neq = lambda x, y: lax.ne(x, y) & ~(isnan(x) & isnan(y))
else:
neq = lax.ne
mask = ones(size, dtype=bool).at[1:].set(any(neq(aux[1:], aux[:-1]), tuple(range(1, aux.ndim))))
else:
mask = zeros(size, dtype=bool)
return aux, mask, perm
def _unique(ar, axis, return_index=False, return_inverse=False, return_counts=False,
size=None, fill_value=None, return_true_size=False):
"""
Find the unique elements of an array along a particular axis.
"""
if ar.shape[axis] == 0 and size and fill_value is None:
raise ValueError(
"jnp.unique: for zero-sized input with nonzero size argument, fill_value must be specified")
aux, mask, perm = _unique_sorted_mask(ar, axis)
ind = mask if size is None else nonzero(mask, size=size)[0]
result = aux[ind] if aux.size else aux
if fill_value is not None:
fill_value = asarray(fill_value, dtype=result.dtype)
if size is not None and fill_value is not None:
if result.shape[0]:
valid = lax.expand_dims(arange(size) < mask.sum(), tuple(range(1, result.ndim)))
result = where(valid, result, fill_value)
else:
result = full_like(result, fill_value, shape=(size, *result.shape[1:]))
result = moveaxis(result, 0, axis)
ret = (result,)
if return_index:
if aux.size:
ret += (perm[ind],)
else:
ret += (perm,)
if return_inverse:
if aux.size:
imask = cumsum(mask) - 1
inv_idx = zeros(mask.shape, dtype=dtypes.canonicalize_dtype(int_))
inv_idx = inv_idx.at[perm].set(imask)
else:
inv_idx = zeros(ar.shape[axis], dtype=int)
ret += (inv_idx,)
if return_counts:
if aux.size:
if size is None:
idx = append(nonzero(mask)[0], mask.size)
else:
idx = nonzero(mask, size=size + 1)[0]
idx = idx.at[1:].set(where(idx[1:], idx[1:], mask.size))
ret += (diff(idx),)
elif ar.shape[axis]:
ret += (array([ar.shape[axis]], dtype=int_),)
else:
ret += (empty(0, dtype=int),)
if return_true_size:
# Useful for internal uses of unique().
ret += (mask.sum(),)
return ret[0] if len(ret) == 1 else ret
_UNIQUE_DOC = """\
Because the size of the output of ``unique`` is data-dependent, the function is not
typically compatible with JIT. The JAX version adds the optional `size` argument which
specifies the size of the data-dependent output arrays: it must be specified statically
for ``jnp.unique`` to be compiled with non-static operands. If specified, the first `size`
unique elements will be returned; if there are fewer unique elements than `size` indicates,
the return value will be padded with `fill_value`, which defaults to the minimum value
along the specified axis of the input."""
@_wraps(np.unique, skip_params=['axis'], lax_description=_UNIQUE_DOC)
def unique(ar, return_index=False, return_inverse=False,
return_counts=False, axis: Optional[int] = None, *, size=None, fill_value=None):
_check_arraylike("unique", ar)
if size is None:
ar = core.concrete_or_error(None, ar, "The error arose for the first argument of jnp.unique()")
else:
size = core.concrete_or_error(operator.index, size, "The error arose for the size argument of jnp.unique()")
ar = asarray(ar)
if axis is None:
axis = 0
ar = ar.flatten()
axis = core.concrete_or_error(operator.index, axis, "axis argument of jnp.unique()")
return _unique(ar, axis, return_index, return_inverse, return_counts, size=size, fill_value=fill_value)
### Indexing
def _rewriting_take(arr, idx, indices_are_sorted=False, unique_indices=False,
mode=None, fill_value=None):
# Computes arr[idx].
# All supported cases of indexing can be implemented as an XLA gather,
# followed by an optional reverse and broadcast_in_dim.
arr = asarray(arr)
treedef, static_idx, dynamic_idx = _split_index_for_jit(idx, arr.shape)
return _gather(arr, treedef, static_idx, dynamic_idx, indices_are_sorted,
unique_indices, mode, fill_value)
# TODO(phawkins): re-enable jit after fixing excessive recompilation for
# slice indexes (e.g., slice(0, 5, None), slice(10, 15, None), etc.).
# @partial(jit, static_argnums=(1, 2))
def _gather(arr, treedef, static_idx, dynamic_idx, indices_are_sorted,
unique_indices, mode, fill_value):
idx = _merge_static_and_dynamic_indices(treedef, static_idx, dynamic_idx)
indexer = _index_to_gather(shape(arr), idx) # shared with _scatter_update
y = arr
if fill_value is not None:
core.concrete_or_error(None, fill_value,
"fill_value argument to indexed get()")
if np.ndim(fill_value) != 0:
raise ValueError("fill_value argument to indexed get() must be a scalar")
if isinstance(fill_value, np.ndarray):
fill_value = fill_value.item()
# Avoid calling gather if the slice shape is empty, both as a fast path and to
# handle cases like zeros(0)[array([], int32)].
if core.is_empty_shape(indexer.slice_shape):
return zeros_like(y, shape=indexer.slice_shape)
# We avoid generating a gather when indexer.gather_indices.size is empty.
if not core.is_empty_shape(indexer.gather_indices.shape):
y = lax.gather(
y, indexer.gather_indices, indexer.dnums, indexer.gather_slice_shape,
unique_indices=unique_indices or indexer.unique_indices,
indices_are_sorted=indices_are_sorted or indexer.indices_are_sorted,
mode=mode, fill_value=fill_value)
# Reverses axes with negative strides.
if indexer.reversed_y_dims:
y = lax.rev(y, indexer.reversed_y_dims)
# This adds np.newaxis/None dimensions.
return expand_dims(y, indexer.newaxis_dims)
_Indexer = collections.namedtuple("_Indexer", [
# The expected shape of the slice output.
"slice_shape",
# The slice shape to pass to lax.gather().
"gather_slice_shape",
# The gather indices to use.
"gather_indices",
# A GatherDimensionNumbers object describing the gather to perform.
"dnums",
# Are the gather_indices known to be non-overlapping and/or sorted?
# (In practice, these translate to "there no advanced indices", because
# only advanced indices could lead to index repetition.)
"unique_indices",
"indices_are_sorted",
# Slice dimensions that have negative strides, and so must be reversed after
# the gather.
"reversed_y_dims",
# Keep track of any axes created by `newaxis`. These must be inserted for
# gathers and eliminated for scatters.
"newaxis_dims",
])
def _split_index_for_jit(idx, shape):
"""Splits indices into necessarily-static and dynamic parts.
Used to pass indices into `jit`-ted function.
"""
# Convert list indices to tuples in cases (deprecated by NumPy.)
idx = _eliminate_deprecated_list_indexing(idx)
# Expand any (concrete) boolean indices. We can then use advanced integer
# indexing logic to handle them.
idx = _expand_bool_indices(idx, shape)
leaves, treedef = tree_flatten(idx)
dynamic = [None] * len(leaves)
static = [None] * len(leaves)
for i, x in enumerate(leaves):
if x is Ellipsis:
static[i] = x
elif isinstance(x, slice):
# slice objects aren't hashable.
static[i] = (x.start, x.stop, x.step)
else:
dynamic[i] = x
return treedef, tuple(static), dynamic
def _merge_static_and_dynamic_indices(treedef, static_idx, dynamic_idx):
"""Recombines indices that were split by _split_index_for_jit."""
idx = []
for s, d in zip(static_idx, dynamic_idx):
if d is not None:
idx.append(d)
elif isinstance(s, tuple):
idx.append(slice(s[0], s[1], s[2]))
else:
idx.append(s)
return treedef.unflatten(idx)
def _int(aval):
return not aval.shape and issubdtype(aval.dtype, integer)
def _index_to_gather(x_shape, idx, normalize_indices=True):
# Remove ellipses and add trailing slice(None)s.
idx = _canonicalize_tuple_index(len(x_shape), idx)
# Check for advanced indexing:
# https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing
# Do the advanced indexing axes appear contiguously? If not, NumPy semantics
# move the advanced axes to the front.
advanced_axes_are_contiguous = False
advanced_indexes = None
# The positions of the advanced indexing axes in `idx`.
idx_advanced_axes = []
# The positions of the advanced indexes in x's shape.
# collapsed, after None axes have been removed. See below.
x_advanced_axes = None
if _is_advanced_int_indexer(idx):
idx_no_nones = [(i, d) for i, d in enumerate(idx) if d is not None]
advanced_pairs = (
(asarray(e), i, j) for j, (i, e) in enumerate(idx_no_nones)
if isscalar(e) or isinstance(e, (Sequence, ndarray, np.ndarray)))
if normalize_indices:
advanced_pairs = ((_normalize_index(e, x_shape[j]), i, j)
for e, i, j in advanced_pairs)
advanced_indexes, idx_advanced_axes, x_advanced_axes = zip(*advanced_pairs)
advanced_axes_are_contiguous = np.all(np.diff(idx_advanced_axes) == 1)
x_axis = 0 # Current axis in x.
y_axis = 0 # Current axis in y, before collapsing. See below.
collapsed_y_axis = 0 # Current axis in y, after collapsing.
# Scatter dimension numbers.
offset_dims = []
collapsed_slice_dims = []
start_index_map = []
use_64bit_index = _any([not core.is_constant_dim(d) or d >= (1 << 31) for d in x_shape])
index_dtype = int64 if use_64bit_index else int32
# Gather indices.
# Pairs of (array, start_dim) values. These will be broadcast into
# gather_indices_shape, with the array dimensions aligned to start_dim, and
# then concatenated.
gather_indices = []
gather_indices_shape = []
# We perform three transformations to y before the scatter op, in order:
# First, y is broadcast to slice_shape. In general `y` only need broadcast to
# the right shape.
slice_shape = []
# Next, y is squeezed to remove newaxis_dims. This removes np.newaxis/`None`
# indices, which the scatter cannot remove itself.
newaxis_dims = []
# Finally, we reverse reversed_y_dims to handle slices with negative strides.
reversed_y_dims = []
gather_slice_shape = []
for idx_pos, i in enumerate(idx):
# Handle the advanced indices here if:
# * the advanced indices were not contiguous and we are the start.
# * we are at the position of the first advanced index.
if (advanced_indexes is not None and
(advanced_axes_are_contiguous and idx_pos == idx_advanced_axes[0] or
not advanced_axes_are_contiguous and idx_pos == 0)):
advanced_indexes = broadcast_arrays(*advanced_indexes)
shape = advanced_indexes[0].shape
ndim = len(shape)
start_dim = len(gather_indices_shape)
gather_indices += ((lax.convert_element_type(a, index_dtype), start_dim)
for a in advanced_indexes)
gather_indices_shape += shape
start_index_map.extend(x_advanced_axes)
collapsed_slice_dims.extend(x_advanced_axes)
slice_shape.extend(shape)
y_axis += ndim
collapsed_y_axis += ndim
# Per-index bookkeeping for advanced indexes.
if idx_pos in idx_advanced_axes:
x_axis += 1
gather_slice_shape.append(1)
continue
try:
abstract_i = core.get_aval(i)
except TypeError:
abstract_i = None
# Handle basic int indexes.
if isinstance(abstract_i, (ConcreteArray, ShapedArray)) and _int(abstract_i):
if core.symbolic_equal_dim(x_shape[x_axis], 0):
# XLA gives error when indexing into an axis of size 0
raise IndexError(f"index is out of bounds for axis {x_axis} with size 0")
i = _normalize_index(i, x_shape[x_axis]) if normalize_indices else i
i = lax.convert_element_type(i, index_dtype)
gather_indices.append((i, len(gather_indices_shape)))
collapsed_slice_dims.append(x_axis)
gather_slice_shape.append(1)
start_index_map.append(x_axis)
x_axis += 1
# Handle np.newaxis (None)
elif i is None:
slice_shape.append(1)
newaxis_dims.append(y_axis)
y_axis += 1
elif isinstance(i, slice):
# Normalize the slice to use None when possible
start, stop, step = i.start, i.stop, i.step
try:
if ((step is None or core.symbolic_equal_dim(step, 1)) and
stop is not None and core.symbolic_equal_dim(stop, x_shape[x_axis])):
# The following is a useful special case with shape polymorphism
stop = None
except TypeError:
pass
# Handle slice(None)
if start is None and stop is None and step is None:
slice_shape.append(x_shape[x_axis])
gather_slice_shape.append(x_shape[x_axis])
offset_dims.append(collapsed_y_axis)
collapsed_y_axis += 1
y_axis += 1
x_axis += 1
# Handle slice index (only static, otherwise an error is raised)
else:
if not _all(_is_slice_element_none_or_constant(elt)
for elt in (start, stop, step)):
msg = ("Array slice indices must have static start/stop/step to be used "
"with NumPy indexing syntax. "
f"Found slice({start}, {stop}, {step}). "
"To index a statically sized "
"array at a dynamic position, try lax.dynamic_slice/"
"dynamic_update_slice (JAX does not support dynamically sized "
"arrays within JIT compiled functions).")
raise IndexError(msg)
if not core.is_constant_dim(x_shape[x_axis]):
msg = ("Cannot use NumPy slice indexing on an array dimension whose "
f"size is not statically known ({x_shape[x_axis]}). "
"Try using lax.dynamic_slice/dynamic_update_slice")
raise IndexError(msg)
start, limit, stride, needs_rev = _static_idx(slice(start, stop, step),
x_shape[x_axis])
if needs_rev:
reversed_y_dims.append(collapsed_y_axis)
if stride == 1:
i = lax.convert_element_type(start, index_dtype)
gather_indices.append((i, len(gather_indices_shape)))
slice_shape.append(limit - start)
gather_slice_shape.append(limit - start)
offset_dims.append(collapsed_y_axis)
start_index_map.append(x_axis)
else:
i = arange(start, limit, stride, dtype=index_dtype)
size = i.shape[0]
slice_shape.append(size)
gather_slice_shape.append(1)
gather_indices.append((i, len(gather_indices_shape)))
gather_indices_shape.append(size)
start_index_map.append(x_axis)
collapsed_slice_dims.append(x_axis)
collapsed_y_axis += 1
y_axis += 1
x_axis += 1
else:
if (abstract_i is not None and
not (issubdtype(abstract_i.dtype, integer) or issubdtype(abstract_i.dtype, bool_))):
msg = ("Indexer must have integer or boolean type, got indexer "
"with type {} at position {}, indexer value {}")
raise TypeError(msg.format(abstract_i.dtype.name, idx_pos, i))
msg = "Indexing mode not yet supported. Open a feature request!\n{}"
raise IndexError(msg.format(idx))
if len(gather_indices) == 0:
gather_indices_array = np.zeros((0,), dtype=index_dtype)
elif len(gather_indices) == 1:
g, _ = gather_indices[0]
gather_indices_array = lax.expand_dims(g, (g.ndim,))
else:
last_dim = len(gather_indices_shape)
gather_indices_shape.append(1)
gather_indices_array = lax.concatenate([
lax.broadcast_in_dim(g, gather_indices_shape, tuple(range(i, i + g.ndim)))
for g, i in gather_indices],
last_dim)
dnums = lax.GatherDimensionNumbers(
offset_dims = tuple(offset_dims),
collapsed_slice_dims = tuple(sorted(collapsed_slice_dims)),
start_index_map = tuple(start_index_map)
)
return _Indexer(
slice_shape=slice_shape,
newaxis_dims=tuple(newaxis_dims),
gather_slice_shape=gather_slice_shape,
reversed_y_dims=reversed_y_dims,
dnums=dnums,
gather_indices=gather_indices_array,
unique_indices=advanced_indexes is None,
indices_are_sorted=advanced_indexes is None)
def _should_unpack_list_index(x):
"""Helper for _eliminate_deprecated_list_indexing."""
return (isinstance(x, (np.ndarray, ndarray)) and np.ndim(x) != 0
or isinstance(x, (Sequence, slice))
or x is Ellipsis or x is None)
def _eliminate_deprecated_list_indexing(idx):
# "Basic slicing is initiated if the selection object is a non-array,
# non-tuple sequence containing slice objects, [Ellipses, or newaxis
# objects]". Detects this and raises a TypeError.
if not isinstance(idx, tuple):
if isinstance(idx, Sequence) and not isinstance(idx, (ndarray, np.ndarray)):
# As of numpy 1.16, some non-tuple sequences of indices result in a warning, while
# others are converted to arrays, based on a set of somewhat convoluted heuristics
# (See https://github.com/numpy/numpy/blob/v1.19.2/numpy/core/src/multiarray/mapping.c#L179-L343)
# In JAX, we raise an informative TypeError for *all* non-tuple sequences.
if _any(_should_unpack_list_index(i) for i in idx):
msg = ("Using a non-tuple sequence for multidimensional indexing is not allowed; "
"use `arr[tuple(seq)]` instead of `arr[seq]`. "
"See https://github.com/google/jax/issues/4564 for more information.")
else:
msg = ("Using a non-tuple sequence for multidimensional indexing is not allowed; "
"use `arr[array(seq)]` instead of `arr[seq]`. "
"See https://github.com/google/jax/issues/4564 for more information.")
raise TypeError(msg)
else:
idx = (idx,)
return idx
def _is_boolean_index(i):
try:
abstract_i = core.get_aval(i)
except TypeError:
abstract_i = None
return (isinstance(abstract_i, ShapedArray) and issubdtype(abstract_i.dtype, bool_)
or isinstance(i, list) and i and _all(_is_scalar(e)
and issubdtype(_dtype(e), np.bool_) for e in i))
def _expand_bool_indices(idx, shape):
"""Converts concrete bool indexes into advanced integer indexes."""
out = []
total_dims = len(shape)
num_ellipsis = _sum(e is Ellipsis for e in idx)
if num_ellipsis > 1:
raise IndexError("an index can only have a single ellipsis ('...')")
elif num_ellipsis == 1:
total_dims = _sum(_ndim(e) if _is_boolean_index(e) else 1 for e in idx
if e is not None and e is not Ellipsis)
ellipsis_offset = 0
for dim_number, i in enumerate(idx):
try:
abstract_i = core.get_aval(i)
except TypeError:
abstract_i = None
if _is_boolean_index(i):
if isinstance(i, list):
i = array(i)
abstract_i = core.get_aval(i)
if not type(abstract_i) is ConcreteArray:
# TODO(mattjj): improve this error by tracking _why_ the indices are not concrete
raise errors.NonConcreteBooleanIndexError(abstract_i)
elif _ndim(i) == 0:
raise TypeError("JAX arrays do not support boolean scalar indices")
else:
i_shape = _shape(i)
start = len(out) + ellipsis_offset
expected_shape = shape[start: start + _ndim(i)]
if i_shape != expected_shape:
raise IndexError("boolean index did not match shape of indexed array in index "
f"{dim_number}: got {i_shape}, expected {expected_shape}")
out.extend(np.where(i))
else:
out.append(i)
if i is Ellipsis:
ellipsis_offset = len(shape) - total_dims - 1
return tuple(out)
def _is_slice_element_none_or_constant(elt):
"""Return True if elt is a constant or None."""
if elt is None: return True
try:
return type(core.get_aval(elt)) is ConcreteArray
except TypeError:
return False
# TODO(mattjj): clean up this logic
def _is_advanced_int_indexer(idx):
"""Returns True if idx should trigger int array indexing, False otherwise."""
# https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing
assert isinstance(idx, tuple)
if _all(e is None or e is Ellipsis or isinstance(e, slice)
or _is_scalar(e) and issubdtype(_dtype(e), np.integer) for e in idx):
return False
return _all(e is None or e is Ellipsis or isinstance(e, slice)
or _is_int_arraylike(e) for e in idx)
def _is_int_arraylike(x):
"""Returns True if x is array-like with integer dtype, False otherwise."""
return (isinstance(x, int) and not isinstance(x, bool)
or issubdtype(getattr(x, "dtype", None), np.integer)
or isinstance(x, (list, tuple)) and _all(_is_int_arraylike(e) for e in x))
def _is_scalar(x):
"""Checks if a Python or NumPy scalar."""
return np.isscalar(x) or (isinstance(x, (np.ndarray, ndarray))
and np.ndim(x) == 0)
def _canonicalize_tuple_index(arr_ndim, idx, array_name='array'):
"""Helper to remove Ellipsis and add in the implicit trailing slice(None)."""
len_without_none = _sum(1 for e in idx if e is not None and e is not Ellipsis)
if len_without_none > arr_ndim:
raise IndexError(
f"Too many indices for {array_name}: {len_without_none} "
f"non-None/Ellipsis indices for dim {arr_ndim}.")
ellipses = (i for i, elt in enumerate(idx) if elt is Ellipsis)
ellipsis_index = next(ellipses, None)
if ellipsis_index is not None:
if next(ellipses, None) is not None:
raise IndexError(
f"Multiple ellipses (...) not supported: {list(map(type, idx))}.")
colons = (slice(None),) * (arr_ndim - len_without_none)
idx = idx[:ellipsis_index] + colons + idx[ellipsis_index + 1:]
elif len_without_none < arr_ndim:
colons = (slice(None),) * (arr_ndim - len_without_none)
idx = tuple(idx) + colons
return idx
def _static_idx(idx: slice, size: core.DimSize):
"""Helper function to compute the static slice start/limit/stride values."""
if isinstance(size, int):
start, stop, step = idx.indices(size)
else:
raise TypeError(size)
if (step < 0 and stop >= start) or (step > 0 and start >= stop):
return 0, 0, 1, False # sliced to size zero
if step > 0:
return start, stop, step, False
else:
k = (start - stop - 1) % (-step)
return stop + k + 1, start + 1, -step, True
blackman = _wrap_numpy_nullary_function(np.blackman)
bartlett = _wrap_numpy_nullary_function(np.bartlett)
hamming = _wrap_numpy_nullary_function(np.hamming)
hanning = _wrap_numpy_nullary_function(np.hanning)
# TODO: lower `kaiser` via lax to allow non-constant beta values.
kaiser = _wrap_numpy_nullary_function(np.kaiser)
def _gcd_cond_fn(xs):
x1, x2 = xs
return any(x2 != 0)
def _gcd_body_fn(xs):
x1, x2 = xs
x1, x2 = (where(x2 != 0, x2, x1),
where(x2 != 0, lax.rem(x1, x2), lax._const(x2, 0)))
return (where(x1 < x2, x2, x1), where(x1 < x2, x1, x2))
@_wraps(np.gcd)
@jit
def gcd(x1, x2):
_check_arraylike("gcd", x1, x2)
if (not issubdtype(_dtype(x1), integer) or
not issubdtype(_dtype(x2), integer)):
raise ValueError("Arguments to jax.numpy.gcd must be integers.")
x1, x2 = _promote_dtypes(x1, x2)
x1, x2 = broadcast_arrays(x1, x2)
gcd, _ = lax.while_loop(_gcd_cond_fn, _gcd_body_fn, (abs(x1), abs(x2)))
return gcd
@_wraps(np.lcm)
@jit
def lcm(x1, x2):
_check_arraylike("lcm", x1, x2)
x1, x2 = _promote_dtypes(x1, x2)
d = gcd(x1, x2)
return where(d == 0, lax._const(d, 0),
abs(multiply(x1, floor_divide(x2, d))))
@_wraps(np.extract)
def extract(condition, arr):
return compress(ravel(condition), ravel(arr))
@_wraps(np.compress, skip_params=['out'])
def compress(condition, a, axis: Optional[int] = None, out=None):
_check_arraylike("compress", condition, a)
if out is not None:
raise NotImplementedError("The 'out' argument to jnp.compress is not supported.")
if ndim(condition) != 1:
raise ValueError("condition must be a 1D array")
condition = asarray(condition).astype(bool)
if axis is None:
axis = 0
a = ravel(a)
else:
a = moveaxis(a, axis, 0)
condition, extra = condition[:a.shape[0]], condition[a.shape[0]:]
if any(extra):
raise ValueError("condition contains entries that are out of bounds")
a = a[:condition.shape[0]]
return moveaxis(a[condition], 0, axis)
@_wraps(np.cov)
@partial(jit, static_argnames=('rowvar', 'bias', 'ddof'))
def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
aweights=None):
if y is not None:
m, y = _promote_args_inexact("cov", m, y)
if y.ndim > 2:
raise ValueError("y has more than 2 dimensions")
else:
m, = _promote_args_inexact("cov", m)
if m.ndim > 2:
raise ValueError("m has more than 2 dimensions") # same as numpy error
X = atleast_2d(m)
if not rowvar and X.shape[0] != 1:
X = X.T
if X.shape[0] == 0:
return array([]).reshape(0, 0)
if y is not None:
y = atleast_2d(y)
if not rowvar and y.shape[0] != 1:
y = y.T
X = concatenate((X, y), axis=0)
if ddof is None:
ddof = 1 if bias == 0 else 0
w = None
if fweights is not None:
_check_arraylike("cov", fweights)
if ndim(fweights) > 1:
raise RuntimeError("cannot handle multidimensional fweights")
if shape(fweights)[0] != X.shape[1]:
raise RuntimeError("incompatible numbers of samples and fweights")
if not issubdtype(_dtype(fweights), integer):
raise TypeError("fweights must be integer.")
# Ensure positive fweights; note that numpy raises an error on negative fweights.
w = asarray(abs(fweights))
if aweights is not None:
_check_arraylike("cov", aweights)
if ndim(aweights) > 1:
raise RuntimeError("cannot handle multidimensional aweights")
if shape(aweights)[0] != X.shape[1]:
raise RuntimeError("incompatible numbers of samples and aweights")
# Ensure positive aweights: note that numpy raises an error for negative aweights.
aweights = abs(aweights)
w = aweights if w is None else w * aweights
avg, w_sum = average(X, axis=1, weights=w, returned=True)
w_sum = w_sum[0]
if w is None:
f = X.shape[1] - ddof
elif ddof == 0:
f = w_sum
elif aweights is None:
f = w_sum - ddof
else:
f = w_sum - ddof * sum(w * aweights) / w_sum
X = X - avg[:, None]
X_T = X.T if w is None else (X * lax.broadcast_to_rank(w, X.ndim)).T
return true_divide(dot(X, X_T.conj()), f).squeeze()
@_wraps(np.corrcoef)
@partial(jit, static_argnames=('rowvar',))
def corrcoef(x, y=None, rowvar=True):
_check_arraylike("corrcoef", x)
c = cov(x, y, rowvar)
if len(shape(c)) == 0:
# scalar - this should yield nan for values (nan/nan, inf/inf, 0/0), 1 otherwise
return divide(c, c)
d = diag(c)
stddev = sqrt(real(d))
c = divide(c, stddev[:,None])
c = divide(c, stddev[None,:])
real_part = clip(real(c), -1, 1)
if iscomplexobj(c):
complex_part = clip(imag(c), -1, 1)
c = lax.complex(real_part, complex_part)
else:
c = real_part
return c
@_wraps(np.quantile, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'interpolation',
'keepdims', 'method'))
def quantile(a, q, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
overwrite_input=False, method="linear", keepdims=False,
interpolation=None):
_check_arraylike("quantile", a, q)
if overwrite_input or out is not None:
msg = ("jax.numpy.quantile does not support overwrite_input=True or "
"out != None")
raise ValueError(msg)
if interpolation is not None:
warnings.warn("The interpolation= argument to 'quantile' is deprecated. "
"Use 'method=' instead.", DeprecationWarning)
return _quantile(a, q, axis, interpolation or method, keepdims, False)
@_wraps(np.nanquantile, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'interpolation',
'keepdims', 'method'))
def nanquantile(a, q, axis: Optional[Union[int, Tuple[int, ...]]] = None,
out=None, overwrite_input=False, method="linear",
keepdims=False, interpolation=None):
_check_arraylike("nanquantile", a, q)
if overwrite_input or out is not None:
msg = ("jax.numpy.nanquantile does not support overwrite_input=True or "
"out != None")
raise ValueError(msg)
if interpolation is not None:
warnings.warn("The interpolation= argument to 'nanquantile' is deprecated. "
"Use 'method=' instead.", DeprecationWarning)
return _quantile(a, q, axis, interpolation or method, keepdims, True)
def _quantile(a, q, axis, interpolation, keepdims, squash_nans):
if interpolation not in ["linear", "lower", "higher", "midpoint", "nearest"]:
raise ValueError("interpolation can only be 'linear', 'lower', 'higher', "
"'midpoint', or 'nearest'")
a, q = _promote_dtypes_inexact(a, q)
if issubdtype(a.dtype, np.complexfloating):
raise ValueError("quantile does not support complex input, as the operation is poorly defined.")
if axis is None:
a = ravel(a)
axis = 0
elif isinstance(axis, tuple):
raise NotImplementedError("Tuple values for axis are not implemented")
else:
axis = _canonicalize_axis(axis, ndim(a))
q_shape = shape(q)
q_ndim = ndim(q)
if q_ndim > 1:
raise ValueError("q must be have rank <= 1, got shape {}".format(shape(q)))
a_shape = shape(a)
if squash_nans:
a = where(isnan(a), nan, a) # Ensure nans are positive so they sort to the end.
a = lax.sort(a, dimension=axis)
counts = sum(logical_not(isnan(a)), axis=axis, dtype=q.dtype,
keepdims=keepdims)
shape_after_reduction = counts.shape
q = lax.expand_dims(
q, tuple(range(q_ndim, len(shape_after_reduction) + q_ndim)))
counts = lax.expand_dims(counts, tuple(range(q_ndim)))
q = lax.mul(q, lax.sub(counts, _constant_like(q, 1)))
low = lax.floor(q)
high = lax.ceil(q)
high_weight = lax.sub(q, low)
low_weight = lax.sub(_constant_like(high_weight, 1), high_weight)
low = lax.max(_constant_like(low, 0), lax.min(low, counts - 1))
high = lax.max(_constant_like(high, 0), lax.min(high, counts - 1))
low = lax.convert_element_type(low, int64)
high = lax.convert_element_type(high, int64)
out_shape = q_shape + shape_after_reduction
index = [lax.broadcasted_iota(int64, out_shape, dim + q_ndim)
for dim in range(len(shape_after_reduction))]
if keepdims:
index[axis] = low
else:
index.insert(axis, low)
low_value = a[tuple(index)]
index[axis] = high
high_value = a[tuple(index)]
else:
a = where(any(isnan(a), axis=axis, keepdims=True), nan, a)
a = lax.sort(a, dimension=axis)
n = a_shape[axis]
q = lax.mul(q, _constant_like(q, n - 1))
low = lax.floor(q)
high = lax.ceil(q)
high_weight = lax.sub(q, low)
low_weight = lax.sub(_constant_like(high_weight, 1), high_weight)
low = lax.clamp(_constant_like(low, 0), low, _constant_like(low, n - 1))
high = lax.clamp(_constant_like(high, 0), high, _constant_like(high, n - 1))
low = lax.convert_element_type(low, int64)
high = lax.convert_element_type(high, int64)
slice_sizes = list(a_shape)
slice_sizes[axis] = 1
dnums = lax.GatherDimensionNumbers(
offset_dims=tuple(range(
q_ndim,
len(a_shape) + q_ndim if keepdims else len(a_shape) + q_ndim - 1)),
collapsed_slice_dims=() if keepdims else (axis,),
start_index_map=(axis,))
low_value = lax.gather(a, low[..., None], dimension_numbers=dnums,
slice_sizes=slice_sizes)
high_value = lax.gather(a, high[..., None], dimension_numbers=dnums,
slice_sizes=slice_sizes)
if q_ndim == 1:
low_weight = lax.broadcast_in_dim(low_weight, low_value.shape,
broadcast_dimensions=(0,))
high_weight = lax.broadcast_in_dim(high_weight, high_value.shape,
broadcast_dimensions=(0,))
if interpolation == "linear":
result = lax.add(lax.mul(low_value.astype(q.dtype), low_weight),
lax.mul(high_value.astype(q.dtype), high_weight))
elif interpolation == "lower":
result = low_value
elif interpolation == "higher":
result = high_value
elif interpolation == "nearest":
pred = lax.le(high_weight, _constant_like(high_weight, 0.5))
result = lax.select(pred, low_value, high_value)
elif interpolation == "midpoint":
result = lax.mul(lax.add(low_value, high_value), _constant_like(low_value, 0.5))
else:
raise ValueError(f"interpolation={interpolation!r} not recognized")
return lax.convert_element_type(result, a.dtype)
@partial(vectorize, excluded={0, 2})
def _searchsorted(a, v, side):
if len(a) == 0:
return 0
op = _sort_le_comparator if side == 'left' else _sort_lt_comparator
a, v = _promote_dtypes(a, v)
def body_fun(i, state):
low, high = state
mid = (low + high) // 2
go_left = op(v, a[mid])
return (where(go_left, low, mid), where(go_left, mid, high))
n_levels = int(np.ceil(np.log2(len(a) + 1)))
return lax.fori_loop(0, n_levels, body_fun, (0, len(a)))[1]
@_wraps(np.searchsorted, skip_params=['sorter'])
@partial(jit, static_argnames=('side', 'sorter'))
def searchsorted(a, v, side='left', sorter=None):
_check_arraylike("searchsorted", a, v)
if side not in ['left', 'right']:
raise ValueError(f"{side!r} is an invalid value for keyword 'side'")
if sorter is not None:
raise NotImplementedError("sorter is not implemented")
if ndim(a) != 1:
raise ValueError("a should be 1-dimensional")
return _searchsorted(a, v, side)
@_wraps(np.digitize)
@partial(jit, static_argnames=('right',))
def digitize(x, bins, right=False):
_check_arraylike("digitize", x, bins)
right = core.concrete_or_error(bool, right, "right argument of jnp.digitize()")
if ndim(bins) != 1:
raise ValueError(f"digitize: bins must be a 1-dimensional array; got bins={bins}")
if len(bins) == 0:
return zeros(x, dtype=dtypes.canonicalize_dtype(int_))
side = 'right' if not right else 'left'
return where(
bins[-1] >= bins[0],
searchsorted(bins, x, side=side),
len(bins) - searchsorted(bins[::-1], x, side=side)
)
_PIECEWISE_DOC = """\
Unlike `np.piecewise`, :py:func:`jax.numpy.piecewise` requires functions in
`funclist` to be traceable by JAX, as it is implemented via :func:`jax.lax.switch`.
See the :func:`jax.lax.switch` documentation for more information.
"""
@_wraps(np.piecewise, lax_description=_PIECEWISE_DOC)
def piecewise(x, condlist, funclist, *args, **kw):
_check_arraylike("piecewise", x)
condlist = array(condlist, dtype=bool_)
nc, nf = len(condlist), len(funclist)
if nf == nc + 1:
funclist = funclist[-1:] + funclist[:-1]
elif nf == nc:
funclist = [0] + list(funclist)
else:
raise ValueError(f"with {nc} condition(s), either {nc} or {nc+1} functions are expected; got {nf}")
consts = {i: c for i, c in enumerate(funclist) if not callable(c)}
funcs = {i: f for i, f in enumerate(funclist) if callable(f)}
return _piecewise(x, condlist, consts,
frozenset(funcs.items()), # dict is not hashable.
*args, **kw)
@partial(jit, static_argnames=['funcs'])
def _piecewise(x, condlist, consts, funcs, *args, **kw):
funcs = dict(funcs)
funclist = [consts.get(i, funcs.get(i)) for i in range(len(condlist) + 1)]
indices = argmax(cumsum(concatenate([zeros_like(condlist[:1]), condlist], 0), 0), 0)
dtype = _dtype(x)
def _call(f):
return lambda x: f(x, *args, **kw).astype(dtype)
def _const(v):
return lambda x: array(v, dtype=dtype)
funclist = [_call(f) if callable(f) else _const(f) for f in funclist]
return vectorize(lax.switch, excluded=(1,))(indices, funclist, x)
@_wraps(np.percentile, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'interpolation',
'keepdims', 'method'))
def percentile(a, q, axis: Optional[Union[int, Tuple[int, ...]]] = None,
out=None, overwrite_input=False, method="linear",
keepdims=False, interpolation=None):
_check_arraylike("percentile", a, q)
a, q = _promote_dtypes_inexact(a, q)
q = true_divide(q, 100.0)
return quantile(a, q, axis=axis, out=out, overwrite_input=overwrite_input,
interpolation=interpolation, method=method, keepdims=keepdims)
@_wraps(np.nanpercentile, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'interpolation',
'keepdims', 'method'))
def nanpercentile(a, q, axis: Optional[Union[int, Tuple[int, ...]]] = None,
out=None, overwrite_input=False, method="linear",
keepdims=False, interpolation=None):
_check_arraylike("nanpercentile", a, q)
q = true_divide(q, float32(100.0))
return nanquantile(a, q, axis=axis, out=out, overwrite_input=overwrite_input,
interpolation=interpolation, method=method,
keepdims=keepdims)
@_wraps(np.median, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'keepdims'))
def median(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
overwrite_input=False, keepdims=False):
_check_arraylike("median", a)
return quantile(a, 0.5, axis=axis, out=out, overwrite_input=overwrite_input,
keepdims=keepdims, method='midpoint')
@_wraps(np.nanmedian, skip_params=['out', 'overwrite_input'])
@partial(jit, static_argnames=('axis', 'overwrite_input', 'keepdims'))
def nanmedian(a, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
overwrite_input=False, keepdims=False):
_check_arraylike("nanmedian", a)
return nanquantile(a, 0.5, axis=axis, out=out,
overwrite_input=overwrite_input, keepdims=keepdims,
method='midpoint')
def _astype(arr, dtype):
if dtype is None:
dtype = dtypes.canonicalize_dtype(float_)
lax._check_user_dtype_supported(dtype, "astype")
return lax.convert_element_type(arr, dtype)
def _nbytes(arr):
return size(arr) * _dtype(arr).itemsize
def _clip(number, min=None, max=None, out=None, *, a_min=None, a_max=None):
# ndarray.clip has a slightly different API from clip (min -> a_min, max -> a_max)
# TODO: remove after deprecation window
if a_min is not None or a_max is not None:
warnings.warn('`a_min` and `a_max` keyword arguments to ndarray.clip are deprecated '
'in favor of `min` and `max` for compatibility with numpy. '
'They will be removed in JAX 0.22.2', FutureWarning)
if min is None and a_min is not None:
min = a_min
if max is None and a_max is not None:
max = a_max
return clip(number, a_min=min, a_max=max, out=out)
def _view(arr, dtype=None, type=None):
lax._check_user_dtype_supported(dtype, "view")
if type is not None:
raise NotImplementedError("`type` argument of array.view()")
if dtype is None:
return arr
arr_dtype = _dtype(arr)
if arr_dtype == dtype:
return arr
# bool is implemented as lax:PRED, which is not compatible with lax.bitcast_convert_type.
# We work around this by casting bool to uint8.
if arr_dtype == bool_:
arr = arr.astype(uint8)
nbits_in = 8 * arr_dtype.itemsize
nbits_out = 8 * np.dtype(dtype).itemsize
if nbits_in == nbits_out:
if dtype == bool_:
return lax.bitcast_convert_type(arr, uint8).astype(dtype)
return lax.bitcast_convert_type(arr, dtype)
if nbits_out > nbits_in and (shape(arr)[-1] * nbits_in) % nbits_out != 0:
raise ValueError("When changing to a larger dtype, its size must be a divisor "
"of the total size in bytes of the last axis of the array.")
byte_dtypes = {8: uint8, 16: uint16, 32: uint32, 64: uint64}
if nbits_in not in byte_dtypes:
raise NotImplementedError(f"arr.view() for arr.dtype={arr_dtype}")
if nbits_out not in byte_dtypes:
raise NotImplementedError(f"arr.view(dtype) for dtype={dtype}")
dt_in = byte_dtypes[nbits_in]
dt_out = byte_dtypes[nbits_out]
arr_bytes = lax.bitcast_convert_type(arr, dt_in)
if nbits_in < nbits_out:
arr_bytes = arr_bytes.reshape(arr.shape[:-1] + (-1, nbits_out // nbits_in)).astype(dt_out)
shifts = expand_dims(arange(0, nbits_out, nbits_in, dtype=dt_out), tuple(range(arr_bytes.ndim - 1)))
arr_bytes = (arr_bytes << shifts).sum(-1).astype(dt_out)
else:
shifts = lax.expand_dims(arange(0, nbits_in, nbits_out, dtype=dt_in), tuple(range(arr_bytes.ndim)))
arr_bytes = ((arr_bytes[..., newaxis] >> shifts) & iinfo(dt_out).max).astype(dt_out)
arr_bytes = arr_bytes.reshape(arr_bytes.shape[:-2] + (-1,))
if dtype == bool_:
return lax.bitcast_convert_type(arr_bytes, uint8).astype(dtype)
return lax.bitcast_convert_type(arr_bytes, dtype)
### track unimplemented functions
_NOT_IMPLEMENTED_DESC = """
*** This function is not yet implemented by jax.numpy, and will raise NotImplementedError ***
"""
def _not_implemented(fun):
@_wraps(fun, update_doc=False, lax_description=_NOT_IMPLEMENTED_DESC)
def wrapped(*args, **kwargs):
msg = "Numpy function {} not yet implemented"
raise NotImplementedError(msg.format(fun))
return wrapped
### add method and operator overloads to arraylike classes
# We add operator overloads to DeviceArray and ShapedArray. These method and
# operator overloads mainly just forward calls to the corresponding lax_numpy
# functions, which can themselves handle instances from any of these classes.
_scalar_types = (int, float, complex, np.generic)
_accepted_binop_types = (int, float, complex, np.generic, np.ndarray, ndarray)
def _defer_to_unrecognized_arg(binary_op):
# Ensure that other array types have the chance to override arithmetic.
def deferring_binary_op(self, other):
if not isinstance(other, _accepted_binop_types):
return NotImplemented
return binary_op(self, other)
return deferring_binary_op
def _swap_args(f):
return lambda x, y: f(y, x)
def _unimplemented_setitem(self, i, x):
msg = ("'{}' object does not support item assignment. JAX arrays are "
"immutable. Instead of ``x[idx] = y``, use ``x = x.at[idx].set(y)`` "
"or another .at[] method: "
"https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.ndarray.at.html")
raise TypeError(msg.format(type(self)))
def _operator_round(number, ndigits=None):
out = round(number, decimals=ndigits or 0)
# If `ndigits` is None, for a builtin float round(7.5) returns an integer.
return out.astype(int) if ndigits is None else out
_operators = {
"getitem": _rewriting_take,
"setitem": _unimplemented_setitem,
"neg": negative,
"pos": positive,
"eq": _defer_to_unrecognized_arg(equal),
"ne": _defer_to_unrecognized_arg(not_equal),
"lt": _defer_to_unrecognized_arg(less),
"le": _defer_to_unrecognized_arg(less_equal),
"gt": _defer_to_unrecognized_arg(greater),
"ge": _defer_to_unrecognized_arg(greater_equal),
"abs": abs,
"add": _defer_to_unrecognized_arg(add),
"radd": _defer_to_unrecognized_arg(add),
"sub": _defer_to_unrecognized_arg(subtract),
"rsub": _defer_to_unrecognized_arg(_swap_args(subtract)),
"mul": _defer_to_unrecognized_arg(multiply),
"rmul": _defer_to_unrecognized_arg(multiply),
"div": _defer_to_unrecognized_arg(divide),
"rdiv": _defer_to_unrecognized_arg(_swap_args(divide)),
"truediv": _defer_to_unrecognized_arg(true_divide),
"rtruediv": _defer_to_unrecognized_arg(_swap_args(true_divide)),
"floordiv": _defer_to_unrecognized_arg(floor_divide),
"rfloordiv": _defer_to_unrecognized_arg(_swap_args(floor_divide)),
"divmod": _defer_to_unrecognized_arg(divmod),
"rdivmod": _defer_to_unrecognized_arg(_swap_args(divmod)),
"mod": _defer_to_unrecognized_arg(mod),
"rmod": _defer_to_unrecognized_arg(_swap_args(mod)),
"pow": _defer_to_unrecognized_arg(power),
"rpow": _defer_to_unrecognized_arg(_swap_args(power)),
"matmul": _defer_to_unrecognized_arg(matmul),
"rmatmul": _defer_to_unrecognized_arg(_swap_args(matmul)),
"and": _defer_to_unrecognized_arg(bitwise_and),
"rand": _defer_to_unrecognized_arg(bitwise_and),
"or": _defer_to_unrecognized_arg(bitwise_or),
"ror": _defer_to_unrecognized_arg(bitwise_or),
"xor": _defer_to_unrecognized_arg(bitwise_xor),
"rxor": _defer_to_unrecognized_arg(bitwise_xor),
"invert": bitwise_not,
"lshift": _defer_to_unrecognized_arg(left_shift),
"rshift": _defer_to_unrecognized_arg(right_shift),
"rlshift": _defer_to_unrecognized_arg(_swap_args(left_shift)),
"rrshift": _defer_to_unrecognized_arg(_swap_args(right_shift)),
"round": _operator_round,
}
# These numpy.ndarray methods are just refs to an equivalent numpy function
_nondiff_methods = ["all", "any", "argmax", "argmin", "argpartition", "argsort",
"nonzero", "searchsorted", "round"]
_diff_methods = ["choose", "conj", "conjugate", "cumprod", "cumsum",
"diagonal", "dot", "max", "mean", "min", "prod", "ptp",
"ravel", "repeat", "sort", "squeeze", "std", "sum",
"swapaxes", "take", "tile", "trace", "var"]
# These methods are mentioned explicitly by nondiff_methods, so we create
# _not_implemented implementations of them here rather than in __init__.py.
# TODO(phawkins): implement these.
argpartition = _not_implemented(np.argpartition)
_NOT_IMPLEMENTED = ['argpartition']
# Experimental support for NumPy's module dispatch with NEP-37.
# Currently requires https://github.com/seberg/numpy-dispatch
_JAX_ARRAY_TYPES = (device_array.DeviceArray, core.Tracer)
_HANDLED_ARRAY_TYPES = _JAX_ARRAY_TYPES + (np.ndarray,)
def __array_module__(self, types):
if builtins.all(issubclass(t, _HANDLED_ARRAY_TYPES) for t in types):
return jax.numpy
else:
return NotImplemented
def _compress_method(a, condition, axis=None, out=None):
return compress(condition, a, axis, out)
@partial(jit, static_argnums=(1,2,3))
def _multi_slice(arr,
start_indices: Tuple[Tuple[int, ...]],
limit_indices: Tuple[Tuple[int, ...]],
removed_dims: Tuple[Tuple[int, ...]]):
"""Extracts multiple slices from `arr`.
This is used to shard DeviceArray arguments to pmap. It's implemented as a
DeviceArray method here to avoid circular imports.
"""
results = []
for starts, limits, removed in safe_zip(start_indices, limit_indices, removed_dims):
sliced = lax.slice(arr, starts, limits)
if removed:
sliced = lax.squeeze(sliced, removed)
results.append(sliced)
return results
# The next two functions are related to iter(device_array), implemented here to
# avoid circular imports.
@jit
def _unstack(x):
return [lax.index_in_dim(x, i, keepdims=False) for i in range(x.shape[0])]
setattr(device_array.DeviceArray, "_unstack", _unstack)
def _chunk_iter(x, size):
if size > x.shape[0]:
yield x
else:
num_chunks, tail = divmod(x.shape[0], size)
for i in range(num_chunks):
yield lax.dynamic_slice_in_dim(x, i * size, size)
if tail:
yield lax.dynamic_slice_in_dim(x, num_chunks * size, tail)
setattr(device_array.DeviceArray, "_chunk_iter", _chunk_iter)
# Syntactic sugar for scatter operations.
class _IndexUpdateHelper:
# Note: this docstring will appear as the docstring for the `at` property.
"""Helper property for index update functionality.
The ``at`` property provides a functionally pure equivalent of in-place
array modificatons.
In particular:
============================== ================================
Alternate syntax Equivalent In-place expression
============================== ================================
``x = x.at[idx].set(y)`` ``x[idx] = y``
``x = x.at[idx].add(y)`` ``x[idx] += y``
``x = x.at[idx].multiply(y)`` ``x[idx] *= y``
``x = x.at[idx].divide(y)`` ``x[idx] /= y``
``x = x.at[idx].power(y)`` ``x[idx] **= y``
``x = x.at[idx].min(y)`` ``x[idx] = minimum(x[idx], y)``
``x = x.at[idx].max(y)`` ``x[idx] = maximum(x[idx], y)``
``x = x.at[idx].get()`` ``x = x[idx]``
============================== ================================
None of the ``x.at`` expressions modify the original ``x``; instead they return
a modified copy of ``x``. However, inside a :py:func:`~jax.jit` compiled function,
expressions like :code:`x = x.at[idx].set(y)` are guaranteed to be applied in-place.
Unlike NumPy in-place operations such as :code:`x[idx] += y`, if multiple
indices refer to the same location, all updates will be applied (NumPy would
only apply the last update, rather than applying all updates.) The order
in which conflicting updates are applied is implementation-defined and may be
nondeterministic (e.g., due to concurrency on some hardware platforms).
By default, JAX assumes that all indices are in-bounds. There is experimental
support for giving more precise semantics to out-of-bounds indexed accesses,
via the ``mode`` parameter (see below).
Arguments
---------
mode : str
Specify out-of-bound indexing mode. Options are:
- ``"promise_in_bounds"``: (default) The user promises that indices are in bounds.
No additional checking will be performed. In practice, this means that
out-of-bounds indices in ``get()`` will be clipped, and out-of-bounds indices
in ``set()``, ``add()``, etc. will be dropped.
- ``"clip"``: clamp out of bounds indices into valid range.
- ``"drop"``: ignore out-of-bound indices.
- ``"fill"``: alias for ``"drop"``. For `get()`, the optional ``fill_value``
argument specifies the value that will be returned.
indices_are_sorted : bool
If True, the implementation will assume that the indices passed to ``at[]``
are sorted in ascending order, which can lead to more efficient execution
on some backends.
unique_indices : bool
If True, the implementation will assume that the indices passed to ``at[]``
are unique, which can result in more efficient execution on some backends.
fill_value : Any
Only applies to the ``get()`` method: the fill value to return for out-of-bounds
slices when `mode` is ``'fill'``. Ignored otherwise. Defaults to ``NaN`` for
inexact types, the largest negative value for signed types, the largest positive
value for unsigned types, and ``True`` for booleans.
Examples
--------
>>> x = jnp.arange(5.0)
>>> x
DeviceArray([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[2].add(10)
DeviceArray([ 0., 1., 12., 3., 4.], dtype=float32)
>>> x.at[10].add(10) # out-of-bounds indices are ignored
DeviceArray([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[20].add(10, mode='clip')
DeviceArray([ 0., 1., 2., 3., 14.], dtype=float32)
>>> x.at[2].get()
DeviceArray(2., dtype=float32)
>>> x.at[20].get() # out-of-bounds indices clipped
DeviceArray(4., dtype=float32)
>>> x.at[20].get(mode='fill') # out-of-bounds indices filled with NaN
DeviceArray(nan, dtype=float32)
>>> x.at[20].get(mode='fill', fill_value=-1) # custom fill value
DeviceArray(-1., dtype=float32)
"""
__slots__ = ("array",)
def __init__(self, array):
self.array = array
def __getitem__(self, index):
return _IndexUpdateRef(self.array, index)
def __repr__(self):
return f"_IndexUpdateHelper({repr(self.array)})"
ndarray.at.__doc__ = _IndexUpdateHelper.__doc__
_power_fn = power
_divide_fn = divide
class _IndexUpdateRef:
"""Helper object to call indexed update functions for an (advanced) index.
This object references a source array and a specific indexer into that array.
Methods on this object return copies of the source array that have been
modified at the positions specified by the indexer.
"""
__slots__ = ("array", "index")
def __init__(self, array, index):
self.array = array
self.index = index
def __repr__(self):
return f"_IndexUpdateRef({repr(self.array)}, {repr(self.index)})"
def get(self, indices_are_sorted=False, unique_indices=False,
mode=None, fill_value=None):
"""Equivalent to ``x[idx]``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexing <numpy.doc.indexing>` ``x[idx]``. This function differs from
the usual array indexing syntax in that it allows additional keyword
arguments ``indices_are_sorted`` and ``unique_indices`` to be passed.
See :mod:`jax.ops` for details.
"""
return _rewriting_take(self.array, self.index,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode,
fill_value=fill_value)
def set(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] = y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] = y``.
See :mod:`jax.ops` for details.
"""
return scatter._scatter_update(self.array, self.index, values, lax.scatter,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode)
def add(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] += y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] += y``.
See :mod:`jax.ops` for details.
"""
return scatter._scatter_update(self.array, self.index, values,
lax.scatter_add,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode)
def multiply(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] *= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] *= y``.
See :mod:`jax.ops` for details.
"""
return scatter._scatter_update(self.array, self.index, values,
lax.scatter_mul,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices,
mode=mode)
mul = multiply
def divide(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] /= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] /= y``.
See :mod:`jax.ops` for details.
"""
return _divide_fn(
self.array,
scatter._scatter_update(ones_like(self.array), self.index, values,
lax.scatter_mul,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode))
def power(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] **= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] **= y``.
See :mod:`jax.ops` for details.
"""
return _power_fn(
self.array,
scatter._scatter_update(ones_like(self.array), self.index, values,
lax.scatter_mul,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode))
def min(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] = minimum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>`
``x[idx] = minimum(x[idx], y)``.
See :mod:`jax.ops` for details.
"""
return scatter._scatter_update(self.array, self.index, values,
lax.scatter_min,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode)
def max(self, values, indices_are_sorted=False, unique_indices=False,
mode=None):
"""Pure equivalent of ``x[idx] = maximum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>`
``x[idx] = maximum(x[idx], y)``.
See :mod:`jax.ops` for details.
"""
return scatter._scatter_update(self.array, self.index, values,
lax.scatter_max,
indices_are_sorted=indices_are_sorted,
unique_indices=unique_indices, mode=mode)
def _set_shaped_array_attributes(shaped_array):
# Set up operator, method, and property forwarding on Tracer instances
# containing
# ShapedArray avals by following the forwarding conventions for Tracer.
# Forward operators using a single-underscore-prefix naming convention:
for operator_name, function in _operators.items():
setattr(shaped_array, "_{}".format(operator_name), staticmethod(function))
# Forward methods and properties using core.{aval_method, aval_property}:
for method_name in _nondiff_methods + _diff_methods:
setattr(shaped_array, method_name, core.aval_method(globals()[method_name]))
setattr(shaped_array, "reshape", core.aval_method(_reshape))
setattr(shaped_array, "transpose", core.aval_method(_transpose))
setattr(shaped_array, "flatten", core.aval_method(ravel))
setattr(shaped_array, "T", core.aval_property(transpose))
setattr(shaped_array, "real", core.aval_property(real))
setattr(shaped_array, "imag", core.aval_property(imag))
setattr(shaped_array, "astype", core.aval_method(_astype))
setattr(shaped_array, "view", core.aval_method(_view))
setattr(shaped_array, "nbytes", core.aval_property(_nbytes))
setattr(shaped_array, "clip", core.aval_method(_clip))
setattr(shaped_array, "_array_module", staticmethod(__array_module__))
setattr(shaped_array, "broadcast", core.aval_method(lax.broadcast))
setattr(shaped_array, "broadcast_in_dim", core.aval_method(lax.broadcast_in_dim))
setattr(shaped_array, "split", core.aval_method(split))
setattr(shaped_array, "compress", _compress_method)
setattr(shaped_array, "at", core.aval_property(_IndexUpdateHelper))
setattr(shaped_array, "item", core.aval_method(device_array.DeviceArray.item))
_set_shaped_array_attributes(ShapedArray)
def _set_device_array_base_attributes(device_array):
# Forward operators, methods, and properties on DeviceArray to lax_numpy
# functions (with no Tracers involved; this forwarding is direct)
for operator_name, function in _operators.items():
setattr(device_array, "__{}__".format(operator_name), function)
for method_name in _nondiff_methods + _diff_methods:
setattr(device_array, method_name, globals()[method_name])
setattr(device_array, "reshape", _reshape)
setattr(device_array, "transpose", _transpose)
setattr(device_array, "flatten", ravel)
setattr(device_array, "T", property(transpose))
setattr(device_array, "real", property(real))
setattr(device_array, "imag", property(imag))
setattr(device_array, "astype", _astype)
setattr(device_array, "view", _view)
setattr(device_array, "nbytes", property(_nbytes))
setattr(device_array, "clip", _clip)
_set_device_array_base_attributes(device_array.DeviceArray)
def _set_device_array_attributes(device_array):
setattr(device_array, "__array_module__", __array_module__)
# Extra methods that are handy
setattr(device_array, "broadcast", lax.broadcast)
setattr(device_array, "broadcast_in_dim", lax.broadcast_in_dim)
setattr(device_array, "split", split)
setattr(device_array, "compress", _compress_method)
setattr(device_array, "_multi_slice", _multi_slice)
setattr(device_array, "at", property(_IndexUpdateHelper))
for t in device_array.device_array_types:
_set_device_array_attributes(t)
_set_device_array_attributes(pxla._ShardedDeviceArray)
_set_device_array_attributes(pxla.pmap_lib.ShardedDeviceArray)