714 lines
33 KiB
Python
714 lines
33 KiB
Python
# Copyright 2022 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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"""Module for discharging state primitives."""
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from __future__ import annotations
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import dataclasses
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from functools import partial
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import operator
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from typing import Any, Callable, Dict, List, Optional, Protocol, Sequence, Tuple, Union
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import numpy as np
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from jax._src import api_util
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from jax._src import ad_util
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from jax._src import core
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from jax._src import linear_util as lu
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from jax._src.interpreters import partial_eval as pe
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from jax._src import source_info_util
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from jax._src import tree_util
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from jax._src.config import config
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from jax._src.interpreters import ad
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from jax._src.lax import lax
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from jax._src.lax import slicing as lax_slicing
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from jax._src.state.types import AbstractRef, RefEffect
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from jax._src.state.primitives import get_p, swap_p, addupdate_p
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from jax._src.state.utils import hoist_consts_to_refs
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from jax._src.util import (safe_map, safe_zip, split_list, weakref_lru_cache,
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partition_list, merge_lists, split_dict)
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## JAX utilities
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map, unsafe_map = safe_map, map
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zip, unsafe_zip = safe_zip, zip
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PyTreeDef = tree_util.PyTreeDef
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## Discharging state
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# Let's say we have a jaxpr that takes in `Ref`s and outputs regular JAX values
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# (`Ref`s should never be outputs from jaxprs). We'd like to convert that jaxpr
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# into a "pure" jaxpr that takes in and outputs values and no longer has the
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# `Read/Write/Accum` effects.
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def discharge_state(jaxpr: core.Jaxpr, consts: Sequence[Any], * ,
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should_discharge: Union[bool, Sequence[bool]] = True
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) -> Tuple[core.Jaxpr, List[Any]]:
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"""Converts a jaxpr that takes in `Ref`s into one that doesn't."""
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if isinstance(should_discharge, bool):
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should_discharge = [should_discharge] * len(jaxpr.invars)
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in_avals = [v.aval.inner_aval
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if type(v.aval) is AbstractRef and d
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else v.aval for v, d in zip(jaxpr.invars, should_discharge)]
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eval_jaxpr = lu.wrap_init(partial(_eval_jaxpr_discharge_state, jaxpr,
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should_discharge, consts))
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new_jaxpr, _ , new_consts = pe.trace_to_jaxpr_dynamic(eval_jaxpr, in_avals)
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return new_jaxpr, new_consts
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@dataclasses.dataclass
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class Environment:
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env: Dict[core.Var, Any]
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def read(self, v: core.Atom) -> Any:
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if type(v) is core.Literal:
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return v.val
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assert isinstance(v, core.Var)
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return self.env[v]
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def write(self, v: core.Var, val: Any) -> None:
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self.env[v] = val
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class DischargeRule(Protocol):
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def __call__(self, in_avals: Sequence[core.AbstractValue],
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out_avals: Sequence[core.AbstractValue], *args: Any,
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**params: Any) -> Tuple[Sequence[Optional[Any]], Sequence[Any]]:
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...
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_discharge_rules: dict[core.Primitive, DischargeRule] = {}
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def register_discharge_rule(prim: core.Primitive):
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def register(f: DischargeRule):
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_discharge_rules[prim] = f
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return register
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def _has_refs(eqn: core.JaxprEqn):
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return any(isinstance(v.aval, AbstractRef) for v in eqn.invars)
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def _eval_jaxpr_discharge_state(
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jaxpr: core.Jaxpr, should_discharge: Sequence[bool], consts: Sequence[Any],
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*args: Any):
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env = Environment({})
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map(env.write, jaxpr.constvars, consts)
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# Here some args may correspond to `Ref` avals but they'll be treated like
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# regular values in this interpreter.
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map(env.write, jaxpr.invars, args)
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refs_to_discharge = set(id(v.aval) for v, d
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in zip(jaxpr.invars, should_discharge) if d
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and isinstance(v.aval, AbstractRef))
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for eqn in jaxpr.eqns:
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if _has_refs(eqn) and any(id(v.aval) in refs_to_discharge
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for v in eqn.invars):
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if eqn.primitive not in _discharge_rules:
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raise NotImplementedError("No state discharge rule implemented for "
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f"primitive: {eqn.primitive}")
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invals = map(env.read, eqn.invars)
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in_avals = [v.aval for v in eqn.invars]
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out_avals = [v.aval for v in eqn.outvars]
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new_invals, ans = _discharge_rules[eqn.primitive](
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in_avals, out_avals, *invals, **eqn.params)
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for new_inval, invar in zip(new_invals, eqn.invars):
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if new_inval is not None:
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env.write(invar, new_inval) # type: ignore[arg-type]
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else:
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# Default primitive rule, similar to `core.eval_jaxpr`. Note that here
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# we assume any higher-order primitives inside of the jaxpr are *not*
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# stateful.
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subfuns, bind_params = eqn.primitive.get_bind_params(eqn.params)
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ans = eqn.primitive.bind(*subfuns, *map(env.read, eqn.invars),
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**bind_params)
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if eqn.primitive.multiple_results:
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map(env.write, eqn.outvars, ans)
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else:
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env.write(eqn.outvars[0], ans)
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# By convention, we return the outputs of the jaxpr first and then the final
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# values of the `Ref`s. Callers to this function should be able to split
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# them up by looking at `len(jaxpr.outvars)`.
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out_vals = map(env.read, jaxpr.outvars)
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ref_vals = map(
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env.read, [v for v in jaxpr.invars if id(v.aval) in refs_to_discharge])
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return out_vals + ref_vals
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@register_discharge_rule(get_p)
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def _get_discharge_rule(
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in_avals: Sequence[core.AbstractValue],
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out_avals: Sequence[core.AbstractValue], x, *non_slice_idx,
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indexed_dims: Sequence[bool]):
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del in_avals, out_avals
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y = _get_discharge(x, non_slice_idx, indexed_dims)
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return (None,) * (len(non_slice_idx) + 1), y
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def _get_discharge(x, idx, indexed_dims):
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if not any(indexed_dims):
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return x
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if all(not i.shape for i in idx):
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return _dynamic_index(x, idx, indexed_dims)
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else:
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return _prepend_gather(x, idx, indexed_dims)
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def _prepend_gather(x, idx, indexed_dims):
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indexer = _indexer(idx, indexed_dims)
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# NumPy advanced int indexing won't prepend w/ only one dim, so add dummy.
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return x[None][(np.array(0, 'int32'), *indexer)]
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def _prepend_scatter(x, idx, indexed_dims, val, *, add=False):
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indexer = _indexer(idx, indexed_dims)
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if add:
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return x[None].at[(0, *indexer)].add(val)[0]
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return x[None].at[(0, *indexer)].set(val)[0]
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def _indexer(idx, indexed_dims):
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idx_ = iter(idx)
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indexer = tuple([next(idx_) if b else slice(None) for b in indexed_dims])
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assert next(idx_, None) is None
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return indexer
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@register_discharge_rule(swap_p)
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def _swap_discharge_rule(
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in_avals: Sequence[core.AbstractValue],
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out_avals: Sequence[core.AbstractValue], x, val, *non_slice_idx,
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indexed_dims: Sequence[bool]):
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del in_avals, out_avals
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if not any(indexed_dims):
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z, x_new = x, val
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z, x_new = _swap_discharge(x, val, non_slice_idx, indexed_dims)
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return (x_new, None) + (None,) * len(non_slice_idx), z
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def _swap_discharge(x, val, idx, indexed_dims):
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if not any(indexed_dims):
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z, x_new = x, val
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elif all(not i.shape for i in idx):
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z = _dynamic_index(x, idx, indexed_dims)
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x_new = _dynamic_update_index(x, idx, val, indexed_dims)
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else:
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z = _prepend_gather(x, idx, indexed_dims)
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x_new = _prepend_scatter(x, idx, indexed_dims, val)
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return z, x_new
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@register_discharge_rule(addupdate_p)
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def _addupdate_discharge_rule(
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in_avals: Sequence[core.AbstractValue],
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out_avals: Sequence[core.AbstractValue], x, val, *non_slice_idx,
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indexed_dims: Sequence[bool]):
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del in_avals, out_avals
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ans = _addupdate_discharge(x, val, non_slice_idx, indexed_dims)
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return (ans, None) + (None,) * len(non_slice_idx), []
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def _addupdate_discharge(x, val, idx, indexed_dims):
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if not any(indexed_dims):
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return x + val
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if all(not i.shape for i in idx):
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y = val + _dynamic_index(x, idx, indexed_dims)
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return _dynamic_update_index(x, idx, y, indexed_dims)
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else:
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return _prepend_scatter(x, idx, indexed_dims, val, add=True)
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def _dynamic_index(x, idx, indexed_dims):
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assert isinstance(idx, (list, tuple)) and idx
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idx_ = iter(idx)
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starts = [next(idx_) if b else np.int32(0) for b in indexed_dims]
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assert next(idx_, None) is None
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sizes = [1 if b else size for b, size in zip(indexed_dims, x.shape)]
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out = lax_slicing.dynamic_slice(x, starts, sizes)
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return lax.squeeze(out, [i for i, b in enumerate(indexed_dims) if b])
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def _dynamic_update_index(x, idx, val, indexed_dims):
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assert isinstance(idx, (list, tuple)) and idx
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idx_ = iter(idx)
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starts = [next(idx_) if b else np.int32(0) for b in indexed_dims]
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assert next(idx_, None) is None
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sizes = [1 if b else size for b, size in zip(indexed_dims, x.shape)]
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return lax_slicing.dynamic_update_slice(x, val.reshape(sizes), starts)
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@register_discharge_rule(core.closed_call_p)
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def _closed_call_discharge_rule(
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in_avals: Sequence[core.AbstractValue], _,*args,
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call_jaxpr: core.ClosedJaxpr):
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jaxpr, consts = call_jaxpr.jaxpr, call_jaxpr.consts
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num_outs = len(jaxpr.outvars)
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discharged_jaxpr, discharged_consts = discharge_state(jaxpr, consts)
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discharged_closed_jaxpr = core.ClosedJaxpr(discharged_jaxpr,
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discharged_consts)
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fun = lu.wrap_init(core.jaxpr_as_fun(discharged_closed_jaxpr))
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out_and_ref_vals = core.closed_call_p.bind(fun, *args,
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call_jaxpr=discharged_closed_jaxpr)
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out_vals, ref_vals = split_list(out_and_ref_vals, [num_outs])
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ref_vals_iter = iter(ref_vals)
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new_invals = tuple(next(ref_vals_iter) if isinstance(aval, AbstractRef)
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else None for aval in in_avals)
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assert next(ref_vals_iter, None) is None
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return new_invals, out_vals
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# # `run_state`
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run_state_p = core.Primitive("run_state")
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run_state_p.multiple_results = True
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def _run_state_bind(*args: Any, jaxpr: core.Jaxpr,
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which_linear: tuple[bool, ...]):
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if config.jax_enable_checks:
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core.check_jaxpr(jaxpr)
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assert len(jaxpr.invars) == len(args)
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assert len(which_linear) == len(args)
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return core.Primitive.bind(run_state_p, *args, jaxpr=jaxpr,
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which_linear=which_linear)
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run_state_p.def_custom_bind(_run_state_bind)
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def _run_state_impl(*args: Any, jaxpr: core.Jaxpr,
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which_linear: tuple[bool, ...]):
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del which_linear
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discharged_jaxpr, consts = discharge_state(jaxpr, ())
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return core.eval_jaxpr(discharged_jaxpr, consts, *args)
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run_state_p.def_impl(_run_state_impl)
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def _run_state_abstract_eval(*avals: core.AbstractValue, jaxpr: core.Jaxpr,
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which_linear: tuple[bool, ...]):
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del which_linear
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# When we abstractly evaluate `run_state`, we want to keep track of which
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# input avals are `Ref`s and which are not. If an aval is a `Ref`, we want to
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# "propagate" out its inner effects. Otherwise, the effects are local to this
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# `run_state`.
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is_ref = {i for i, aval in enumerate(avals) if isinstance(aval, AbstractRef)}
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nonlocal_effects = {e for e in jaxpr.effects
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if (isinstance(e, RefEffect) and e.input_index in is_ref)
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or not isinstance(e, RefEffect)}
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return avals, nonlocal_effects
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run_state_p.def_effectful_abstract_eval(_run_state_abstract_eval)
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def _run_state_jvp(primals: Sequence[Any], tangents: Sequence[Any], *,
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jaxpr: core.Jaxpr, which_linear: tuple[bool, ...]):
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nonzero_tangents = [not isinstance(t, ad_util.Zero) for t in tangents]
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discharged_jaxpr, body_consts = discharge_state(jaxpr, ())
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for _ in range(len(nonzero_tangents)):
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_, out_nonzero_tangents = ad.jvp_jaxpr(
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core.ClosedJaxpr(discharged_jaxpr, body_consts),
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nonzero_tangents, instantiate=nonzero_tangents)
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if out_nonzero_tangents == nonzero_tangents:
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break
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nonzero_tangents = map(operator.or_, nonzero_tangents, out_nonzero_tangents)
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else:
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raise Exception("Invalid fixpoint")
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del discharged_jaxpr, body_consts, out_nonzero_tangents
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tangents = [ad.instantiate_zeros(t) if inst else t
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for t, inst in zip(tangents, nonzero_tangents)]
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tangents = [t for t in tangents if type(t) is not ad_util.Zero]
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closed_jvp_jaxpr, _ = ad.jvp_jaxpr(core.ClosedJaxpr(jaxpr, ()),
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nonzero_tangents, [])
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jvp_jaxpr_, jvp_consts = closed_jvp_jaxpr.jaxpr, closed_jvp_jaxpr.consts
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jvp_jaxpr = hoist_consts_to_refs(jvp_jaxpr_)
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jvp_which_linear = (*(False,) * len(jvp_consts), *which_linear, *(True,) * len(tangents))
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out = run_state_p.bind(*jvp_consts, *primals, *tangents, jaxpr=jvp_jaxpr,
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which_linear=jvp_which_linear)
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out_consts, out_primals, out_tangents = split_list(out, [len(jvp_consts),
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len(primals)])
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del out_consts
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out_tangents_iter = iter(out_tangents)
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out_tangents = [next(out_tangents_iter) if nz else ad_util.Zero.from_value(p)
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for p, nz in zip(out_primals, nonzero_tangents)]
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return out_primals, out_tangents
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ad.primitive_jvps[run_state_p] = _run_state_jvp
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_save_everything = lambda *_, **__: True
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def _convert_outputs_to_writes(
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jaxpr: core.Jaxpr) -> Tuple[core.Jaxpr, List[core.ShapedArray]]:
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assert not jaxpr.constvars, "Jaxpr shouldn't have constvars."
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in_avals = [v.aval for v in jaxpr.invars]
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@lu.wrap_init
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def eval_jaxpr(*refs):
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# We split the refs into the original input refs and the dummy residual
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# refs.
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orig_refs, residual_refs = split_list(refs, [len(in_avals)])
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residual_vals = core.eval_jaxpr(jaxpr, (), *orig_refs)
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for res_ref, res_val in zip(residual_refs, residual_vals):
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res_ref[...] = res_val
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return []
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res_ref_avals = [AbstractRef(v.aval) if not isinstance(v.aval, AbstractRef)
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else v.aval for v in jaxpr.outvars]
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jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(
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eval_jaxpr, [*in_avals, *res_ref_avals])
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assert not consts
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return jaxpr, [core.ShapedArray(a.inner_aval.shape, a.inner_aval.dtype) # pytype: disable=attribute-error
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for a in res_ref_avals]
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def _convert_inputs_to_reads(num_res: int, jaxpr: core.Jaxpr) -> core.Jaxpr:
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assert not jaxpr.constvars, "Jaxpr should not have constvars"
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@lu.wrap_init
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def eval_jaxpr(*refs):
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residual_refs, orig_refs = split_list(refs, [num_res])
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residual_vals = [r[...] for r in residual_refs]
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() = core.eval_jaxpr(jaxpr, (), *residual_vals, *orig_refs)
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return []
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res_val_avals, orig_ref_avals = \
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split_list([v.aval for v in jaxpr.invars], [num_res])
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res_ref_avals = [AbstractRef(aval) if not isinstance(aval, AbstractRef) else
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aval for aval in res_val_avals]
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jaxpr, _, () = pe.trace_to_jaxpr_dynamic(
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eval_jaxpr, [*res_ref_avals, *orig_ref_avals])
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return jaxpr
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def _run_state_partial_eval(trace: pe.JaxprTrace, *tracers: pe.JaxprTracer,
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jaxpr: core.Jaxpr, which_linear: tuple[bool, ...]):
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num_inputs = len(tracers)
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assert num_inputs == len(jaxpr.invars)
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in_unknowns = [not t.pval.is_known() for t in tracers]
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# We first need to run a fixpoint to determine which of the `Ref`s are unknown
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# after running the for loop. We want to use the jaxpr to determine which
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# `Ref`s are unknown after executing the for loop body given which `Ref`s are
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# unknown before. However, the jaxpr has no outputs. Instead, we discharge
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# the body and run the fixpoint with the discharged jaxpr. We can do this
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# because the outputs of the jaxpr are one-to-one with the inputs.
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discharged_jaxpr_, discharged_consts = discharge_state(jaxpr, ())
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discharged_jaxpr = pe.convert_constvars_jaxpr(discharged_jaxpr_)
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for _ in range(num_inputs):
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jaxpr_in_unknowns = [False] * len(discharged_consts) + in_unknowns
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_, _, out_unknowns, out_inst, _, _ = pe.partial_eval_jaxpr_stateful(
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discharged_jaxpr, jaxpr_in_unknowns, jaxpr_in_unknowns,
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in_unknowns, False, _save_everything)
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# assert out_inst == out_unknowns
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out_unknowns = list(out_unknowns)
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if out_unknowns == in_unknowns:
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break
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in_unknowns = map(operator.or_, in_unknowns, out_unknowns)
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else:
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raise Exception("Invalid fixpoint")
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del out_unknowns # redundant since it's the same as `in_unknowns`
|
|
tracers = tuple(trace.instantiate_const(t) if uk else t # type: ignore
|
|
for t, uk in zip(tracers, in_unknowns))
|
|
|
|
# We use `partial_eval_jaxpr_stateful` here because it won't remove effectful
|
|
# primitives like `get`/`set`.
|
|
jaxpr_known_resout, jaxpr_unknown_resin_, _, _, num_res_out, num_res_ref = \
|
|
pe.partial_eval_jaxpr_stateful(jaxpr, in_unknowns, in_inst=in_unknowns,
|
|
ensure_out_unknowns=[], ensure_out_inst=[],
|
|
saveable=_save_everything)
|
|
# # `partial_eval_jaxpr_stateful` will give us jaxprs that have hybrid `Ref`
|
|
# and regular valued input/outputs. However, we'd like to bind these jaxprs to
|
|
# a `for`, which expects only `Ref` inputs and no output. We need to convert
|
|
# both of these jaxprs into ones that are compatible with `for`.
|
|
|
|
# `jaxpr_known_resout` is a jaxpr that maps from all the input `Refs`
|
|
# to output residual values (none of them should be `Ref`s). We'll need to
|
|
# convert the output residual values into `Ref`s that are initially empty
|
|
# `Ref`s that are written to at the end of the jaxpr.
|
|
num_res = num_res_out + num_res_ref
|
|
|
|
num_invars = len(jaxpr_known_resout.invars) - num_res_ref
|
|
_, res_ref_avals = split_list(
|
|
[v.aval for v in jaxpr_known_resout.invars], [num_invars])
|
|
res_avals = [a.inner_aval for a in res_ref_avals] # pytype: disable=attribute-error
|
|
jaxpr_known, new_res_avals = _convert_outputs_to_writes(jaxpr_known_resout)
|
|
# We now run the known jaxpr to obtain our residual values.
|
|
known_tracers, _ = partition_list(in_unknowns, tracers)
|
|
known_which_linear, _ = partition_list(in_unknowns, which_linear)
|
|
known_vals = [t.pval.get_known() for t in known_tracers]
|
|
all_res_avals = [*res_avals, *new_res_avals]
|
|
empty_res = map(ad_util.zeros_like_aval, all_res_avals)
|
|
jaxpr_known_args = [*known_vals, *empty_res]
|
|
|
|
jaxpr_known_which_linear = (*known_which_linear, *(False,) * num_res)
|
|
out_flat = run_state_p.bind(*jaxpr_known_args, jaxpr=jaxpr_known,
|
|
which_linear=jaxpr_known_which_linear)
|
|
known_outputs, residuals = split_list(out_flat, [len(known_tracers)])
|
|
residuals = map(trace.new_instantiated_const, residuals)
|
|
ref_res, nonref_res = split_list(residuals, [num_res_ref])
|
|
|
|
# Now we handle the `jaxpr_unknown` that expects residual values as inputs.
|
|
# This jaxpr is the output of `partial_eval_jaxpr_stateful` that marks which
|
|
# inputs are actually used.
|
|
# `partial_eval_jaxpr_stateful` doesn't remove extra inputs/outputs for you
|
|
# so we use `dce_jaxpr` here to do that.
|
|
# To make it compatible with `for`, we need to convert those residual values
|
|
# into `Ref`s.
|
|
jaxpr_unknown = _convert_inputs_to_reads(len(new_res_avals),
|
|
jaxpr_unknown_resin_)
|
|
_, unknown_tracers = partition_list(in_unknowns, tracers)
|
|
_, uk_which_linear = partition_list(in_unknowns, which_linear)
|
|
unknown_which_linear = (False,) * num_res + tuple(uk_which_linear)
|
|
unknown_inputs = [*nonref_res, *ref_res, *unknown_tracers]
|
|
# Outputs match inputs so we construct output tracers that look like the input
|
|
# tracers.
|
|
res_ref_unknown_outputs = [
|
|
pe.JaxprTracer(trace, pe.PartialVal.unknown(t.aval), None)
|
|
for t in unknown_inputs]
|
|
name_stack = source_info_util.current_name_stack()[len(trace.name_stack):]
|
|
source = source_info_util.current().replace(name_stack=name_stack)
|
|
|
|
assert len(unknown_inputs) == len(res_ref_unknown_outputs)
|
|
assert len(unknown_inputs) == len(jaxpr_unknown.invars)
|
|
uk_params = dict(jaxpr=jaxpr_unknown, which_linear=unknown_which_linear)
|
|
_, eqn_effects = run_state_p.abstract_eval(*[v.aval for v in unknown_inputs],
|
|
**uk_params)
|
|
eqn = pe.new_eqn_recipe(unknown_inputs, res_ref_unknown_outputs,
|
|
run_state_p, uk_params,
|
|
eqn_effects, source)
|
|
for t in res_ref_unknown_outputs: t.recipe = eqn
|
|
_, unknown_outputs = split_list(res_ref_unknown_outputs, [num_res])
|
|
return merge_lists(in_unknowns, known_outputs, unknown_outputs)
|
|
pe.custom_partial_eval_rules[run_state_p] = _run_state_partial_eval
|
|
|
|
def _run_state_partial_eval_custom(
|
|
saveable: Callable[..., bool],
|
|
in_unknowns: Sequence[bool],
|
|
in_inst: Sequence[bool],
|
|
eqn: core.JaxprEqn):
|
|
if not any(in_unknowns):
|
|
return eqn, None, in_unknowns, [False] * len(in_unknowns), []
|
|
jaxpr, which_linear = split_dict(eqn.params, ["jaxpr", "which_linear"])
|
|
num_inputs = len(eqn.invars)
|
|
# We first need to run a fixpoint to determine which of the `Ref`s are unknown
|
|
# after running the for loop. However, the jaxpr has no outputs. Instead, we
|
|
# discharge the body and run the fixpoint with the discharged jaxpr. We can do
|
|
# this because the outputs of the discharged jaxpr are one-to-one with the
|
|
# inputs.
|
|
discharged_jaxpr, discharged_consts = discharge_state(jaxpr, ())
|
|
discharged_jaxpr = discharged_jaxpr.replace(
|
|
invars=discharged_jaxpr.constvars + discharged_jaxpr.invars,
|
|
constvars=[])
|
|
in_unknowns, in_inst = list(in_unknowns), list(in_inst)
|
|
out_unknowns, out_inst = in_unknowns, in_unknowns
|
|
for _ in range(num_inputs):
|
|
jaxpr_in_unknowns = [False] * len(discharged_consts) + in_unknowns
|
|
_, _, out_unknowns, out_inst, _, _ = pe.partial_eval_jaxpr_stateful(
|
|
discharged_jaxpr,
|
|
in_unknowns=jaxpr_in_unknowns,
|
|
in_inst=jaxpr_in_unknowns,
|
|
ensure_out_unknowns=in_unknowns,
|
|
ensure_out_inst=in_unknowns,
|
|
saveable=saveable)
|
|
out_unknowns = list(out_unknowns)
|
|
if out_unknowns == in_unknowns:
|
|
break
|
|
in_unknowns = map(operator.or_, in_unknowns, out_unknowns)
|
|
else:
|
|
if num_inputs > 0: raise Exception("Invalid fixpoint")
|
|
del out_unknowns # Redundant since it's the same as `in_unknowns`
|
|
new_inst = [x for x, already, inst in zip(eqn.invars, in_inst, out_inst)
|
|
if type(x) is core.Var and inst and not already]
|
|
|
|
# We use `partial_eval_jaxpr_stateful` here because it won't remove effectful
|
|
# primitives like `get`/`set`.
|
|
jaxpr_known_resout, jaxpr_staged_resin_, _, _, num_res_out, num_res_ref = \
|
|
pe.partial_eval_jaxpr_stateful(jaxpr, in_unknowns,
|
|
in_unknowns, [], [], saveable)
|
|
num_res = num_res_ref + num_res_out
|
|
# `partial_eval_jaxpr_stateful` will give us jaxprs that have hybrid `Ref` and
|
|
# non-Ref input/outputs. However, we'd like to bind these jaxprs to a
|
|
# `for`, which expects only `Ref` inputs and no output. We need to convert
|
|
# both of these jaxprs into ones that are compatible with `for`.
|
|
# TODO(sharadmv,mattjj): implement "passthrough" optimization.
|
|
|
|
# `jaxpr_known_resout` is a jaxpr that maps from all the input `Refs`
|
|
# to output residual values (none of them should be `Ref`s). We'll need to
|
|
# convert the output residual values into `Ref`s that are initially empty
|
|
# `Ref`s that are written to at the end of the jaxpr.
|
|
jaxpr_known, res_avals = _convert_outputs_to_writes(jaxpr_known_resout)
|
|
|
|
# In a stateful partial_eval, the residuals should be `Ref`s.
|
|
res_avals = map(AbstractRef, res_avals) # type: ignore
|
|
|
|
known_invars, staged_invars = partition_list(in_unknowns, eqn.invars)
|
|
known_outvars, staged_outvars = partition_list(in_unknowns, eqn.outvars)
|
|
newvar = core.gensym()
|
|
_, res_ref_avals = split_list([v.aval for v in jaxpr_known_resout.invars],
|
|
[len(known_invars)])
|
|
nonref_resvars = map(newvar, res_avals)
|
|
ref_resvars = map(newvar, res_ref_avals)
|
|
known_out_resvars = map(newvar, [*res_ref_avals, *res_avals])
|
|
|
|
known_which_linear, _ = partition_list(in_unknowns, which_linear)
|
|
jaxpr_known_which_linear = (*known_which_linear, *(False,) * num_res)
|
|
known_and_res_invars = [*known_invars, *ref_resvars, *nonref_resvars]
|
|
|
|
known_params = dict(jaxpr=jaxpr_known, which_linear=jaxpr_known_which_linear)
|
|
_, known_effects = run_state_p.abstract_eval(
|
|
*[v.aval for v in known_and_res_invars], **known_params)
|
|
eqn_known = pe.new_jaxpr_eqn(known_and_res_invars,
|
|
[*known_outvars, *known_out_resvars],
|
|
run_state_p, known_params,
|
|
known_effects, eqn.source_info)
|
|
|
|
jaxpr_staged = _convert_inputs_to_reads(len(res_avals), jaxpr_staged_resin_)
|
|
|
|
_, staged_which_linear = partition_list(in_unknowns, which_linear)
|
|
which_linear_unknown = (*[False] * num_res, *staged_which_linear)
|
|
staged_params = dict(jaxpr=jaxpr_staged, which_linear=which_linear_unknown)
|
|
rejiggered_resvars = [*nonref_resvars, *ref_resvars]
|
|
_, staged_invars = partition_list(in_unknowns, eqn.invars)
|
|
res_staged_invars = [*rejiggered_resvars, *staged_invars]
|
|
_, staged_effects = run_state_p.abstract_eval(
|
|
*[v.aval for v in res_staged_invars], **staged_params)
|
|
_, staged_outvars = partition_list(in_unknowns, eqn.outvars)
|
|
if num_res:
|
|
@lu.wrap_init
|
|
def staged(*args):
|
|
out = run_state_p.bind(*args, **staged_params)
|
|
return out[num_res:]
|
|
staged_call_jaxpr, _, () = pe.trace_to_jaxpr_dynamic(staged,
|
|
[v.aval for v in res_staged_invars])
|
|
eqn_staged = pe.new_jaxpr_eqn(res_staged_invars,
|
|
staged_outvars,
|
|
core.closed_call_p,
|
|
dict(call_jaxpr=core.ClosedJaxpr(staged_call_jaxpr, ())),
|
|
staged_effects, eqn.source_info)
|
|
assert len(res_staged_invars) == len(staged_call_jaxpr.invars)
|
|
assert len(staged_outvars) == len(staged_call_jaxpr.outvars)
|
|
else:
|
|
eqn_staged = pe.new_jaxpr_eqn(staged_invars,
|
|
staged_outvars,
|
|
run_state_p,
|
|
staged_params,
|
|
staged_effects, eqn.source_info)
|
|
new_vars = [*new_inst, *nonref_resvars, *ref_resvars]
|
|
return eqn_known, eqn_staged, in_unknowns, in_unknowns, new_vars
|
|
pe.partial_eval_jaxpr_custom_rules[run_state_p] = _run_state_partial_eval_custom
|
|
|
|
def _transpose_jaxpr(jaxpr: core.Jaxpr, which_linear: Sequence[bool]
|
|
) -> tuple[core.Jaxpr, Any]:
|
|
def trans(*args):
|
|
# First we want to run the computation to read all the residual refs. We can
|
|
# do that by using partial evaluation with all linear inputs unknown.
|
|
res_jaxpr_, tangent_jaxpr_, *_, num_res_out, num_res_ref = \
|
|
pe.partial_eval_jaxpr_stateful(jaxpr, which_linear, in_inst=which_linear,
|
|
ensure_out_inst=[],
|
|
ensure_out_unknowns=[],
|
|
saveable=_save_everything)
|
|
|
|
num_unknown = sum(which_linear)
|
|
num_known = len(jaxpr.invars) - num_unknown
|
|
res_args, _ = partition_list(which_linear, args)
|
|
res_jaxpr_avals = [v.aval for v in res_jaxpr_.invars]
|
|
_, res_avals = split_list(res_jaxpr_avals, [num_known])
|
|
res_avals = [a.inner_aval for a in res_avals] # pytype: disable=attribute-error
|
|
all_avals = [*res_avals, *[v.aval for v in res_jaxpr_.outvars]]
|
|
empty_res = map(ad.zeros_like_aval, all_avals)
|
|
res_jaxpr, _ = _convert_outputs_to_writes(res_jaxpr_)
|
|
res = run_state_p.bind(*res_args, *empty_res, jaxpr=res_jaxpr,
|
|
which_linear=(False,) * (len(res_args) + len(empty_res)))
|
|
res = res[len(res_args):]
|
|
ref_res_, nonref_res_ = split_list(res, [num_res_ref])
|
|
|
|
# Now that we have residual values, we run the tangent jaxpr. It takes as
|
|
# input the residuals, the loop index, and all the refs (at least, the ones
|
|
# that are used in the body). Luckily, `tangent_jaxpr_` has all known and
|
|
# unknown inputs!
|
|
tangent_jaxpr, used_inputs = pe.dce_jaxpr(tangent_jaxpr_, [])
|
|
used_res, used_cts = split_list(used_inputs, [len(res)])
|
|
used_nonref_res, used_ref_res = split_list(used_res, [num_res_out])
|
|
_, nonref_res = partition_list(used_nonref_res, nonref_res_)
|
|
_, ref_res = partition_list(used_ref_res, ref_res_)
|
|
primals_args = [*nonref_res, *ref_res]
|
|
_, tangent_args = partition_list(which_linear, args)
|
|
_, ct_args = partition_list(used_cts, tangent_args)
|
|
ad.backward_pass(
|
|
tangent_jaxpr, (), False, (), (*primals_args, *ct_args), ())
|
|
return []
|
|
jaxpr_trans, _, consts = pe.trace_to_jaxpr_dynamic(
|
|
lu.wrap_init(trans), [v.aval for v in jaxpr.invars])
|
|
return jaxpr_trans, consts
|
|
|
|
def _run_state_transpose(in_cts, *args, jaxpr: core.Jaxpr,
|
|
which_linear: tuple[bool, ...]):
|
|
# if any in_ct is nonzero, we definitely want it in args_ (and the
|
|
# corresponding x in args could be an undefined primal, but doesnt have to be)
|
|
# for non-res stuff:
|
|
# getting and setting => (nonzero ct, UndefinedPrimal arg)
|
|
# just setting => (nonzero ct, not UndefinedPrimal, dummy value)
|
|
# just getting => (zero ct , UndefinedPrimal arg)
|
|
# for res stuff:
|
|
# (zero ct , not UndefinedPrimal)
|
|
assert any(which_linear)
|
|
transpose_args = []
|
|
for x, ct in zip(args, in_cts):
|
|
if type(ct) is ad_util.Zero and not ad.is_undefined_primal(x):
|
|
# this is a residual, take x!
|
|
transpose_args.append(x)
|
|
elif type(ct) is ad_util.Zero and ad.is_undefined_primal(x):
|
|
# the loop was 'just getting', plug in a zero
|
|
transpose_args.append(ad_util.zeros_like_aval(x.aval))
|
|
elif type(ct) is not ad_util.Zero and not ad.is_undefined_primal(x):
|
|
# the loop was 'just setting', grab that cotangent! x is dummy
|
|
transpose_args.append(ct)
|
|
elif type(ct) is not ad_util.Zero and ad.is_undefined_primal(x):
|
|
# the loop was 'getting and setting', grab that cotangent!
|
|
transpose_args.append(ct)
|
|
jaxpr_transpose_, consts = _transpose_jaxpr(jaxpr, which_linear)
|
|
jaxpr_transpose = hoist_consts_to_refs(jaxpr_transpose_)
|
|
which_linear = (*[False] * len(consts), *which_linear)
|
|
const_all_outs = run_state_p.bind(*consts, *transpose_args,
|
|
jaxpr=jaxpr_transpose,
|
|
which_linear=which_linear)
|
|
_, all_outs = split_list(const_all_outs, [len(consts)])
|
|
ct_outs = [ct if ad.is_undefined_primal(x) else None
|
|
for x, ct in zip(args, all_outs)]
|
|
return ct_outs
|
|
ad.primitive_transposes[run_state_p] = _run_state_transpose
|
|
|
|
@register_discharge_rule(run_state_p)
|
|
def _run_state_discharge_rule(in_avals: Sequence[core.AbstractValue],
|
|
out_avals: Sequence[core.AbstractValue],
|
|
*args: Any, jaxpr: core.Jaxpr,
|
|
which_linear: Sequence[bool]):
|
|
del out_avals
|
|
out_vals = run_state_p.bind(*args, jaxpr=jaxpr, which_linear=which_linear)
|
|
new_invals = []
|
|
for aval, out_val in zip(in_avals, out_vals):
|
|
new_invals.append(out_val if isinstance(aval, AbstractRef) else None)
|
|
return new_invals, out_vals
|
|
|
|
def initial_style_jaxpr(
|
|
fun: Callable, in_tree: PyTreeDef, in_avals: Sequence[core.AbstractValue]
|
|
) -> Tuple[core.Jaxpr, List[Any], PyTreeDef]:
|
|
return _initial_style_jaxpr(fun, in_tree, tuple(in_avals))
|
|
|
|
@weakref_lru_cache
|
|
def _initial_style_jaxpr(fun, in_tree, in_avals):
|
|
fun_, out_tree_thunk = api_util.flatten_fun_nokwargs(lu.wrap_init(fun),
|
|
tree_util.treedef_tuple((in_tree,)))
|
|
debug = pe.debug_info(fun_, in_tree, out_tree_thunk, False, 'run_state')
|
|
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(fun_, in_avals, debug)
|
|
return jaxpr, consts, out_tree_thunk()
|
|
|
|
def run_state(f: Callable[..., None]):
|
|
def wrapped(args):
|
|
flat_args, in_tree = tree_util.tree_flatten(args)
|
|
avals = [core.raise_to_shaped(core.get_aval(arg)) for arg in flat_args]
|
|
jaxpr_, consts, _ = initial_style_jaxpr(f, in_tree, map(AbstractRef, avals))
|
|
jaxpr = hoist_consts_to_refs(jaxpr_)
|
|
which_linear = (False,) * (len(consts) + len(flat_args))
|
|
out_const_flat = run_state_p.bind(*consts, *flat_args, jaxpr=jaxpr,
|
|
which_linear=which_linear)
|
|
_, out_flat = split_list(out_const_flat, [len(consts)])
|
|
return in_tree.unflatten(out_flat)
|
|
return wrapped
|
|
|
|
def run_state_reference(f: Callable[..., None]):
|
|
def wrapped(args):
|
|
flat_args, in_tree = tree_util.tree_flatten(args)
|
|
avals = [core.raise_to_shaped(core.get_aval(arg)) for arg in flat_args]
|
|
jaxpr_, consts, _ = initial_style_jaxpr(f, in_tree, map(AbstractRef, avals))
|
|
jaxpr = hoist_consts_to_refs(jaxpr_)
|
|
discharged_jaxpr, discharged_consts = discharge_state(jaxpr, ())
|
|
out_const_flat = core.eval_jaxpr(discharged_jaxpr, discharged_consts,
|
|
*consts, *args)
|
|
_, out_flat = split_list(out_const_flat, [len(consts)])
|
|
return in_tree.unflatten(out_flat)
|
|
return wrapped
|