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b3a02e1b62
This allows the JIT cache to work properly with ufunc methods, because bound methods are created with a new ID each time.
277 lines
11 KiB
Python
277 lines
11 KiB
Python
# Copyright 2023 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tools to create numpy-style ufuncs."""
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_AT_INPLACE_WARNING = """\
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Because JAX arrays are immutable, jnp.ufunc.at() cannot operate inplace like
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np.ufunc.at(). Instead, you can pass inplace=False and capture the result; e.g.
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>>> arr = jnp.add.at(arr, ind, val, inplace=False)
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"""
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from functools import partial
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import operator
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import jax
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from jax._src.lax import lax as lax_internal
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from jax._src.numpy.lax_numpy import _eliminate_deprecated_list_indexing, append, take
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from jax._src.numpy.reductions import _moveaxis
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from jax._src.numpy.util import _wraps, check_arraylike, _broadcast_to, _where
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from jax._src.numpy.vectorize import vectorize
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from jax._src.util import canonicalize_axis
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import numpy as np
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class ufunc:
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"""Functions that operate element-by-element on whole arrays.
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This is a class for LAX-backed implementations of numpy ufuncs.
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"""
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def __init__(self, func, /, nin, nout, *, name=None, nargs=None, identity=None):
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# We want ufunc instances to work properly when marked as static,
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# and for this reason it's important that their properties not be
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# mutated. We prevent this by storing them in a dunder attribute,
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# and accessing them via read-only properties.
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self.__name__ = name or func.__name__
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self.__static_props = {
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'func': func,
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'call': vectorize(func),
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'nin': operator.index(nin),
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'nout': operator.index(nout),
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'nargs': operator.index(nargs or nin),
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'identity': identity
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}
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_func = property(lambda self: self.__static_props['func'])
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_call = property(lambda self: self.__static_props['call'])
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nin = property(lambda self: self.__static_props['nin'])
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nout = property(lambda self: self.__static_props['nout'])
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nargs = property(lambda self: self.__static_props['nargs'])
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identity = property(lambda self: self.__static_props['identity'])
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def __hash__(self):
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# Do not include _call, because it is computed from _func.
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return hash((self._func, self.__name__, self.identity,
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self.nin, self.nout, self.nargs))
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def __eq__(self, other):
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# Do not include _call, because it is computed from _func.
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return isinstance(other, ufunc) and (
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(self._func, self.__name__, self.identity, self.nin, self.nout, self.nargs) ==
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(other._func, other.__name__, other.identity, other.nin, other.nout, other.nargs))
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def __repr__(self):
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return f"<jnp.ufunc '{self.__name__}'>"
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def __call__(self, *args, out=None, where=None, **kwargs):
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if out is not None:
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raise NotImplementedError(f"out argument of {self}")
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if where is not None:
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raise NotImplementedError(f"where argument of {self}")
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return self._call(*args, **kwargs)
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@_wraps(np.ufunc.reduce, module="numpy.ufunc")
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@partial(jax.jit, static_argnames=['self', 'axis', 'dtype', 'out', 'keepdims'])
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def reduce(self, a, axis=0, dtype=None, out=None, keepdims=False, initial=None, where=None):
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if self.nin != 2:
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raise ValueError("reduce only supported for binary ufuncs")
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if self.nout != 1:
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raise ValueError("reduce only supported for functions returning a single value")
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if out is not None:
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raise NotImplementedError(f"out argument of {self.__name__}.reduce()")
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# TODO(jakevdp): implement where.
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if where is not None:
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raise NotImplementedError(f"where argument of {self.__name__}.reduce()")
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return self._reduce_via_scan(a, axis=axis, dtype=dtype, keepdims=keepdims, initial=initial)
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def _reduce_via_scan(self, arr, axis=0, dtype=None, keepdims=False, initial=None):
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assert self.nin == 2 and self.nout == 1
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check_arraylike(f"{self.__name__}.reduce", arr)
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arr = lax_internal.asarray(arr)
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if initial is None:
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initial = self.identity
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if dtype is None:
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dtype = jax.eval_shape(self, lax_internal._one(arr), lax_internal._one(arr)).dtype
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if isinstance(axis, tuple):
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axis = tuple(canonicalize_axis(a, arr.ndim) for a in axis)
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raise NotImplementedError("tuple of axes")
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elif axis is None:
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if keepdims:
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final_shape = (1,) * arr.ndim
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else:
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final_shape = ()
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arr = arr.ravel()
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axis = 0
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else:
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axis = canonicalize_axis(axis, arr.ndim)
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if keepdims:
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final_shape = (*arr.shape[:axis], 1, *arr.shape[axis + 1:])
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else:
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final_shape = (*arr.shape[:axis], *arr.shape[axis + 1:])
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# TODO: handle without transpose?
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if axis != 0:
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arr = _moveaxis(arr, axis, 0)
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if initial is None and arr.shape[0] == 0:
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raise ValueError("zero-size array to reduction operation {self.__name__} which has no ideneity")
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def body_fun(i, val):
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return self._call(val, arr[i].astype(dtype))
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if initial is None:
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start = 1
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initial = arr[0]
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else:
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check_arraylike(f"{self.__name__}.reduce", arr)
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start = 0
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initial = _broadcast_to(lax_internal.asarray(initial).astype(dtype), arr.shape[1:])
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result = jax.lax.fori_loop(start, arr.shape[0], body_fun, initial)
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if keepdims:
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result = result.reshape(final_shape)
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return result
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@_wraps(np.ufunc.accumulate, module="numpy.ufunc")
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@partial(jax.jit, static_argnames=['self', 'axis', 'dtype'])
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def accumulate(self, a, axis=0, dtype=None, out=None):
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if self.nin != 2:
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raise ValueError("accumulate only supported for binary ufuncs")
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if self.nout != 1:
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raise ValueError("accumulate only supported for functions returning a single value")
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if out is not None:
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raise NotImplementedError(f"out argument of {self.__name__}.accumulate()")
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return self._accumulate_via_scan(a, axis=axis, dtype=dtype)
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def _accumulate_via_scan(self, arr, axis=0, dtype=None):
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assert self.nin == 2 and self.nout == 1
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check_arraylike(f"{self.__name__}.accumulate", arr)
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arr = lax_internal.asarray(arr)
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if dtype is None:
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dtype = jax.eval_shape(self, lax_internal._one(arr), lax_internal._one(arr)).dtype
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if axis is None or isinstance(axis, tuple):
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raise ValueError("accumulate does not allow multiple axes")
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axis = canonicalize_axis(axis, np.ndim(arr))
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arr = _moveaxis(arr, axis, 0)
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def scan_fun(carry, _):
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i, x = carry
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y = _where(i == 0, arr[0].astype(dtype), self._call(x.astype(dtype), arr[i].astype(dtype)))
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return (i + 1, y), y
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_, result = jax.lax.scan(scan_fun, (0, arr[0].astype(dtype)), None, length=arr.shape[0])
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return _moveaxis(result, 0, axis)
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@_wraps(np.ufunc.accumulate, module="numpy.ufunc")
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@partial(jax.jit, static_argnums=[0], static_argnames=['inplace'])
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def at(self, a, indices, b=None, /, *, inplace=True):
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if inplace:
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raise NotImplementedError(_AT_INPLACE_WARNING)
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if b is None:
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return self._at_via_scan(a, indices)
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else:
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return self._at_via_scan(a, indices, b)
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def _at_via_scan(self, a, indices, *args):
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check_arraylike(f"{self.__name__}.at", a, *args)
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dtype = jax.eval_shape(self, lax_internal._one(a), *(lax_internal._one(arg) for arg in args)).dtype
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a = lax_internal.asarray(a).astype(dtype)
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args = tuple(lax_internal.asarray(arg).astype(dtype) for arg in args)
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indices = _eliminate_deprecated_list_indexing(indices)
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if not indices:
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return a
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shapes = [np.shape(i) for i in indices if not isinstance(i, slice)]
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shape = shapes and jax.lax.broadcast_shapes(*shapes)
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if not shape:
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return a.at[indices].set(self._call(a.at[indices].get(), *args))
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args = tuple(_broadcast_to(arg, shape).ravel() for arg in args)
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indices = [idx if isinstance(idx, slice) else _broadcast_to(idx, shape).ravel() for idx in indices]
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def scan_fun(carry, x):
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i, a = carry
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idx = tuple(ind if isinstance(ind, slice) else ind[i] for ind in indices)
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a = a.at[idx].set(self._call(a.at[idx].get(), *(arg[i] for arg in args)))
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return (i + 1, a), x
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carry, _ = jax.lax.scan(scan_fun, (0, a), None, len(indices[0]))
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return carry[1]
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@_wraps(np.ufunc.reduceat, module="numpy.ufunc")
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@partial(jax.jit, static_argnames=['self', 'axis', 'dtype'])
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def reduceat(self, a, indices, axis=0, dtype=None, out=None):
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if self.nin != 2:
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raise ValueError("reduceat only supported for binary ufuncs")
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if self.nout != 1:
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raise ValueError("reduceat only supported for functions returning a single value")
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if out is not None:
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raise NotImplementedError(f"out argument of {self.__name__}.reduceat()")
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return self._reduceat_via_scan(a, indices, axis=axis, dtype=dtype)
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def _reduceat_via_scan(self, a, indices, axis=0, dtype=None):
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check_arraylike(f"{self.__name__}.reduceat", a, indices)
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a = lax_internal.asarray(a)
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idx_tuple = _eliminate_deprecated_list_indexing(indices)
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assert len(idx_tuple) == 1
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indices = idx_tuple[0]
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if a.ndim == 0:
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raise ValueError(f"reduceat: a must have 1 or more dimension, got {a.shape=}")
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if indices.ndim != 1:
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raise ValueError(f"reduceat: indices must be one-dimensional, got {indices.shape=}")
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if dtype is None:
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dtype = a.dtype
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if axis is None or isinstance(axis, (tuple, list)):
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raise ValueError("reduceat requires a single integer axis.")
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axis = canonicalize_axis(axis, a.ndim)
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out = take(a, indices, axis=axis)
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ind = jax.lax.expand_dims(append(indices, a.shape[axis]),
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np.delete(np.arange(out.ndim), axis))
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ind_start = jax.lax.slice_in_dim(ind, 0, ind.shape[axis] - 1, axis=axis)
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ind_end = jax.lax.slice_in_dim(ind, 1, ind.shape[axis], axis=axis)
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def loop_body(i, out):
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return _where((i > ind_start) & (i < ind_end),
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self._call(out, take(a, i.reshape(1), axis=axis)),
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out)
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return jax.lax.fori_loop(0, a.shape[axis], loop_body, out)
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@_wraps(np.ufunc.outer, module="numpy.ufunc")
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@partial(jax.jit, static_argnums=[0])
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def outer(self, A, B, /, **kwargs):
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if self.nin != 2:
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raise ValueError("outer only supported for binary ufuncs")
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if self.nout != 1:
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raise ValueError("outer only supported for functions returning a single value")
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check_arraylike(f"{self.__name__}.outer", A, B)
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_ravel = lambda A: jax.lax.reshape(A, (np.size(A),))
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result = jax.vmap(jax.vmap(partial(self._call, **kwargs), (None, 0)), (0, None))(_ravel(A), _ravel(B))
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return result.reshape(*np.shape(A), *np.shape(B))
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def frompyfunc(func, /, nin, nout, *, identity=None):
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"""Create a JAX ufunc from an arbitrary JAX-compatible scalar function.
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Args:
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func : a callable that takes `nin` scalar arguments and return `nout` outputs.
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nin: integer specifying the number of scalar inputs
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nout: integer specifying the number of scalar outputs
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identity: (optional) a scalar specifying the identity of the operation, if any.
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Returns:
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wrapped : jax.numpy.ufunc wrapper of func.
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"""
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# TODO(jakevdp): use functools.wraps or similar to wrap the docstring?
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return ufunc(func, nin, nout, identity=identity)
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