def partial_eval_by_shape(fn, input_spec, *args, **kwargs):
"""Lazily evaluate a function by using the shapes of the inputs.
This function is similar to `jax.eval_shape` with the key difference that
function outputs that can be computed without a concrete value of the
inputs are returned as is instead of only the shape. See for example
`module.init_by_shape` where this functionality is used to initialize a
model without using input data lr computation.
Args:
fn: the function to be lazily evaluated.
input_spec: an iterable of shapes or (shape, dtype) tuples specifying the
shape and type of the inputs. If unspecified the dtype is float32.
*args: other arguments passed to the module's apply function
**kwargs: keyword arguments passed to the module's apply function
Returns:
A pair consisting of the model output and an instance of Model
"""
# output cannot be returned in lazy_create because jax.eval_shape will only
# return the shape and dtype.
# TODO(mattjj,jheek): use a public JAX API
f = lambda *inputs: fn(*inputs, *args, **kwargs)
input_structs = [_parse_spec(spec) for spec in input_spec]
inputs_flat, in_tree = jax.tree_flatten(input_structs)
f_flat, out_tree = jax.api_util.flatten_fun_nokwargs(lu.wrap_init(f), in_tree)
in_pvals = [pe.PartialVal.unknown(jax.ShapedArray(x.shape, x.dtype))
for x in inputs_flat]
if config.omnistaging_enabled:
_, out_pvals, _ = pe.trace_to_jaxpr(f_flat, in_pvals)
else:
with jax.core.initial_style_staging():
_, out_pvals, _ = pe.trace_to_jaxpr(f_flat, in_pvals, stage_out=True)
out_flat = [const if pv is None else jax.ShapeDtypeStruct(pv.shape, pv.dtype)
for pv, const in out_pvals]
return jax.tree_unflatten(out_tree(), out_flat)
def wrapped(*args, **kwargs):
fun = lu.wrap_init(f, kwargs)
flat_args, in_tree = tree_util.tree_flatten(args)
flat_fun, out_tree = api_util.flatten_fun_nokwargs(fun, in_tree)
flat_avals = safe_map(get_shaped_aval, flat_args)
pvals = [pe.PartialVal((aval, jax_core.unit)) for aval in flat_avals]
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(
flat_fun,
pvals,
instantiate=True,
stage_out=True,
trace_type=pe.StagingJaxprTrace)
out_avals = [pval.get_aval() for pval in out_pvals]
typed_jaxpr = jax_core.TypedJaxpr(jaxpr, consts, flat_avals, out_avals)
return typed_jaxpr, (in_tree, out_tree())
def transposed(*args):
in_primals, out_cts = tree_unflatten(treedef, args)
in_pvals = [
pe.PartialVal.unknown(x.aval)
if ad.is_undefined_primal(x) else pe.PartialVal.known(x)
for x in in_primals
]
primal_fun = lu.wrap_init(partial(core.eval_jaxpr, jaxpr, ()))
tangent_jaxpr, _, consts = pe.trace_to_jaxpr(primal_fun, in_pvals,
False)
dummy_args = [ad.UndefinedPrimal(v.aval) for v in tangent_jaxpr.invars]
in_cts_ = ad.backward_pass(tangent_jaxpr, reduce_axes, False, consts,
dummy_args, out_cts)
in_cts, cell.treedef = tree_flatten(in_cts_)
return in_cts
def scan_fn(broadcast_in, init, *args):
xs = jax.tree_multimap(transpose_to_front, in_axes, args)
def body_fn(c, xs, init_mode=False):
# inject constants
xs = jax.tree_multimap(
lambda ax, arg, x: (arg if ax is broadcast else x), in_axes,
args, xs)
broadcast_out, c, ys = fn(broadcast_in, c, *xs)
if init_mode:
ys = jax.tree_multimap(
lambda ax, y: (y if ax is broadcast else ()), out_axes, ys)
return broadcast_out, ys
else:
ys = jax.tree_multimap(
lambda ax, y: (() if ax is broadcast else y), out_axes, ys)
return c, ys
broadcast_body = functools.partial(body_fn, init_mode=True)
carry_pvals = jax.tree_map(
lambda x: pe.PartialVal.unknown(
jax.ShapedArray(jnp.shape(x), jnp.result_type(x))), init)
scan_pvals = jax.tree_map(
lambda x: pe.PartialVal.unknown(
jax.ShapedArray(jnp.shape(x)[1:], jnp.result_type(x))), xs)
input_pvals = (carry_pvals, scan_pvals)
in_pvals, in_tree = jax.tree_flatten(input_pvals)
f_flat, out_tree = jax.api_util.flatten_fun_nokwargs(
lu.wrap_init(broadcast_body), in_tree)
_, out_pvals, _ = pe.trace_to_jaxpr(f_flat, in_pvals)
out_flat = []
for pv, const in out_pvals:
if pv is not None:
raise ValueError(
'broadcasted variable has a data dependency on the scan body.'
)
out_flat.append(const)
broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
c, ys = lax.scan(body_fn, init, xs, length=length, reverse=reverse)
ys = jax.tree_multimap(transpose_from_front, out_axes, ys)
ys = jax.tree_multimap(
lambda ax, const, y: (const if ax is broadcast else y), out_axes,
constants_out, ys)
return broadcast_in, c, ys
def _get_jax_objects(function, args, kwargs):
# Set up function for transformation
wrapped_function = j_linear_util.wrap_init(function)
# Flatten input arguments
jax_arguments, in_tree = j_tree_util.tree_flatten((args, kwargs))
# Transform function to accept flat arguments
# and return a flat list result
jaxtree_function, _ = j_api_util.flatten_fun(wrapped_function, in_tree)
# Abstract and partial-value's flat arguments
partial_values = j_util.safe_map(_get_partial_value, jax_arguments)
# Trace function into Jaxpr
jaxpr, _, constants = ji_partial_eval.trace_to_jaxpr(
jaxtree_function, partial_values
)
result = (jaxpr, constants)
return result
def wrapped(*args, **kwargs):
fun = lu.wrap_init(f, kwargs)
flat_args, in_tree = tree_util.tree_flatten(args)
flat_fun, out_tree = api_util.flatten_fun_nokwargs(fun, in_tree)
flat_avals = safe_map(get_shaped_aval, flat_args)
if dynamic:
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(
flat_fun,
flat_avals)
else:
pvals = [pe.PartialVal.unknown(aval) for aval in flat_avals]
jaxpr, _, consts = pe.trace_to_jaxpr(
flat_fun,
pvals,
instantiate=True)
typed_jaxpr = jax_core.ClosedJaxpr(jaxpr, consts)
return typed_jaxpr, (in_tree, out_tree())
def wrapped(*args, **kwargs):
fun = lu.wrap_init(f, kwargs)
flat_args, in_tree = tree_util.tree_flatten(args)
flat_fun, out_tree = api_util.flatten_fun_nokwargs(fun, in_tree)
flat_avals = safe_map(get_shaped_aval, flat_args)
if not jax.config.omnistaging_enabled:
raise ValueError('Oryx must be used with JAX omnistaging enabled.')
if dynamic:
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(flat_fun, flat_avals)
else:
pvals = [
pe.PartialVal((aval, jax_core.unit)) for aval in flat_avals
]
jaxpr, _, consts = pe.trace_to_jaxpr(flat_fun,
pvals,
instantiate=True)
typed_jaxpr = jax_core.ClosedJaxpr(jaxpr, consts)
return typed_jaxpr, (in_tree, out_tree())
def closure_convert(fun, in_tree, in_avals):
in_pvals = [pe.PartialVal.unknown(aval) for aval in in_avals]
wrapped_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree)
with core.initial_style_staging():
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(wrapped_fun,
in_pvals,
instantiate=True,
stage_out=False)
out_tree = out_tree()
num_consts = len(consts)
def converted_fun(y, t, *consts_args):
consts, args = split_list(consts_args, [num_consts])
all_args, in_tree2 = tree_flatten((y, t, *args))
assert in_tree == in_tree2
out_flat = core.eval_jaxpr(jaxpr, consts, *all_args)
return tree_unflatten(out_tree, out_flat)
return converted_fun, consts
def linearize(traceable, *primals, **kwargs):
has_aux = kwargs.pop('has_aux', False)
if not has_aux:
jvpfun = jvp(traceable)
else:
jvpfun, aux = jvp(traceable, has_aux=True)
in_pvals = (tuple(pe.PartialVal.known(p) for p in primals)
+ tuple(pe.PartialVal.unknown(get_aval(p).at_least_vspace())
for p in primals))
_, in_tree = tree_flatten(((primals, primals), {}))
jvpfun_flat, out_tree = flatten_fun(jvpfun, in_tree)
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(jvpfun_flat, in_pvals)
out_primals_pvals, out_tangents_pvals = tree_unflatten(out_tree(), out_pvals)
assert all(out_primal_pval.is_known() for out_primal_pval in out_primals_pvals)
_, out_primals_consts = unzip2(out_primals_pvals)
jaxpr.invars = jaxpr.invars[len(primals):]
jaxpr.outvars = jaxpr.outvars[len(out_primals_pvals):]
if not has_aux:
return out_primals_consts, out_tangents_pvals, jaxpr, consts
else:
return out_primals_consts, out_tangents_pvals, jaxpr, consts, aux()
def _tscan(f, a, bs, fields=(0, )):
"""
Works as jax.lax.scan but has additional `fields` argument to select only
necessary fields from `a`'s structure. Defaults to selecting only the first
field. Other fields will be filled by None.
"""
# Note: code is copied and modified from lax.scan implementation in
# [JAX](https://github.com/google/jax) to support the additional `fields`
# arg. Original code has the following copyright:
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License")
# convert pytree to flat jaxtuple
a, a_tree = pytree_to_flatjaxtuple(a)
bs, b_tree = pytree_to_flatjaxtuple(bs)
fields, _ = pytree_to_flatjaxtuple(fields)
f, out_tree = pytree_fun_to_flatjaxtuple_fun(wrap_init(f),
(a_tree, b_tree))
# convert arrays to abstract values
a_aval, _ = lax._abstractify(a)
bs_aval, _ = lax._abstractify(bs)
# convert bs to b
b_aval = core.AbstractTuple(
[ShapedArray(b.shape[1:], b.dtype) for b in bs_aval])
# convert abstract values to partial values (?) then evaluate to get jaxpr
a_pval = partial_eval.PartialVal((a_aval, core.unit))
b_pval = partial_eval.PartialVal((b_aval, core.unit))
jaxpr, pval_out, consts = partial_eval.trace_to_jaxpr(f, (a_pval, b_pval))
aval_out, _ = pval_out
consts = core.pack(consts)
out = tscan_p.bind(a, bs, fields, consts, aval_out=aval_out, jaxpr=jaxpr)
return tree_unflatten(out_tree(), out)
def closure_convert(fun, in_tree, in_avals):
in_pvals = [pe.PartialVal.unknown(aval) for aval in in_avals]
wrapped_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree)
with core.initial_style_staging():
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(
wrapped_fun, in_pvals, instantiate=True, stage_out=False)
out_tree = out_tree()
# We only want to closure convert for constants with respect to which we're
# differentiating. As a proxy for that, we hoist consts with float dtype.
# TODO(mattjj): revise this approach
is_float = lambda c: dtypes.issubdtype(dtypes.dtype(c), jnp.inexact)
(closure_consts, hoisted_consts), merge = partition_list(is_float, consts)
num_consts = len(hoisted_consts)
def converted_fun(y, t, *hconsts_args):
hoisted_consts, args = split_list(hconsts_args, [num_consts])
consts = merge(closure_consts, hoisted_consts)
all_args, in_tree2 = tree_flatten((y, t, *args))
assert in_tree == in_tree2
out_flat = core.eval_jaxpr(jaxpr, consts, *all_args)
return tree_unflatten(out_tree, out_flat)
return converted_fun, hoisted_consts
def _make_typed_jaxpr(traceable, in_avals): pvals = [pe.PartialVal((aval, core.unit)) for aval in in_avals] jaxpr, pvals_out, consts = pe.trace_to_jaxpr(traceable, pvals, instantiate=True) out_avals, _ = unzip2(pvals_out) return core.TypedJaxpr(jaxpr, consts, in_avals, out_avals)
def while_loop(cond_fun, body_fun, init_val):
"""Call ``body_fun`` repeatedly in a loop while ``cond_fun`` is True.
The type signature in brief is
.. code-block:: haskell
while_loop :: (a -> Bool) -> (a -> a) -> a -> a
The semantics of ``while_loop`` are given by this Python implementation::
def while_loop(cond_fun, body_fun, init_val):
val = init_val
while cond_fun(val):
val = body_fun(val)
return val
Unlike that Python version, ``while_loop`` is a JAX primitive and is lowered
to a single XLA While HLO. That makes it useful for reducing compilation times
for jit-compiled functions, since native Python loop constructs in an ``@jit``
function are unrolled, leading to large XLA computations.
Another difference from using Python-native loop constructs is that
``while_loop`` is not reverse-mode differentiable because XLA computations
require static bounds on memory requirements.
Args:
cond_fun: function of type ``a -> Bool``.
body_fun: function of type ``a -> a``.
init_val: value of type ``a``, a type that can be a scalar, array, or any
pytree (nested Python tuple/list/dict) thereof, representing the initial
loop carry value.
Returns:
The output from the final iteration of body_fun, of type ``a``.
"""
init_val_flat, in_tree = pytree_to_jaxtupletree(init_val)
flat_body_fun, out_tree = pytree_fun_to_jaxtupletree_fun(lu.wrap_init(body_fun), (in_tree,))
flat_cond_fun, _ = pytree_fun_to_jaxtupletree_fun(lu.wrap_init(cond_fun), (in_tree,))
carry_pval_flat = carry_aval, _ = _abstractify(init_val_flat)
cond_jaxpr, cond_pval_out, cond_consts = pe.trace_to_jaxpr(flat_cond_fun, (carry_pval_flat,))
body_jaxpr, body_pval_out, body_consts = pe.trace_to_jaxpr(flat_body_fun, (carry_pval_flat,), instantiate=True)
carry_aval_out, _ = body_pval_out
assert isinstance(carry_aval_out, core.AbstractValue)
assert carry_aval == core.lattice_join(carry_aval, carry_aval_out)
cond_pv, cond_const = cond_pval_out
if cond_pv is None:
# cond_fun evaluates to a constant, so don't need to generate a while_loop
if cond_const:
raise ValueError("infinite loop with no effects")
else:
return init_val
else:
assert isinstance(cond_pv, core.AbstractValue)
if (not isinstance(cond_pv, ShapedArray) or cond_pv.shape
or cond_pv.dtype != onp.bool_):
msg = "while_loop cond_fun must return a scalar boolean, got {}."
raise TypeError(msg.format(cond_pv))
if out_tree() != in_tree:
raise TypeError("body_fun input and output must have identical structure")
out_flat = while_p.bind(
init_val_flat, core.pack(cond_consts), core.pack(body_consts),
aval_out=carry_aval_out, cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)
return build_tree(out_tree(), out_flat)
def _make_typed_jaxpr(traceable, in_avals): pvals = [pe.PartialVal((aval, core.unit)) for aval in in_avals] jaxpr, pval_out, consts = pe.trace_to_jaxpr(traceable, pvals, instantiate=True) out_aval, _ = pval_out assert isinstance(out_aval, core.AbstractValue) return core.TypedJaxpr(jaxpr, consts, in_avals, out_aval)
def scan(f, init, xs):
"""Scan a function over leading array axes while carrying along state.
The type signature in brief is
.. code-block:: haskell
scan :: (c -> a -> (c, b)) -> c -> [a] -> (c, [b])
where we use [t] here to denote the type t with an additional leading axis.
That is, if t is an array type then [t] represents the type with an additional
leading axis, and if t is a pytree (container) type with array leaves then [t]
represents the type with the same pytree structure and corresponding leaves
each with an additional leading axis.
When both ``a`` and ``b`` are array types, the semantics of ``scan`` are given
by this Python implementation::
def scan(f, init, xs):
carry = init
ys = []
for x in xs:
carry, y = f(carry, x)
ys.append(y)
return carry, np.stack(ys)
Unlike that Python version, both ``a`` and ``b`` may be arbitrary pytree
types, and so multiple arrays can be scanned over at once and produce multiple
output arrays.
Also unlike that Python version, ``scan`` is a JAX primitive and is lowered to
a single XLA While HLO. That makes it useful for reducing compilation times
for jit-compiled functions, since native Python loop constructs in an ``@jit``
function are unrolled, leading to large XLA computations.
Args:
f: a Python function to be scanned of type ``c -> a -> (c, b)``, meaning
that ``f`` accepts two arguments where the first is a value of the loop
carry and the second is a slice of ``xs`` along its leading axis, and that
``f`` returns a pair where the first element represents a new value for
the loop carry and the second represents a slice of the output.
init: an initial loop carry value of type ``c``, which can be a scalar,
array, or any pytree (nested Python tuple/list/dict) thereof, representing
the initial loop carry value.
xs: the value of type ``[a]`` over which to scan along the leading axis,
where ``[a]`` can be an array or any pytree (nested Python
tuple/list/dict) thereof with consistent leading axis sizes.
Returns:
A pair of type ``(c, [b])`` where the first element represents the final
loop carry value and the second element represents the stacked outputs of
the second output of ``f`` when scanned over the leading axis of the inputs.
"""
(init, xs), in_trees = unzip2(map(pytree_to_jaxtupletree, (init, xs)))
f, out_tree = pytree_fun_to_jaxtupletree_fun(lu.wrap_init(f), in_trees)
carry_pval = carry_aval, _ = _abstractify(init)
xs_aval, _ = _abstractify(xs)
x_aval = _demote_aval_rank(xs_aval)
x_pval = pe.PartialVal((x_aval, core.unit))
jaxpr, pval_out, consts = pe.trace_to_jaxpr(
f, (carry_pval, x_pval), instantiate=True)
pv_out, const_out = pval_out
assert isinstance(pv_out, core.AbstractValue) and const_out == core.unit
if not isinstance(pv_out, core.AbstractTuple) or len(pv_out) != 2:
msg = ("scanned function must have signature `c -> a -> (c, b)`, but the "
"output was not a pair: got type {}.")
raise TypeError(msg.format(pv_out))
carry_aval_out, y_aval = pv_out
if carry_aval != carry_aval_out:
msg = ("scanned function carry output does not match carry input: "
"input carry is {} and output carry is {}.")
raise TypeError(msg.format(carry_aval, carry_aval_out))
lifted_jaxpr = pe._closure_convert_jaxpr(jaxpr)
consts_aval, _ = _abstractify(core.pack(consts))
in_avals = (consts_aval, carry_aval, x_aval)
out_aval = core.AbstractTuple((carry_aval, y_aval))
jaxpr = core.TypedJaxpr(lifted_jaxpr, (), in_avals, out_aval)
length = _leading_dim_size(xs)
out = scan_p.bind(core.pack(consts), init, xs,
forward=True, length=length, jaxpr=jaxpr)
return build_tree(out_tree(), out)
def inv_backward_pass(jaxpr: core.Jaxpr, consts, primals_in, primals_out, cotangents_in):
if all(type(ct) is ad.Zero for ct in cotangents_in):
return map(lambda v: ad.Zero(v.aval), jaxpr.invars)
def write_cotangent(v, ct):
# assert v not in primal_env
if ct is not None and type(v) is not Literal:
ct_env[v] = ad.add_tangents(ct_env[v], ct) if v in ct_env else ct
def read_cotangent(v):
return ct_env.get(v, ad.Zero(v.aval))
def read_primal(v):
if type(v) is Literal:
return v.val
else:
return primal_env.get(v, ad.UndefinedPrimal(v.aval))
def write_primal(v, val):
if type(v) is Literal:
return
primal_env.setdefault(v, val)
# Invert while computing cotangents
ct_env: Dict[Any, Any] = {}
primal_env: Dict[Any, Any] = {}
write_primal(core.unitvar, core.unit)
map(write_primal, jaxpr.invars, primals_in)
map(write_primal, jaxpr.outvars, primals_out)
map(write_primal, jaxpr.constvars, consts)
map(write_cotangent, jaxpr.outvars, cotangents_in)
for eqn in jaxpr.eqns[::-1]:
primals_in = map(read_primal, eqn.invars)
primals_out = map(read_primal, eqn.outvars)
cts_in = map(read_cotangent, eqn.outvars)
should_invert = any(type(primal) is not ad.UndefinedPrimal
for primal in primals_out)
should_vjp = any(type(ct) is not ad.Zero for ct in cts_in)
assert not eqn.primitive.call_primitive
# Skip primals equations that are only jvp coefficients and don't affect
# primal outputs.
if not should_invert and not should_vjp:
continue
def abstract(value):
return raise_to_shaped(value.aval if ad.is_undefined_primal(value) else get_aval(value))
# Get the ivjp_jaxpr
if eqn.primitive is custom_ivjp_p:
ivjp_jaxpr = eqn.params['ivjp_jaxpr']
else:
if eqn.primitive in primitive_ivjps:
complete_ivjp = lu.wrap_init(primitive_ivjps[eqn.primitive])
else:
complete_ivjp = lu.wrap_init(partial(synthesize_ivjp, eqn, map(ad.is_undefined_primal, primals_in)))
_, in_tree = tree_flatten(
tuple(map(abstract, x) for x in (primals_in, primals_out, primals_out)))
complete_ivjp_flat, _ = flatten_fun_nokwargs(complete_ivjp, in_tree)
in_avals = map(abstract, primals_in + primals_out + primals_out)
# TODO: Actually we do know some of the inputs, because they might be literals!
ivjp_jaxpr, out_pvals, _ = pe.trace_to_jaxpr(
complete_ivjp_flat, map(pe.PartialVal.unknown, in_avals), instantiate=True)
assert not ivjp_jaxpr.constvars # That might happen some time, but don't bother until then
ivjp_jaxpr = core.ClosedJaxpr(ivjp_jaxpr, [])
# Once we know what the ivjp can do exactly, we have to isolate the part we are
# actually able to compute with the values we have at hand.
num_inputs = len(eqn.invars)
unknowns = (map(ad.is_undefined_primal, primals_in) +
map(ad.is_undefined_primal, primals_out) +
[False] * len(cts_in))
jaxpr_known, jaxpr_unknown, out_unknowns = pe.partial_eval_jaxpr( # type: ignore
ivjp_jaxpr, unknowns, instantiate=False) # type:ignore
unknown_rec_primals_in, unknown_cotangents = split_list(out_unknowns, [num_inputs])
# Make sure we're able to compute all cotangents. We don't really care if we
# can reconstruct or primals or not, although failure to do so might result in
# failing to compute cotangents later.
assert not any(unknown_cotangents)
# Remove residual outputs -- we won't be computing the unknown jaxpr anyway.
num_outputs = len(jaxpr_unknown.jaxpr.outvars)
jaxpr_known.jaxpr.outvars = jaxpr_known.jaxpr.outvars[:num_outputs]
# TODO: We could drop the outputs that correspond to primals that we already know.
# This only matters in eager mode, so leaving it out for now...
ivjp = core.jaxpr_as_fun(jaxpr_known)
rec_primals_in, cts_out = split_list(ivjp(*primals_in, *primals_out, *cts_in),
[num_inputs])
# Unknown rec_primals_in are core.units, so we have to replace them
# with UnknownPrimals because that's what write_primal will ignore.
rec_primals_in = [prev if unknown else rec
for prev, rec, unknown
in zip(primals_in, rec_primals_in, unknown_rec_primals_in)]
map(write_primal, eqn.invars, rec_primals_in)
map(write_cotangent, [v for v in eqn.invars if type(v) is not Literal], cts_out)
# NOTE: We keep the cotangents associated with primal variables, while the contract of a
# transpose is to return them in positions associated with tangent variables, which
# is what causes this whole confusion.
return map(read_cotangent, jaxpr.invars)
def DIABLED_test_print_jaxpr_compound(self):
# TODO(dougalm): figure out what jaxpr-tracing api to expose and re-enable
pv = pe.PartialVal((ShapedArray((2, 3), onp.float32), core.unit))
print(pe.trace_to_jaxpr(fun_with_call_closure, (pv, ))[0])
def while_loop(cond_fun, body_fun, init_val):
"""Call ``body_fun`` repeatedly in a loop while ``cond_fun`` is True.
The type signature in brief is
.. code-block:: haskell
while_loop :: (a -> Bool) -> (a -> a) -> a -> a
The semantics of ``while_loop`` are given by this Python implementation::
def while_loop(cond_fun, body_fun, init_val):
val = init_val
while cond_fun(val):
val = body_fun(val)
return val
Unlike that Python version, ``while_loop`` is a JAX primitive and is lowered
to a single XLA While HLO. That makes it useful for reducing compilation times
for jit-compiled functions, since native Python loop constructs in an ``@jit``
function are unrolled, leading to large XLA computations.
Another difference from using Python-native loop constructs is that
``while_loop`` is not reverse-mode differentiable because XLA computations
require static bounds on memory requirements.
Args:
cond_fun: function of type ``a -> Bool``.
body_fun: function of type ``a -> a``.
init_val: value of type ``a``, a type that can be a scalar, array, or any
pytree (nested Python tuple/list/dict) thereof, representing the initial
loop carry value.
Returns:
The output from the final iteration of body_fun, of type ``a``.
"""
init_val_flat, in_tree = pytree_to_jaxtupletree(init_val)
flat_body_fun, out_tree = pytree_fun_to_jaxtupletree_fun(
lu.wrap_init(body_fun), (in_tree, ))
flat_cond_fun, _ = pytree_fun_to_jaxtupletree_fun(lu.wrap_init(cond_fun),
(in_tree, ))
carry_pval_flat = carry_aval, _ = _abstractify(init_val_flat)
cond_jaxpr, cond_pval_out, cond_consts = pe.trace_to_jaxpr(
flat_cond_fun, (carry_pval_flat, ))
body_jaxpr, body_pval_out, body_consts = pe.trace_to_jaxpr(
flat_body_fun, (carry_pval_flat, ), instantiate=True)
carry_aval_out, _ = body_pval_out
assert isinstance(carry_aval_out, core.AbstractValue)
assert carry_aval == core.lattice_join(carry_aval, carry_aval_out)
cond_pv, cond_const = cond_pval_out
if cond_pv is None:
# cond_fun evaluates to a constant, so don't need to generate a while_loop
if cond_const:
raise ValueError("infinite loop with no effects")
else:
return init_val
else:
assert isinstance(cond_pv, core.AbstractValue)
if (not isinstance(cond_pv, ShapedArray) or cond_pv.shape
or cond_pv.dtype != onp.bool_):
msg = "while_loop cond_fun must return a scalar boolean, got {}."
raise TypeError(msg.format(cond_pv))
# We don't want to promote literal constants as loop arguments; there are
# sometimes many of them. We pass tracers as loop arguments, but leave
# nontracers as constants. We also sort the constants so the nontracers are
# first.
def split_tracers_and_nontracers(jaxpr, consts):
tracer = []
nontracer = []
for x in zip(jaxpr.constvars, consts):
# TODO(phawkins): We avoid treating DeviceArrays as constant literals so
# we don't copy large arrays back to the host. We probably should relax
# this and either always copy small constants, or opportunistically use
# DeviceArray values for which we already know npy_value.
not_literal_const = isinstance(x[1],
(core.Tracer, xla.DeviceArray))
(tracer if not_literal_const else nontracer).append(x)
tracer_vars, tracer_consts = unzip2(tracer)
nontracer_vars, nontracer_consts = unzip2(nontracer)
return nontracer_vars + tracer_vars, nontracer_consts, tracer_consts
cond_split = split_tracers_and_nontracers(cond_jaxpr, cond_consts)
cond_jaxpr.constvars, cond_nontracer_consts, cond_tracer_consts = cond_split
body_split = split_tracers_and_nontracers(body_jaxpr, body_consts)
body_jaxpr.constvars, body_nontracer_consts, body_tracer_consts = body_split
if out_tree() != in_tree:
raise TypeError(
"body_fun input and output must have identical structure")
out_flat = while_p.bind(
init_val_flat,
core.pack(cond_tracer_consts),
core.pack(body_tracer_consts),
cond_consts=lax._OpaqueParam(cond_nontracer_consts),
body_consts=lax._OpaqueParam(body_nontracer_consts),
aval_out=carry_aval_out,
cond_jaxpr=cond_jaxpr,
body_jaxpr=body_jaxpr)
return build_tree(out_tree(), out_flat)
def inner(scope_fn, repack_fn, variable_groups_xs, rng_groups_xs):
# split rngs
split_fn = lambda rng: random.split(rng, length)
broadcast_rngs_xs = []
scan_rngs_xs = []
for rng_groups in rng_groups_xs:
broadcast_rngs_xs.append(
tuple(rng_group
for rng_group, split in zip(rng_groups, rng_splits)
if not split))
scan_rngs_xs.append(
tuple(
jax.tree_map(split_fn, rng_group)
for rng_group, split in zip(rng_groups, rng_splits)
if split))
def body(carry, xs, init_mode=False):
carry_vars_xs, c = carry
scan_vars_xs, scan_rngs_xs, x = xs
variable_groups_xs = combine(scan_vars_xs, carry_vars_xs,
broadcast_vars_xs)
rng_groups_xs = []
for broadcast_rngs, scan_rngs in zip(broadcast_rngs_xs,
scan_rngs_xs):
rng_groups_xs.append(broadcast_rngs + scan_rngs)
scope = scope_fn(variable_groups_xs, rng_groups_xs)
carry, y = fn(scope, c, x)
out_vars = repack_fn(scope)
scan_vars_xs, carry_vars_out_xs, broadcast_vars_out_xs = split(
out_vars, 1)
# TODO(jheek) more informative error check
def check_shapes(c_in, c_out):
if not isinstance(c_in, jnp.ndarray) or not isinstance(
c_out, jnp.ndarray):
return
if jnp.shape(c_in) != jnp.shape(c_out) or jnp.dtype(
c_in) != jnp.dtype(c_out):
raise ValueError()
try:
jax.tree_multimap(check_shapes, carry_vars_xs,
carry_vars_out_xs)
except ValueError:
raise ValueError(
'carry variables must have the same shape and dtype before and after scan.'
)
if init_mode:
return broadcast_vars_out_xs
else:
return (carry_vars_out_xs, carry), (scan_vars_xs, y)
broadcast_body = functools.partial(body, init_mode=True)
scan_vars_xs, carry_vars_xs, broadcast_vars_xs = split(
variable_groups_xs, 0)
carry0 = (carry_vars_xs, init_carry)
xxs = (scan_vars_xs, scan_rngs_xs, xs)
# use partial evaluation to find the variables that are broadcasted out
# an error is thrown if a broadcasted output has a dependency on any scan variables
carry_pvals = jax.tree_map(
lambda x: pe.PartialVal.unknown(jax.ShapedArray(x.shape, x.dtype)),
carry0)
scan_pvals = jax.tree_map(
lambda x: pe.PartialVal.unknown(
jax.ShapedArray(x.shape[1:], x.dtype)), xxs)
input_pvals = (carry_pvals, scan_pvals)
in_pvals, in_tree = jax.tree_flatten(input_pvals)
f_flat, out_tree = jax.api_util.flatten_fun_nokwargs(
lu.wrap_init(broadcast_body), in_tree)
_, out_pvals, _ = pe.trace_to_jaxpr(f_flat, in_pvals)
# _, out_pvals, _ = pe.trace_to_jaxpr(f_flat, in_pvals, stage_out=True)
out_flat = []
for pv, const in out_pvals:
if pv is not None:
raise ValueError(
'broadcasted variable has a data dependency on the scan body.'
)
out_flat.append(const)
(carry_vars_xs, carry), (scan_vars_xs, ys) = lax.scan(body,
carry0,
xxs,
length=length,
reverse=reverse)
broadcast_vars_xs = jax.tree_unflatten(out_tree(), out_flat)
out_vars_xs = combine(carry_vars_xs, scan_vars_xs, broadcast_vars_xs)
return (carry, ys), out_vars_xs
def _instantiated_trace_to_jaxpr(fun, avals):
pvals = map(lambda aval: pe.PartialVal((aval, unit)), avals)
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(fun, pvals, instantiate=True)
out_avals, _ = unzip2(out_pvals)
return jaxpr, out_avals, consts
def while_loop(cond_fun, body_fun, init_val):
"""Call `body_fun` repeatedly in a loop while `cond_fun` is True.
Arguments:
cond_fun: pure function of type `T -> Bool`.
body_fun: pure function of type `T -> T`.
init_val: value of type `T`, a type that can be a scalar, array, or any
(nested) Python tuple/list/dict thereof.
Returns:
The output from the final iteration of body_fun, of type `T`.
The semantics of `while_loop` are given by this Python implementation::
def while_loop(cond_fun, body_fun, init_val):
val = init_val
while cond_fun(val):
val = body_fun(val)
return val
Unlike that pure Python version, `while_loop` is a JAX primitive and is
lowered to a single XLA While HLO. That makes it useful for reducing
compilation times for jit-compiled functions, since native Python loop
constructs in an `@jit` function are unrolled, leading to large XLA
computations.
Another difference from using Python-native loop constructs is that
`while_loop` is not (yet) reverse-mode differentiable because XLA computations
require static bounds on memory requirements.
"""
init_val_flat, in_tree = pytree_to_jaxtupletree(init_val)
flat_body_fun, out_tree = pytree_fun_to_jaxtupletree_fun(
lu.wrap_init(body_fun), (in_tree, ))
flat_cond_fun, _ = pytree_fun_to_jaxtupletree_fun(lu.wrap_init(cond_fun),
(in_tree, ))
pval_flat = lax._abstractify(init_val_flat)
cond_jaxpr, _, cond_consts = pe.trace_to_jaxpr(flat_cond_fun,
(pval_flat, ))
body_jaxpr, pval_out, body_consts = pe.trace_to_jaxpr(
flat_body_fun, (pval_flat, ))
aval_out, _ = pval_out
# We don't want to promote literal constants as loop arguments; there are
# sometimes many of them. We pass tracers as loop arguments, but leave
# nontracers as constants. We also sort the constants so the nontracers are
# first.
def split_tracers_and_nontracers(jaxpr, consts):
tracer = []
nontracer = []
for x in zip(jaxpr.constvars, consts):
# TODO(phawkins): We avoid treating DeviceArrays as constant literals so
# we don't copy large arrays back to the host. We probably should relax
# this and either always copy small constants, or opportunistically use
# DeviceArray values for which we already know npy_value.
not_literal_const = isinstance(x[1],
(core.Tracer, xla.DeviceArray))
(tracer if not_literal_const else nontracer).append(x)
tracer_vars, tracer_consts = unzip2(tracer)
nontracer_vars, nontracer_consts = unzip2(nontracer)
return nontracer_vars + tracer_vars, nontracer_consts, tracer_consts
cond_split = split_tracers_and_nontracers(cond_jaxpr, cond_consts)
cond_jaxpr.constvars, cond_nontracer_consts, cond_tracer_consts = cond_split
body_split = split_tracers_and_nontracers(body_jaxpr, body_consts)
body_jaxpr.constvars, body_nontracer_consts, body_tracer_consts = body_split
if out_tree() != in_tree:
raise TypeError(
"body_fun input and output must have identical structure")
out_flat = while_p.bind(
init_val_flat,
core.pack(cond_tracer_consts),
core.pack(body_tracer_consts),
cond_consts=lax._OpaqueParam(cond_nontracer_consts),
body_consts=lax._OpaqueParam(body_nontracer_consts),
aval_out=aval_out,
cond_jaxpr=cond_jaxpr,
body_jaxpr=body_jaxpr)
return build_tree(out_tree(), out_flat)
def trace_jaxpr(fun, operand): op_flat, in_tree = pytree_to_flatjaxtuple(operand) fun_flat, out_tree = pytree_fun_to_flatjaxtuple_fun(lu.wrap_init(fun), (in_tree,)) jaxpr, pvout, consts = pe.trace_to_jaxpr(fun_flat, (_abstractify(op_flat),)) return op_flat, jaxpr, consts, pvout, out_tree
