284 lines
10 KiB
Python
284 lines
10 KiB
Python
# Copyright 2021 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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import functools
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import operator
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from typing import Callable, Optional
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from jax import lax
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from jax._src import api
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from jax._src import core
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from jax._src import custom_api_util
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from jax._src import linear_util as lu
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from jax._src import source_info_util
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from jax._src import traceback_util
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from jax._src import tree_util
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from jax._src import util
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from jax._src.api_util import flatten_fun_nokwargs
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from jax._src.interpreters import ad
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from jax._src.interpreters import batching
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from jax._src.interpreters.batching import not_mapped
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from jax._src.interpreters import mlir
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from jax._src.interpreters import partial_eval as pe
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from jax._src.interpreters import xla
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from jax._src.tree_util import (tree_flatten, tree_map, tree_structure,
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tree_unflatten, treedef_tuple)
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source_info_util.register_exclusion(__file__)
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traceback_util.register_exclusion(__file__)
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map, unsafe_map = util.safe_map, map
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zip, unsafe_zip = util.safe_zip, zip
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@custom_api_util.register_custom_decorator_type
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class custom_vmap:
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fun: Callable
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vmap_rule: Optional[Callable]
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def __init__(self, fun: Callable):
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functools.update_wrapper(self, fun)
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self.fun = fun # type: ignore[assignment]
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self.vmap_rule = None
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__getattr__ = custom_api_util.forward_attr
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def def_vmap(self, vmap_rule: Callable) -> Callable:
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self.vmap_rule = vmap_rule
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return vmap_rule
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@traceback_util.api_boundary
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def __call__(self, *args, **kwargs):
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assert not kwargs
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args_flat, in_tree = tree_flatten(args)
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flat_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(self.fun), in_tree)
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in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args_flat]
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debug = pe.debug_info(self.fun, in_tree, out_tree, False, "custom_vmap")
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jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
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closed_call = core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ())
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in_tree = treedef_tuple((tree_structure(consts), in_tree))
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assert self.vmap_rule is not None
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out_flat = custom_vmap_p.bind(*consts, *args_flat,
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call=closed_call,
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rule=ClosedRule(self.vmap_rule),
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in_tree=in_tree,
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out_tree=out_tree())
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return tree_unflatten(out_tree(), out_flat)
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### utils
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# Define a class, instead of making a function closing over `rule`, so
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# that we can override __str__
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class ClosedRule:
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def __init__(self, rule):
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functools.update_wrapper(self, rule)
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self.rule = rule
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def __call__(self, axis_size, all_in_batched, *all_args):
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_, args = all_args
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consts_batched, in_batched = all_in_batched
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assert not any(tree_util.tree_leaves(consts_batched)), consts_batched
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return call_rule(self.rule, axis_size, in_batched, args)
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def __str__(self):
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return str(self.rule)
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def ensure_list(xs):
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return xs if type(xs) is list else list(xs)
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def rule_name(rule):
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return getattr(rule, '__name__', '<unnamed rule>')
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def call_rule(rule, axis_size, in_batched, args):
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return rule(axis_size, ensure_list(in_batched), *args)
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def check_vmap_rule_trees(rule, original_out_tree, out_tree, out_batched_tree):
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if out_tree != out_batched_tree:
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raise ValueError(
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'structure of output value and output batching specification returned '
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f'by custom vmap rule ({rule_name(rule)}) do not match.\n'
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f'Output values: {out_tree}\n'
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f'Batching spec: {out_batched_tree}')
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if out_tree != original_out_tree:
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raise ValueError(
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f'structure of output returned by custom vmap rule ({rule_name(rule)}) '
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'does not match that of original custom-vmapped function.\n'
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f'Original output: {original_out_tree}\n'
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f'Rule output: {out_tree}')
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# Like batching.bdim_at_front, but doesn't broadcast if not mapped
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def maybe_bdim_at_front(x, bdim):
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if bdim is not_mapped:
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return x
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else:
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return util.moveaxis(x, bdim, 0)
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# Like batching.batch except (a) not curried and (b) returns inferred output
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# axes instead of accepting and matching a given spec of output axes. Assumes
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# `f` is pytree-flattened
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def vmap_unrestricted(f: lu.WrappedFun, *args, in_axes, axis_name, axis_size):
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f, out_axes = batching.batch_subtrace(f)
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f = batching._batch_outer(f, axis_name, axis_size, in_axes,
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batching.BatchTrace, None)
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outs = f.call_wrapped(*args)
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return outs, out_axes()
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### custom_vmap_p rules
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def custom_vmap_impl(*args, call, rule, in_tree, out_tree):
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del rule, in_tree, out_tree
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return core.jaxpr_as_fun(call)(*args)
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def custom_vmap_batching(args_flat, dims, *, call, rule, in_tree, out_tree):
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del call
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axis_size, = {x.shape[d] for x, d in zip(args_flat, dims) if d is not None}
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args_flat = map(maybe_bdim_at_front, args_flat, dims)
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flat_in_batched = [d is not not_mapped for d in dims]
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args = tree_unflatten(in_tree, args_flat)
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in_batched = tree_unflatten(in_tree, flat_in_batched)
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out, out_batched = call_rule(rule, axis_size, in_batched, args)
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flat_outs, tree1 = tree_flatten(out)
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flat_out_batched, tree2 = tree_flatten(out_batched)
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check_vmap_rule_trees(rule, out_tree, tree1, tree2)
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flat_out_dims = [0 if b else not_mapped for b in flat_out_batched]
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return flat_outs, flat_out_dims
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def custom_vmap_abstract_eval(*in_avals, call, **_):
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return call.out_avals
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def custom_vmap_jvp(primals, tangents, *, call, rule, in_tree, out_tree):
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def jvp_of_rule_rule(axis_size, in_batched, primals, tangents):
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in_batched_ps, in_batched_ts = in_batched
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mutually_batched = tree_map(operator.and_, in_batched_ps, in_batched_ts)
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extra_batched_ps = tree_map(lambda pb, tb: 0 if pb and not tb else None,
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in_batched_ps, in_batched_ts)
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extra_batched_ts = tree_map(lambda pb, tb: 0 if tb and not pb else None,
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in_batched_ps, in_batched_ts)
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out_mutually_batched = lu.Store()
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flat_ps_ts, tree_ps_ts = tree_flatten((primals, tangents))
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flat_extra_batched_ps_ts, tree_ps_ts2 = tree_flatten(
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(extra_batched_ps, extra_batched_ts),
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is_leaf=lambda x: x is None)
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# TODO(frostig): assert these also equal:
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# treedef_tuple((in_tree, in_tree))
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# once https://github.com/google/jax/issues/9066 is fixed
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assert tree_ps_ts == tree_ps_ts2
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del tree_ps_ts2
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def to_jvp(*primals):
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out, out_batched = call_rule(rule, axis_size, mutually_batched, primals)
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check_vmap_rule_trees(
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rule, out_tree, tree_structure(out), tree_structure(out_batched))
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out_mutually_batched.store(out_batched)
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return out
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def to_vmap_over_extra_batched_dims(primals, tangents):
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return api.jvp(to_jvp, primals, tangents)
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to_vmap_over_extra_batched_dims_flat, out_tree2 = flatten_fun_nokwargs(
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lu.wrap_init(to_vmap_over_extra_batched_dims),
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tree_ps_ts)
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flat_out_ps_ts, flat_out_axes = vmap_unrestricted(
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to_vmap_over_extra_batched_dims_flat, *flat_ps_ts,
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in_axes=flat_extra_batched_ps_ts,
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axis_name=core.no_axis_name, axis_size=axis_size)
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n, ragged = divmod(len(flat_out_ps_ts), 2)
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assert not ragged
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flat_out_ps, flat_out_ts = flat_out_ps_ts[:n], flat_out_ps_ts[n:]
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flat_out_axes_p, flat_out_axes_t = flat_out_axes[:n], flat_out_axes[n:]
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flat_out_ps = map(maybe_bdim_at_front, flat_out_ps, flat_out_axes_p)
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flat_out_extra_batched_ps = [d is not not_mapped for d in flat_out_axes_p]
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flat_out_ts = map(maybe_bdim_at_front, flat_out_ts, flat_out_axes_t)
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flat_out_extra_batched_ts = [d is not not_mapped for d in flat_out_axes_t]
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out_ps, out_ts = tree_unflatten(
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out_tree2(), [*flat_out_ps, *flat_out_ts])
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out_extra_batched_ps, out_extra_batched_ts = tree_unflatten(
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out_tree2(), [*flat_out_extra_batched_ps, *flat_out_extra_batched_ts])
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out_batched_ps = tree_map(
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operator.or_, out_mutually_batched.val, out_extra_batched_ps)
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out_batched_ts = tree_map(
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operator.or_, out_mutually_batched.val, out_extra_batched_ts)
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return (out_ps, out_ts), (out_batched_ps, out_batched_ts)
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tangents = map(ad.instantiate_zeros, tangents)
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jvp_call, _ = ad.jvp_jaxpr(call, [True] * len(primals), True)
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jvp_in_tree = treedef_tuple((in_tree, in_tree))
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jvp_out_tree = treedef_tuple((out_tree, out_tree))
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outs = custom_vmap_p.bind(
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*primals, *tangents,
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call=jvp_call, rule=jvp_of_rule_rule,
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in_tree=jvp_in_tree, out_tree=jvp_out_tree)
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assert len(outs) % 2 == 0, len(outs)
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out_primals, out_tangents = util.split_list(outs, [len(outs) // 2])
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return out_primals, out_tangents
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custom_vmap_p = core.Primitive('custom_vmap_call')
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custom_vmap_p.multiple_results = True
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custom_vmap_p.def_impl(custom_vmap_impl)
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custom_vmap_p.def_abstract_eval(custom_vmap_abstract_eval)
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batching.primitive_batchers[custom_vmap_p] = custom_vmap_batching
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ad.primitive_jvps[custom_vmap_p] = custom_vmap_jvp
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xla.register_initial_style_primitive(custom_vmap_p)
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mlir.register_lowering(custom_vmap_p, mlir.lower_fun(
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custom_vmap_impl, multiple_results=True))
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# -- custom vmap applications
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def tree_split(mask, tree):
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lhs = tree_map(lambda l, x: x if l else None, mask, tree)
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rhs = tree_map(lambda l, x: None if l else x, mask, tree)
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return lhs, rhs
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def tree_merge(mask, lhs_tree, rhs_tree):
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return tree_map(lambda l, x_l, x_r: x_l if l else x_r,
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mask, lhs_tree, rhs_tree)
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def sequential_vmap(f):
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f = custom_vmap(f)
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@f.def_vmap
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def rule(axis_size, in_batched, *args):
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del axis_size
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def to_map(mapped_args):
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args = tree_merge(in_batched, mapped_args, bcast_args)
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return f(*args)
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mapped_args, bcast_args = tree_split(in_batched, list(args))
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out = lax.map(to_map, mapped_args)
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out_batched = tree_map(lambda _: True, out)
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return out, out_batched
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return f
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