def ignore_errors_jaxpr(jaxpr, error):
"""Constructs a jaxpr which takes two extra args but ignores them."""
err_aval = core.raise_to_shaped(core.get_aval(error.err))
code_aval = core.raise_to_shaped(core.get_aval(error.code))
consts = jaxpr.consts
jaxpr = jaxpr.jaxpr
new_vars = core.gensym([jaxpr])
new_invars = (new_vars(err_aval), new_vars(code_aval), *jaxpr.invars)
new_jaxpr = core.Jaxpr(jaxpr.constvars, new_invars, jaxpr.outvars,
jaxpr.eqns)
return core.ClosedJaxpr(new_jaxpr, consts)
def array_to_spvalue(arg):
if isinstance(arg, BCOO):
return spenv.sparse(arg.shape, arg.data, arg.indices)
elif core.get_aval(arg) is core.abstract_unit:
return spenv.unit()
else:
return spenv.dense(arg)
def typecheck_atom(env, x):
if isinstance(x, Literal):
return core.raise_to_shaped(core.get_aval(x.val))
elif isinstance(x, Var):
return typecheck_type(env, x.aval)
else:
raise TypeError(f'atom of unexpected type {x}')
def _check_output_dtype_jacfwd(holomorphic, x):
aval = core.get_aval(x)
if holomorphic:
if not dtypes.issubdtype(aval.dtype, np.complexfloating):
raise TypeError(
"jacfwd with holomorphic=True requires outputs with complex dtype, "
f"but got {aval.dtype.name}.")
def array_to_argspec(arg):
if isinstance(arg, BCOO):
return ArgSpec(arg.shape, spenv.push(arg.data), spenv.push(arg.indices))
elif core.get_aval(arg) is core.abstract_unit:
return ArgSpec((), None, None)
else:
return ArgSpec(np.shape(arg), spenv.push(arg), None)
def _sparsify_jaxpr(spenv, jaxpr, *argspecs):
# TODO(jakevdp): currently this approach discards all information about
# shared data & indices when generating the sparsified jaxpr. The
# current approach produces valid sparsified while loops, but they
# don't work in corner cases (see associated TODO in sparsify_test.py)
out_tree = None
@lu.wrap_init
def wrapped(*args_flat):
nonlocal out_tree
args = tree_unflatten(in_tree, args_flat)
argspecs = arrays_to_argspecs(spenv, args)
result = eval_sparse(jaxpr.jaxpr, jaxpr.consts, argspecs, spenv)
out = argspecs_to_arrays(spenv, result)
out_flat, out_tree = tree_flatten(out)
return out_flat
args = argspecs_to_arrays(spenv, argspecs)
args_flat, in_tree = tree_flatten(args)
avals_flat = [
core.raise_to_shaped(core.get_aval(arg)) for arg in args_flat
]
sp_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(wrapped, avals_flat)
sp_jaxpr = pe.ClosedJaxpr(pe.convert_constvars_jaxpr(sp_jaxpr), consts)
return sp_jaxpr, out_tree
def _sparsify_jaxpr(spenv, jaxpr, *spvalues):
# TODO(jakevdp): currently this approach discards all information about
# shared data & indices when generating the sparsified jaxpr. The
# current approach produces valid sparsified while loops, but they
# don't work in corner cases (see associated TODO in sparsify_test.py)
out_tree = None
@lu.wrap_init
def wrapped(*args_flat):
# TODO(frostig,jakevdp): This closes over `spenv`, which can bring
# in buffers from the "outer scope" as constants. Is this a
# problem for primitives like cond and while_loop, which always
# convert constvars to invars when staging out their subjaxprs?
nonlocal out_tree
args = tree_unflatten(in_tree, args_flat)
spvalues = arrays_to_spvalues(spenv, args)
result = eval_sparse(jaxpr.jaxpr, jaxpr.consts, spvalues, spenv)
out = spvalues_to_arrays(spenv, result)
out_flat, out_tree = tree_flatten(out)
return out_flat
args = spvalues_to_arrays(spenv, spvalues)
args_flat, in_tree = tree_flatten(args)
avals_flat = [core.raise_to_shaped(core.get_aval(arg)) for arg in args_flat]
sp_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(wrapped, avals_flat)
sp_jaxpr = pe.ClosedJaxpr(sp_jaxpr, consts)
return sp_jaxpr, out_tree
def trace_to_jaxpr_finalize(in_tracers,
out_tracers,
trace,
instantiate=True):
# TODO: This is the final part of the partial_eval.trace_to_subjaxpr. Share.
instantiate = [instantiate] * len(out_tracers)
out_tracers = safe_map(trace.full_raise,
safe_map(core.full_lower, out_tracers))
out_tracers = safe_map(partial(pe.instantiate_const_at, trace),
instantiate, out_tracers)
jaxpr, consts, env = pe.tracers_to_jaxpr(in_tracers, out_tracers)
out_pvals = [t.pval for t in out_tracers]
# TODO: this is from partial_eval.trace_to_jaxpr. Share.
assert not env
# TODO: this is from the final part of lax_control_flow._initial_style_jaxpr
out_avals = safe_map(abstract_arrays.raise_to_shaped,
unzip2(out_pvals)[0])
const_avals = tuple(
abstract_arrays.raise_to_shaped(core.get_aval(c)) for c in consts)
in_pvals = [t.pval for t in in_tracers]
in_avals = tuple(
safe_map(abstract_arrays.raise_to_shaped,
unzip2(in_pvals)[0]))
typed_jaxpr = core.TypedJaxpr(pe.convert_constvars_jaxpr(jaxpr), (),
const_avals + in_avals, out_avals)
return typed_jaxpr, consts
def _custom_vjp_call_jaxpr_jvp(primals, tangents, *,
fun_jaxpr: core.ClosedJaxpr,
fwd_jaxpr_thunk: Callable[[],
Tuple[core.Jaxpr,
Sequence[Any]]],
bwd: lu.WrappedFun, out_trees: Callable,
num_consts: int):
_, args = split_list(primals, [num_consts])
consts_dot, args_dot = split_list(tangents, [num_consts])
if any(type(t) is not Zero for t in consts_dot):
raise ad.CustomVJPException()
fwd_jaxpr, fwd_consts = fwd_jaxpr_thunk() # consts can be tracers!
out_tree, res_tree = out_trees()
args_dot = map(ad.instantiate_zeros, args_dot)
# Cast float0 to zeros with the primal dtype because custom vjp rules don't
# currently handle float0s
args_dot = map(ad.replace_float0s, args, args_dot)
res_and_primals_out = core.eval_jaxpr(fwd_jaxpr, fwd_consts, *args)
res, primals_out = split_list(res_and_primals_out, [res_tree.num_leaves])
avals_out = [raise_to_shaped(core.get_aval(x)) for x in primals_out]
tangents_out = ad.custom_lin_p.bind(*res,
*args_dot,
num_res=res_tree.num_leaves,
bwd=bwd,
avals_out=avals_out)
tangents_out = map(ad.recast_to_float0, primals_out, tangents_out)
return primals_out, tangents_out
def __call__(self, *args: Any, **kwargs: Any) -> ReturnValue:
if not self.fwd or not self.bwd:
msg = "No VJP defined for custom_vjp function {} using defvjp."
raise AttributeError(msg.format(self.__name__))
args = _resolve_kwargs(self.fun, args, kwargs)
if self.nondiff_argnums:
for i in self.nondiff_argnums:
_check_for_tracers(args[i])
nondiff_argnums = set(self.nondiff_argnums)
dyn_argnums = [
i for i in range(len(args)) if i not in nondiff_argnums
]
f_, dyn_args = argnums_partial(lu.wrap_init(self.fun), dyn_argnums,
args)
static_args = [args[i] for i in self.nondiff_argnums]
fwd, _ = argnums_partial(lu.wrap_init(self.fwd), dyn_argnums, args)
bwd = _add_args(lu.wrap_init(self.bwd), static_args)
else:
f_, dyn_args = lu.wrap_init(self.fun), args
fwd, bwd = lu.wrap_init(self.fwd), lu.wrap_init(self.bwd)
args_flat, in_tree = tree_flatten(dyn_args)
in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args_flat]
flat_fun, out_tree = flatten_fun_nokwargs(f_, in_tree)
flat_fwd, out_trees = _flatten_fwd(fwd, in_tree)
flat_bwd = _flatten_bwd(bwd, in_tree, in_avals, out_trees)
out_flat = custom_vjp_call_p.bind(flat_fun,
flat_fwd,
flat_bwd,
*args_flat,
out_trees=out_trees)
fst, aux = lu.merge_linear_aux(out_tree, out_trees)
out_tree = aux if fst else aux[0]
return tree_unflatten(out_tree, out_flat)
class ErrorTracer(core.Tracer):
def __init__(self, trace, val):
self._trace = trace
self.val = val
core.get_aval(val), val
aval = property(lambda self: core.get_aval(self.val))
full_lower = lambda self: self
def fwd(*args, **kwargs):
ans, rule = fun(*args, **kwargs)
ans_flat, out_tree = tree_flatten((ans, ))
rule, in_tree = flatten_fun_nokwargs(lu.wrap_init(rule), out_tree)
ans_avals = [core.get_aval(x).at_least_vspace() for x in ans_flat]
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(rule, ans_avals)
return ans, Residuals(jaxpr, in_tree(), out_tree, consts)
def remat_transpose(reduce_axes, out_cts, *in_primals, jaxpr, **params):
assert not jaxpr.constvars
cell = lambda: None
@lu.wrap_init
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, ()))
t_jaxpr, _, consts = pe.trace_to_jaxpr_nounits(primal_fun, in_pvals, False)
dummy_args = [ad.UndefinedPrimal(v.aval) for v in t_jaxpr.invars]
in_cts = ad.backward_pass(t_jaxpr, reduce_axes, False, consts, dummy_args,
out_cts)
in_cts_ = iter(in_cts)
in_cts = [next(in_cts_) if ad.is_undefined_primal(x)
else ad_util.Zero(x.aval) for x in in_primals]
assert next(in_cts_, None) is None
in_cts, cell.treedef = tree_flatten(in_cts)
return in_cts
args, treedef = tree_flatten((in_primals, out_cts))
in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args]
transposed_jaxpr_, _, consts = pe.trace_to_jaxpr_dynamic(transposed, in_avals)
transposed_jaxpr = pe.convert_constvars_jaxpr(transposed_jaxpr_)
in_cts = remat_p.bind(*consts, *args, jaxpr=transposed_jaxpr, **params)
return tree_unflatten(cell.treedef, in_cts) # type: ignore
def bdim_at_front(x, bdim, size):
if core.get_aval(x) is core.abstract_unit:
return core.unit
if bdim is not_mapped:
return broadcast(x, size, 0)
else:
return moveaxis(x, bdim, 0)
def broadcast(x, sz, axis):
if core.get_aval(x) is core.abstract_unit:
return core.unit
shape = list(np.shape(x))
shape.insert(axis, sz)
broadcast_dims = tuple(np.delete(np.arange(len(shape)), axis))
return jax.lax.broadcast_in_dim(x, shape, broadcast_dims)
def aval(self):
aval = raise_to_shaped(core.get_aval(self.val))
if self.batch_dim is not_mapped or aval is core.abstract_unit:
return aval
else:
return core.mapped_aval(aval.shape[self.batch_dim], self.batch_dim,
aval)
def maybe_bdim_at_front(x, bdim):
if core.get_aval(x) is core.abstract_unit:
return core.unit
if bdim is not_mapped:
return x
else:
return util.moveaxis(x, bdim, 0)
def new(cls, val):
if val is jax_core.unit:
return InverseAndILDJ.unknown(jax_core.abstract_unit)
val = np.array(val)
aval = jax_core.get_aval(val)
aval = abstract_arrays.raise_to_shaped(aval)
ndslice = NDSlice.new(val, np.zeros_like(val))
return InverseAndILDJ(aval, frozenset([ndslice]))
def _jaxtupletree_select(pred, on_true, on_false):
aval = core.get_aval(on_true)
if type(aval) is core.AbstractTuple:
return core.pack(map(partial(_jaxtupletree_select, pred), on_true, on_false))
elif isinstance(aval, UnshapedArray):
return lax.select(pred, on_true, on_false)
else:
raise TypeError(aval)
def _flatten_jvp(in_tree, *args):
primals_in, tangents_in = split_list(args, [len(args) // 2])
py_primals = tree_unflatten(in_tree, primals_in)
py_tangents = tree_unflatten(in_tree, tangents_in)
pair_out = yield (py_primals, py_tangents), {}
if not isinstance(pair_out, (list, tuple)) or len(pair_out) != 2:
msg = (
"Custom JVP rule must produce a pair (list or tuple of length two) "
"representing primal and tangent outputs, got {}.")
raise TypeError(msg.format(pair_out))
py_primals_out, py_tangents_out = pair_out
primals_out, out_tree = tree_flatten(py_primals_out)
tangents_out, out_tree2 = tree_flatten(py_tangents_out)
if out_tree != out_tree2:
msg = (
"Custom JVP rule must produce primal and tangent outputs with equal "
"container (pytree) structures, but got {} and {} respectively.")
raise TypeError(msg.format(out_tree, out_tree2))
# TODO(mattjj): compare primals' tangent types to tangent objects' types
primal_avals_out = [
raise_to_shaped(core.get_aval(x), weak_type=False).strip_named_shape()
for x in primals_out
]
tangent_avals_out = [
raise_to_shaped(core.get_aval(t), weak_type=False).strip_named_shape()
for t in tangents_out
]
if primal_avals_out != tangent_avals_out:
if len(primal_avals_out) == 1:
(av1, ), (av2, ) = primal_avals_out, tangent_avals_out
msg = (
"Custom JVP rule must produce primal and tangent outputs with "
"equal shapes and dtypes, but got {} and {} respectively.")
raise TypeError(msg.format(av1.str_short(), av2.str_short()))
else:
msg = (
"Custom JVP rule must produce primal and tangent outputs with "
"equal shapes and dtypes, but got:\n{}")
disagreements = (
" primal {} for tangent {}".format(av1.str_short(),
av2.str_short())
for av1, av2 in zip(primal_avals_out, tangent_avals_out)
if av1 != av2)
raise TypeError(msg.format('\n'.join(disagreements)))
yield primals_out + tangents_out, out_tree
def _initial_style_jaxpr(fun, in_tree, in_avals):
in_pvals = [pe.PartialVal((aval, core.unit)) for aval in in_avals]
fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree)
jaxpr, out_pvals, consts = pe.trace_to_jaxpr(fun, in_pvals, instantiate=True)
out_avals = _map(raise_to_shaped, unzip2(out_pvals)[0])
const_avals = tuple(raise_to_shaped(core.get_aval(c)) for c in consts)
typed_jaxpr = core.TypedJaxpr(pe.closure_convert_jaxpr(jaxpr),
(), const_avals + in_avals, out_avals)
return typed_jaxpr, consts, out_tree()
def _custom_ivjp(fun, ivjp, args):
in_avals = [raise_to_shaped(get_aval(x)) for x in args]
fun_jaxpr = _initial_style_jaxpr(fun, in_avals)
try:
ivjp_jaxpr = _initial_style_jaxpr(ivjp, in_avals + fun_jaxpr.out_avals * 2)
except RecursionError:
raise ValueError("Calls to {} from its custom ivjp aren't supported yet".format(fun.__name__))
return custom_ivjp_p.bind(*args, fun_jaxpr=fun_jaxpr,
ivjp_jaxpr=ivjp_jaxpr)
def __init__(self, trace, val, batch_dim: Optional[int]):
if config.jax_enable_checks:
assert type(batch_dim) in (int, NotMapped)
if type(batch_dim) is int:
aval = raise_to_shaped(core.get_aval(val))
assert aval is core.abstract_unit or 0 <= batch_dim < len(
aval.shape) # type: ignore
self._trace = trace
self.val = val
self.batch_dim = batch_dim
def fun_remat(*args, **kwargs):
args_flat, in_tree = tree_flatten((args, kwargs))
flat_fun, out_tree = flatten_fun(lu.wrap_init(fun), in_tree)
in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args_flat]
debug = pe.debug_info(fun, in_tree, False, "checkpoint")
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
out_flat = remat_p.bind(
*consts, *args_flat, jaxpr=pe.convert_constvars_jaxpr(jaxpr),
prevent_cse=prevent_cse, differentiated=False, policy=policy)
return tree_unflatten(out_tree(), out_flat)
def write_cotangent(prim, v, ct):
# assert v not in primal_env
assert ct is not Zero, (prim, v.aval) # check for an old harmless type error
if ct is None or type(v) is Literal:
return
if type(ct) is Zero:
# FIXME: This triggers a lot of failures!
# assert v.aval == ct.aval, (prim, v.aval, ct.aval)
return
axes_to_reduce = tuple(axis_name for axis_name in reduce_axes
if axis_name in core.get_aval(ct).named_shape
and axis_name not in v.aval.named_shape)
if axes_to_reduce:
ct = jax.lax.psum(ct, axis_name=axes_to_reduce)
ct_env[v] = add_tangents(ct_env[v], ct) if v in ct_env else ct
if config.jax_enable_checks:
ct_aval = core.get_aval(ct_env[v])
joined_aval = core.lattice_join(v.aval, ct_aval).strip_weak_type().strip_named_shape()
assert v.aval.strip_weak_type().strip_named_shape() == joined_aval, (prim, v.aval, ct_aval)
def shaped_abstractify(x):
try:
return core.raise_to_shaped(core.get_aval(x))
except TypeError:
pass
weak_type = getattr(x, 'weak_type', False)
named_shape = getattr(x, 'named_shape', {})
return core.ShapedArray(np.shape(x), _dtype(x), weak_type=weak_type,
named_shape=named_shape)
def handle_sow(self, *values, name, tag, tree, mode):
"""Stores a sow in the reaps dictionary."""
del tag
if name in self.reaps:
raise ValueError(f'Variable has already been reaped: {name}')
avals = tree_util.tree_unflatten(
tree,
[abstract_arrays.raise_to_shaped(jax_core.get_aval(v)) for v in values])
self.reaps[name] = Reap(
tree_util.tree_unflatten(tree, values), dict(mode=mode, aval=avals))
return values
def __init__(self, trace, val, batch_dim: Optional[int],
source_info: Optional[source_info_util.SourceInfo] = None):
if config.jax_enable_checks:
assert type(batch_dim) in (int, NotMapped)
if type(batch_dim) is int:
aval = raise_to_shaped(core.get_aval(val))
assert batch_dim is not_mapped or 0 <= batch_dim < len(aval.shape) # type: ignore
self._trace = trace
self.val = val
self.batch_dim = batch_dim
self.source_info = source_info
def process_custom_vjp_call(self, _, __, fwd, bwd, tracers, *, out_trees):
primals_in, tangents_in = unzip2((t.primal, t.tangent) for t in tracers)
tangents_in = map(instantiate_zeros, tangents_in)
res_and_primals_out = fwd.call_wrapped(*map(core.full_lower, primals_in))
out_tree, res_tree = out_trees()
res, primals_out = split_list(res_and_primals_out, [res_tree.num_leaves])
avals_out = [raise_to_shaped(core.get_aval(x)) for x in primals_out]
tangents_out = custom_lin_p.bind(
*res, *tangents_in, num_res=res_tree.num_leaves, bwd=bwd,
out_avals=avals_out)
tangents_out = map(recast_to_float0, primals_out, tangents_out)
return map(partial(JVPTracer, self), primals_out, tangents_out)
def get_arg(a, unknown):
if unknown:
return tree_flatten(
(
tree_map(
lambda x: PartialVal.unknown(get_aval(x).at_least_vspace()), a
),
{},
)
)[0]
else:
return PartialVal.known(a)
