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4236eb2b59
This change, when enabled, stages out all primitive calls in the dynamic scope of a jitted, pmapped, or control flow function, rather than only staging out based on data dependence. One improvement is that jitted functions can consume less memory, by avoiding instantiating large constants at trace time, and cause less memory fragmentation as well. It also simplifies several internals. See https://github.com/google/jax/pull/3370 fo more information.
87 lines
2.3 KiB
Python
87 lines
2.3 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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from jax import core
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from .core import (lattice_join, Primitive, Unit, unit, AbstractUnit,
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valid_jaxtype, raise_to_shaped, get_aval)
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from .tree_util import register_pytree_node
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from typing import Any, Dict
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from .util import safe_map
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Array = Any
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map = safe_map
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jaxval_adders = {}
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jaxval_adders[Unit] = lambda _, __: unit
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def add_jaxvals(x, y):
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if core.get_aval(x) is core.abstract_unit is core.get_aval(y):
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return core.unit
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else:
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return add_jaxvals_p.bind(x, y)
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add_jaxvals_p = Primitive('add_any')
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@add_jaxvals_p.def_impl
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def add_impl(xs, ys):
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return jaxval_adders[type(xs)](xs, ys)
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@add_jaxvals_p.def_abstract_eval
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def add_abstract(xs, ys):
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return lattice_join(xs, ys)
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jaxval_zeros_likers: Dict[type, Array] = {}
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def zeros_like_aval(aval):
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return aval_zeros_likers[type(aval)](aval)
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aval_zeros_likers: Dict[type, Array] = {}
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aval_zeros_likers[AbstractUnit] = lambda _: unit
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def zeros_like_jaxval(val):
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return zeros_like_p.bind(val)
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zeros_like_p = Primitive('zeros_like')
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@zeros_like_p.def_impl
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def zeros_like_impl(example):
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return jaxval_zeros_likers[type(example)](example)
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zeros_like_p.def_abstract_eval(lambda x: x)
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class Zero:
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__slots__ = ['aval']
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def __init__(self, aval):
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self.aval = aval
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def __repr__(self):
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return 'Zero({})'.format(self.aval)
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@staticmethod
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def from_value(val):
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return Zero(raise_to_shaped(get_aval(val)))
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register_pytree_node(Zero, lambda z: ((), z.aval), lambda aval, _: Zero(aval))
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def _stop_gradient_impl(x):
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if not valid_jaxtype(x):
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raise TypeError("stop_gradient only works on valid JAX arrays, but "
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f"input argument is: {x}")
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return x
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stop_gradient_p = Primitive('stop_gradient')
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stop_gradient_p.def_impl(_stop_gradient_impl)
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stop_gradient_p.def_abstract_eval(lambda x: x)
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