mirror of
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253 lines
8.1 KiB
Python
253 lines
8.1 KiB
Python
# Copyright 2018 Google LLC
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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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"""
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Utilities for defining functions composed with transformations.
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For example,
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from jax import linear_util as lu
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wf = lu.wrap_init(f) # Produce a WrappedFun for applying transformations on `f`
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A `WrappedFun` object represents a function `f`, together with a sequence of
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nested transformations that are to be applied to the positional and keyword
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arguments at call time and function return values at return time.
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A transformation can take some static positional arguments that are given
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at the wrapping time, and may also return some auxiliary output:
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wf, aux_out_thunk = trans1(wf, static_arg)
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We can call the transformed function. First, the transformation is applied
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to the dynamic args and keyword args to produce new dynamic and keyword args.
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Then the underlying function is called and the transformation is applied to
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the results.
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If there are multiple transformations, they form a stack. The arguments are
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transformed first with the last applied transformation; the results are
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transformed first with the first applied transformation.
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res = wf.call_wrapped(dynamic_args, kwargs)
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# Now `aux_out_thunk()` is the auxiliary output.
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A transformation is written as a generator function that takes zero or more
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static positional arguments (given when the transformation is instantiated),
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along with positional and keyword arguments to be transformed.
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The generator will yield twice:
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@lu.transformation_with_aux
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def trans1(static_arg, *dynamic_args, **kwargs):
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...
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# First yield: pair of transformed (args, kwargs). Get back the results.
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results = yield (new_dynamic_args, new_kwargs)
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...
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# Second yield: pair of (transformed results, and auxiliary output)
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yield new_results, auxiliary_output
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`WrappedFun` objects explicitly represent the set of transformations so that
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they can be used as dictionary keys for memoization. `WrappedFun` objects
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compare as equal only if they compute the same function. The static and the
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dynamic positional arguments for the generators, and also the auxiliary output
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data must be immutable, because it will be stored in function memoization tables.
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"""
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from typing import Any, Tuple
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import weakref
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from .util import curry
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class StoreException(Exception): pass
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class EmptyStoreValue(object): pass
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_EMPTY_STORE_VALUE = EmptyStoreValue()
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class Store(object):
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"""Storage for a value, with checks for overwriting or reading empty store."""
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__slots__ = ("_val",)
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def __init__(self):
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self._val = _EMPTY_STORE_VALUE
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def store(self, val):
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if self._val is not _EMPTY_STORE_VALUE:
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raise StoreException("Store occupied")
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self._val = val
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@property
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def val(self):
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if not self:
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raise StoreException("Store empty")
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return self._val
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def __nonzero__(self):
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return self._val is not _EMPTY_STORE_VALUE
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__bool__ = __nonzero__
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class WrappedFun(object):
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"""Represents a function `f` to which `transforms` are to be applied.
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Arguments:
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f: the function to be transformed.
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transforms: a list of `(gen, gen_static_args)` tuples representing
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transformations to apply to `f.` Here `gen` is a generator function
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and `gen_static_args` is a tuple of static arguments for the generator. See
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description at the start of this module for the expected behavior of the
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generator.
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stores: a list of out_store for the auxiliary output of the `transforms`.
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params: extra parameters to pass as keyword arguments to `f`, along with the
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transformed keyword arguments.
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"""
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__slots__ = ("f", "transforms", "stores", "params")
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def __init__(self, f, transforms, stores, params):
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self.f = f
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self.transforms = transforms
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self.stores = stores
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self.params = params
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@property
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def __name__(self):
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return getattr(self.f, '__name__', '<unnamed wrapped function>')
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def wrap(self, gen, gen_static_args, out_store) -> 'WrappedFun':
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"""Add another transform and its store."""
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return WrappedFun(self.f, ((gen, gen_static_args),) + self.transforms,
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(out_store,) + self.stores, self.params)
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def populate_stores(self, stores):
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"""Copy the values from the `stores` into `self.stores`."""
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for self_store, other_store in zip(self.stores, stores):
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if self_store is not None:
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self_store.store(other_store.val)
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def call_wrapped(self, *args, **kwargs):
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"""Calls the underlying function, applying the transforms.
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The positional `args` and keyword `kwargs` are passed to the first
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transformation generator.
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"""
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stack = []
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for (gen, gen_static_args), out_store in zip(self.transforms, self.stores):
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gen = gen(*(gen_static_args + tuple(args)), **kwargs)
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args, kwargs = next(gen)
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stack.append((gen, out_store))
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gen = None
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ans = self.f(*args, **dict(self.params, **kwargs))
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del args
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while stack:
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gen, out_store = stack.pop()
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ans = gen.send(ans)
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if out_store is not None:
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ans, side = ans
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out_store.store(side)
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return ans
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def __repr__(self):
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def transform_to_str(x):
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i, (gen, args) = x
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return "{} : {} {}".format(i, fun_name(gen), fun_name(args))
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transformation_stack = map(transform_to_str, enumerate(self.transforms))
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return "Wrapped function:\n" + '\n'.join(transformation_stack) + '\nCore: ' + fun_name(self.f) + '\n'
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def __hash__(self):
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return hash((self.f, self.transforms, self.params))
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def __eq__(self, other):
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return (self.f == other.f and self.transforms == other.transforms and
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self.params == other.params)
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@curry
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def transformation(gen, fun: WrappedFun, *gen_static_args) -> WrappedFun:
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"""Adds one more transformation to a WrappedFun.
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Args:
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gen: the transformation generator function
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fun: a WrappedFun on which to apply the transformation
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gen_static_args: static args for the generator function
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"""
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return fun.wrap(gen, gen_static_args, None)
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@curry
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def transformation_with_aux(gen, fun: WrappedFun, *gen_static_args) -> Tuple[WrappedFun, Any]:
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"""Adds one more transformation with auxiliary output to a WrappedFun."""
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out_store = Store()
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out_thunk = lambda: out_store.val
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return fun.wrap(gen, gen_static_args, out_store), out_thunk
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def fun_name(f):
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try:
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return f.__name__
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except:
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return str(f)
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def wrap_init(f, params={}) -> WrappedFun:
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"""Wraps function `f` as a `WrappedFun`, suitable for transformation."""
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return WrappedFun(f, (), (), tuple(sorted(params.items())))
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def cache(call):
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"""Cache decorator for WrappedFun calls.
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Args:
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call: a function that takes a WrappedFun as a first argument
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Returns:
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the memoized `call` function.
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"""
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fun_caches = weakref.WeakKeyDictionary()
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def memoized_fun(fun: WrappedFun, *args):
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cache = fun_caches.setdefault(fun.f, {})
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key = (fun.transforms, fun.params, args)
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result = cache.get(key, None)
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if result is not None:
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ans, stores = result
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fun.populate_stores(stores)
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else:
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ans = call(fun, *args)
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cache[key] = (ans, fun.stores)
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return ans
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memoized_fun.cache_clear = fun_caches.clear
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return memoized_fun
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@transformation
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def hashable_partial(x, *args):
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ans = yield (x,) + args, {}
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yield ans
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def merge_linear_aux(aux1, aux2):
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try:
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out1 = aux1()
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except StoreException:
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# store 1 was not occupied, so store 2 better be
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try:
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out2 = aux2()
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except StoreException:
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raise StoreException("neither store occupied")
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else:
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return False, out2
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else:
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# store 1 was occupied, so let's check store 2 is not occupied
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try:
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out2 = aux2()
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except StoreException:
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return True, out1
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else:
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raise StoreException("both stores occupied")
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