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[jax2tf] Fix higher-order differentiation.

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We must ensure that we call jax2tf.convert recursively to ensure
that the proper tf.custom_gradient is used. This means that we can
reuse the conversion of the VJP function between native and graph
serialization.
This commit is contained in:
George Necula 2023-09-19 11:45:38 +02:00
parent f0bde75dd3
commit 5b8f91fed7
6 changed files with 133 additions and 74 deletions
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62
jax/experimental/export/export.py
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@ -33,15 +33,15 @@ from jax import sharding
from jax._src import core
from jax._src import dispatch
from jax._src import pjit
from jax._src import sharding_impls
from jax._src import source_info_util
from jax._src.interpreters import mlir
from jax._src.interpreters import pxla
from jax._src.lib import xla_client
from jax._src.lib.mlir import ir
from jax._src.lib.mlir.dialects import hlo
from jax._src.lib.mlir.dialects import func as func_dialect
from jax._src import pjit
from jax._src import sharding_impls
from jax._src import source_info_util
from jax._src import tree_util
from jax._src import util
from jax._src import xla_bridge as xb
@ -821,35 +821,40 @@ def _check_module(mod: ir.Module, *,
raise ValueError(msg)
def _export_native_vjp(primal_fun_jax, primal: Exported) -> Exported:
# Export the VJP of `primal_fun_jax`. See documentation for Exported.vjp
def _get_vjp_fun(primal_fun: Callable, *,
in_tree: tree_util.PyTreeDef,
in_avals: Sequence[core.AbstractValue],
out_avals: Sequence[core.AbstractValue],
module_kept_var_idx: tuple[int, ...],
in_shardings,
out_shardings,
apply_jit: bool
) -> tuple[Callable, Sequence[core.AbstractValue]]:
# Since jax.vjp does not handle kwargs, it is easier to do all the work
# here with flattened functions.
def fun_vjp_jax(*args_and_out_cts_flat_jax):
# Takes a flat list of primals and output cotangents
def flattened_primal_fun_jax(*args_flat):
args, kwargs = primal.in_tree.unflatten(args_flat)
res = primal_fun_jax(*args, **kwargs)
res_flat, res_tree = tree_util.tree_flatten(res)
assert res_tree == primal.out_tree
args, kwargs = in_tree.unflatten(args_flat)
res = primal_fun(*args, **kwargs)
res_flat, _ = tree_util.tree_flatten(res)
return res_flat
args_flat_jax, out_cts_flat_jax = util.split_list(args_and_out_cts_flat_jax,
[len(primal.in_avals)])
[len(in_avals)])
_, pullback_jax = jax.vjp(flattened_primal_fun_jax, *args_flat_jax)
return pullback_jax(out_cts_flat_jax)
vjp_in_avals = list(
itertools.chain(primal.in_avals,
map(lambda a: a.at_least_vspace(), primal.out_avals)))
itertools.chain(in_avals,
map(lambda a: a.at_least_vspace(), out_avals)))
# Expand in_shardings to all in_avals even not kept ones.
all_in_shardings = [sharding_impls.UNSPECIFIED] * len(primal.in_avals)
for idx, in_s in zip(sorted(primal.module_kept_var_idx),
primal.in_shardings): # type: ignore
all_in_shardings = [sharding_impls.UNSPECIFIED] * len(in_avals)
for idx, in_s in zip(sorted(module_kept_var_idx),
in_shardings): # type: ignore
all_in_shardings[idx] = in_s # type: ignore
all_shardings = all_in_shardings + list(primal.out_shardings) # type: ignore
all_shardings = all_in_shardings + list(out_shardings) # type: ignore
# Cannot mix unspecified and specified shardings. Make the unspecified
# ones replicated.
specified_shardings = [
@ -871,14 +876,29 @@ def _export_native_vjp(primal_fun_jax, primal: Exported) -> Exported:
for s in all_shardings]
vjp_in_shardings = tuple(all_shardings)
vjp_out_shardings = tuple(all_shardings[:len(primal.in_avals)])
vjp_out_shardings = tuple(all_shardings[:len(in_avals)])
if all(sharding_impls.is_unspecified(s) for s in vjp_out_shardings):
vjp_out_shardings = sharding_impls.UNSPECIFIED
fun_vjp_jax = pjit.pjit(fun_vjp_jax,
in_shardings=vjp_in_shardings,
out_shardings=vjp_out_shardings)
if apply_jit:
return pjit.pjit(fun_vjp_jax,
in_shardings=vjp_in_shardings,
out_shardings=vjp_out_shardings), vjp_in_avals
else:
assert vjp_in_shardings == sharding_impls.UNSPECIFIED
assert vjp_out_shardings == sharding_impls.UNSPECIFIED
return fun_vjp_jax, vjp_in_avals
def _export_native_vjp(primal_fun, primal: Exported) -> Exported:
# Export the VJP of `primal_fun_jax`. See documentation for Exported.vjp
fun_vjp_jax, vjp_in_avals = _get_vjp_fun(primal_fun,
in_tree=primal.in_tree,
module_kept_var_idx=primal.module_kept_var_idx,
in_avals=primal.in_avals,
in_shardings=primal.in_shardings,
out_avals=primal.out_avals,
out_shardings=primal.out_shardings,
apply_jit=True)
return export(fun_vjp_jax,
lowering_platform=primal.lowering_platform,
disabled_checks=primal.disabled_checks)(*vjp_in_avals)

106
jax/experimental/jax2tf/jax2tf.py
View File

@ -409,7 +409,10 @@ def convert(fun_jax: Callable,
outs_tf, outs_avals, outs_tree = impl.run_fun_tf(args_flat_tf)
return (tuple(outs_tf),
_make_custom_gradient_fn_tf(
fun_jax,
impl=impl,
with_gradient=with_gradient,
args_specs=args_specs, kwargs_specs=kwargs_specs,
args_tf=args_flat_tf,
outs_avals=outs_avals,
outs_tf=outs_tf))
@ -466,18 +469,9 @@ class SerializationImpl:
"""
raise NotImplementedError
def run_vjp_fun_tf(self,
vjp_args_flat_tf: Sequence[TfVal],
outs_avals: Sequence[core.AbstractValue]) -> Sequence[TfVal]:
"""Runs the VJP function as a TF function.
Args:
vjp_args_flat_tf: the flattened sequence of tf.Tensor, including the
primal arguments followed by the output cotangents.
outs_avals: the flattened primal outputs avals
Returns: the flattened sequence of input cotangents.
"""
def get_vjp_fun(self) -> tuple[Callable,
Sequence[core.AbstractValue]]:
"""Returns the VJP function, and the VJP in_avals."""
raise NotImplementedError
@ -486,6 +480,9 @@ class NativeSerializationImpl(SerializationImpl):
args_specs, kwargs_specs,
native_serialization_platforms: Sequence[str],
native_serialization_disabled_checks: Sequence[DisabledSafetyCheck]):
self.convert_kwargs = dict(native_serialization=True,
native_serialization_platforms=native_serialization_platforms,
native_serialization_disabled_checks=native_serialization_disabled_checks)
self.fun_jax = fun_jax
self.args_specs = args_specs
self.kwargs_specs = kwargs_specs
@ -518,22 +515,23 @@ class NativeSerializationImpl(SerializationImpl):
results = _run_exported_as_tf(args_flat_tf, self.exported)
return results, tuple(self.exported.out_avals), self.exported.out_tree
def run_vjp_fun_tf(self,
vjp_args_flat_tf: Sequence[TfVal],
outs_avals: Sequence[core.AbstractValue]) -> Sequence[TfVal]:
del outs_avals
exported_vjp = self.exported.vjp()
vjp_args_flat_tf = tuple(tf.identity(arg, f"jax2tf_arg_{arg_idx}")
for arg_idx, arg in enumerate(vjp_args_flat_tf))
in_cts_flat = _run_exported_as_tf(vjp_args_flat_tf, exported_vjp)
return tuple(tf.identity(arg, "jax2tf_out") for arg in in_cts_flat)
def get_vjp_fun(self) -> tuple[Callable,
Sequence[core.AbstractValue]]:
return export._get_vjp_fun(self.fun_jax,
in_tree=self.exported.in_tree,
module_kept_var_idx=self.exported.module_kept_var_idx,
in_avals=self.exported.in_avals,
in_shardings=self.exported.in_shardings,
out_avals=self.exported.out_avals,
out_shardings=self.exported.out_shardings,
apply_jit=True)
class GraphSerializationImpl(SerializationImpl):
def __init__(self, fun_jax, *,
args_specs, kwargs_specs,
args_flat_tf: Sequence[TfVal],
enable_xla: bool):
self.convert_kwargs = dict(native_serialization=False)
self.fun_jax = fun_jax
self.args_specs = args_specs
self.kwargs_specs = kwargs_specs
@ -559,7 +557,6 @@ class GraphSerializationImpl(SerializationImpl):
_thread_local_state.include_xla_op_metadata = False
_thread_local_state.tf_outer_name_scope = tf.get_current_name_scope()
assert not _thread_local_state.shape_env, f"Unexpected shape environment {_thread_local_state.shape_env}"
args_specs_flat, self.in_tree = tree_util.tree_flatten(
(self.args_specs, self.kwargs_specs))
self.args_avals_flat = tuple(
@ -572,42 +569,34 @@ class GraphSerializationImpl(SerializationImpl):
_thread_local_state.shape_env = zip(dim_vars, dim_values)
fun_flat_jax, out_tree_thunk = flatten_fun_jax(self.fun_jax, self.in_tree)
# out_tree_thunk will be ready after we call run_fun_tf below.
self.fun_flat_jax = fun_flat_jax
self.out_tree_thunk = out_tree_thunk
def after_conversion(self):
self._restore_context()
def run_fun_tf(self,
args_flat_tf: Sequence[TfVal]
) -> tuple[Sequence[TfVal], Sequence[core.ShapedArray], tree_util.PyTreeDef]:
outs_tf, outs_avals = _interpret_fun_jax(
self.fun_flat_jax,
fun_flat_jax, out_tree_thunk = flatten_fun_jax(self.fun_jax, self.in_tree)
# out_tree_thunk will be ready after we _interpret_fun_jax below
outs_tf, self.outs_avals = _interpret_fun_jax(
fun_flat_jax,
args_flat_tf, self.args_avals_flat,
self.name_stack,
fresh_constant_cache=True)
return outs_tf, outs_avals, self.out_tree_thunk()
return outs_tf, self.outs_avals, out_tree_thunk()
def run_vjp_fun_tf(self,
vjp_args_flat_tf: Sequence[TfVal],
outs_avals: Sequence[core.AbstractValue]) -> Sequence[TfVal]:
def fun_vjp_jax(*args_and_out_cts_flat_jax):
# Takes a flat list of primals and output cotangents
args_flat_jax, out_cts_flat_jax = util.split_list(args_and_out_cts_flat_jax, [len(self.args_avals_flat)])
_, pullback_jax = jax.vjp(self.fun_flat_jax, *args_flat_jax)
return pullback_jax(out_cts_flat_jax)
vjp_in_avals = tuple(self.args_avals_flat) + tuple(outs_avals)
vjp_polymorphic_shapes = tuple(str(a.shape) # Note: may be _DimExpr, not just DimVar
for a in vjp_in_avals) # type: ignore
return convert(
fun_vjp_jax,
with_gradient=False,
polymorphic_shapes=vjp_polymorphic_shapes,
native_serialization=False)(*vjp_args_flat_tf)
def get_vjp_fun(self) -> tuple[Callable,
Sequence[core.AbstractValue]]:
# We reuse the code for native serialization to get the VJP functions,
# except we use unspecified shardings, and we do not apply a jit on the
# VJP. This matches the older behavior of jax2tf for graph serialization.
return export._get_vjp_fun(self.fun_jax,
in_tree=self.in_tree,
module_kept_var_idx=tuple(range(len(self.args_avals_flat))),
in_avals=self.args_avals_flat,
in_shardings=(sharding_impls.UNSPECIFIED,) * len(self.args_avals_flat),
out_avals=self.outs_avals,
out_shardings=(sharding_impls.UNSPECIFIED,) * len(self.outs_avals),
apply_jit=False)
def dtype_of_val(val: TfVal) -> DType:
@ -728,8 +717,11 @@ def preprocess_arg_tf(arg_idx: int,
return arg_tf
def _make_custom_gradient_fn_tf(*,
def _make_custom_gradient_fn_tf(fun_jax,
*,
impl: SerializationImpl,
with_gradient: bool,
args_specs, kwargs_specs,
args_tf: Sequence[TfVal],
outs_avals: Sequence[core.ShapedArray],
outs_tf: Sequence[TfVal]):
@ -737,6 +729,8 @@ def _make_custom_gradient_fn_tf(*,
Args:
impl: the serialization implementation details
with_gradient: whether to include a tf.custom_gradient
args_specs, kwargs_specs: the jax.ShapeDtypeArrays for the args and kwargs
args_tf: the flattened TF arguments of the primal function
outs_avals: the flattened output JAX abstract values of the primal function
outs_tf: the flattened TF outputs of the primal function
@ -765,7 +759,17 @@ def _make_custom_gradient_fn_tf(*,
out_cts_fixed_flat_tf = tuple(map(fix_out_ct, out_cts_flat_tf, outs_avals, outs_tf))
vjp_args_flat_tf = tuple(args_tf) + out_cts_fixed_flat_tf
in_cts_flat = impl.run_vjp_fun_tf(vjp_args_flat_tf, outs_avals)
fun_vjp_jax, vjp_in_avals = impl.get_vjp_fun()
vjp_polymorphic_shapes = tuple(
str(a.shape) # Note: may be _DimExpr, not just DimVar
for a in vjp_in_avals) # type: ignore
in_cts_flat = convert(
fun_vjp_jax,
with_gradient=with_gradient,
polymorphic_shapes=vjp_polymorphic_shapes,
**impl.convert_kwargs)(*vjp_args_flat_tf)
# We do not need to fix the in_cts because the TF gradient machinery
# will adjust the unconnected gradients and those for integer types.

6
jax/experimental/jax2tf/tests/call_tf_test.py
View File

@ -1295,6 +1295,12 @@ class RoundTripToTfTest(tf_test_util.JaxToTfTestCase):
def test_several_round_trips(self,
f2_function=False, f2_saved_model=False,
f4_function=False, f4_saved_model=False):
if (f2_saved_model and
f4_saved_model and
not config.jax2tf_default_native_serialization):
# TODO: Getting error Found invalid capture Tensor("jax2tf_vjp/jax2tf_arg_0:0", shape=(), dtype=float32) when saving custom gradients
# when saving f4, but only with non-native serialization.
raise unittest.SkipTest("TODO: error invalid capture when saving custom gradients")
x = np.array(.7, dtype=np.float32)
# f(n)(x) = 2. * x^n
def f(n):

16
jax/experimental/jax2tf/tests/jax2tf_test.py
View File

@ -320,6 +320,22 @@ class Jax2TfTest(tf_test_util.JaxToTfTestCase):
self.assertAllClose(5., tape.gradient(v, x))
self.assertAllClose(4., tape.gradient(v, y))
def test_higher_order_gradients(self):
f = lambda x: x ** 3
f_tf = jax2tf.convert(f)
x = tf.Variable(4.0, dtype=tf.float32) # Create a Tensorflow variable initialized to 4.0
with tf.GradientTape() as t2:
with tf.GradientTape() as t1:
y = f_tf(x)
# Compute the gradient inside the outer `t2` context manager
# which means the gradient computation is differentiable as well.
dy_dx = t1.gradient(y, x)
d2y_dx2 = t2.gradient(dy_dx, x)
self.assertAllClose(np.float32(48.), dy_dx.numpy())
self.assertAllClose(np.float32(24.), d2y_dx2.numpy())
@jtu.sample_product(with_function=[False, True])
def test_gradients_pytree(self, with_function=False):
def f(xy: tuple[float, float]) -> dict[str, float]:

4
jax/experimental/jax2tf/tests/sharding_test.py
View File

@ -401,14 +401,14 @@ class ShardingTest(tf_test_util.JaxToTfTestCase):
# Annotation count for the primal input and the grad output
count_in_P = self.GEQ(2) if in_shardings == "P" else 0
if config.jax2tf_default_native_serialization:
# With native serialization even unspecified in_shardings turn into replicated
# With native serialization even unspecified shardings turn into replicated
count_in_replicated = self.GEQ(2) if in_shardings in [None, "missing"] else 0
else:
count_in_replicated = self.GEQ(2) if in_shardings is None else 0
# Annotation count for the contangent input
count_out_P = self.GEQ(1) if out_shardings == "P" else 0
if config.jax2tf_default_native_serialization:
# With native serialization even unspecified in_shardings turn into replicated
# With native serialization even unspecified shardings turn into replicated
count_out_replicated = self.GEQ(1) if out_shardings in [None, "missing"] else 0
else:
count_out_replicated = self.GEQ(1) if out_shardings is None else 0

13
tests/export_test.py
View File

@ -249,6 +249,19 @@ class JaxExportTest(jtu.JaxTestCase):
f1 = export.call_exported(exp_f)
self.assertAllClose(jax.grad(f)(x), jax.grad(f1)(x))
def test_higher_order_grad(self):
f = lambda x: x ** 3
x = np.float32(4.)
exp_f = export.export(f)(x)
f1 = export.call_exported(exp_f)
self.assertAllClose(jax.grad(f)(x),
jax.grad(f1)(x))
self.assertAllClose(jax.grad(jax.grad(f))(x),
jax.grad(jax.grad(f1))(x))
self.assertAllClose(jax.grad(jax.grad(jax.grad(f)))(x),
jax.grad(jax.grad(jax.grad(f1)))(x))
def test_pytree_vjp(self):
def f(a_b_pair, *, a, b):
return (dict(res=a_b_pair, a=2. * a, b=3. * b),