Revision 66b182b735503db3a7bbc956b2fc1d30d897505b authored by Matthew Johnson on 06 December 2019, 15:44:12 UTC, committed by Matthew Johnson on 06 December 2019, 19:12:59 UTC
1 parent 1da950d
api_test.py
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from functools import partial
import unittest
import warnings
import weakref
from absl import logging
from absl.testing import absltest
import numpy as onp
import six
if six.PY3:
import concurrent.futures
import jax
import jax.numpy as np
from jax import jit, grad, device_put, jacfwd, jacrev, hessian
from jax import api, lax
from jax.core import Primitive
from jax.interpreters import ad
from jax.interpreters.xla import DeviceArray
from jax.abstract_arrays import concretization_err_msg
from jax.lib import xla_bridge as xb
from jax import test_util as jtu
from jax import tree_util
from jax.config import config
config.parse_flags_with_absl()
FLAGS = config.FLAGS
class APITest(jtu.JaxTestCase):
def test_grad_argnums(self):
def f(x, y, z, flag=False):
assert flag
return 1.0 * x + 2.0 * y + 3.0 * z
assert grad(f)(1.0, 1.0, 1.0, flag=True) == 1.0
assert grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == 2.0
assert grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (3.0, 1.0)
def test_value_and_grad_argnums(self):
def f(x, y, z, flag=False):
assert flag
return 1.0 * x + 2.0 * y + 3.0 * z
y = f(1.0, 1.0, 1.0, flag=True)
assert api.value_and_grad(f)(1.0, 1.0, 1.0, flag=True) == (y, 1.0)
assert api.value_and_grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == (y, 2.0)
assert api.value_and_grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (y, (3.0, 1.0))
def test_jit_static_args(self):
side = []
def f(x, y, z, flag=False, flag2=False):
assert flag
side.append(None)
return 100*x + 10*y + z
f1 = jit(f, static_argnums=(3, 4))
assert f1(1, 2, 3, True, False) == 123
assert len(side) == 1
assert f1(2, 1, 3, True, False) == 213
assert len(side) == 1
assert f1(2, 1, 3, True, True) == 213
assert len(side) == 2
side[:] = []
f2 = jit(f, static_argnums=(0, 2, 3, 4))
assert f2(1, 2, 3, True, False) == 123
assert len(side) == 1
assert f2(1, 3, 3, True, False) == 133
assert len(side) == 1
assert f2(2, 2, 3, True, False) == 223
assert len(side) == 2
assert f2(2, 4, 3, True, False) == 243
assert len(side) == 2
assert f2(2, 4, 3, True, True) == 243
assert len(side) == 3
assert f2(2, 5, 3, True, True) == 253
assert len(side) == 3
def test_jit_kwargs(self):
side = []
def f(x, y, z):
side.append(None)
return 100*x + 10*y + z
f = jit(f)
assert f(1, 2, 3) == 123
assert len(side) == 1
assert f(1, 2, 3) == 123
assert len(side) == 1
assert f(1, 2, z=3) == 123
assert len(side) == 2 # actually recompiles from kwarg
assert f(1, 2, z=3) == 123
assert len(side) == 2 # but should still cache
f(1, 2, z=onp.zeros(3)) # doesn't crash
def test_jit_many_args(self):
@jit
def f(args_list):
return sum(args_list)
self.assertEqual(f(list(range(500))), sum(range(500)))
def test_grad_of_jit(self):
side = []
@jit
def f(x):
side.append(None)
return x * x
assert grad(f)(1.0) == 2.0
assert len(side) == 1
assert grad(f)(2.0) == 4.0
assert len(side) == 1
def test_jit_of_grad(self):
side = []
@jit
def f(x):
side.append(None)
return x * x
g = jit(grad(f))
assert g(1.0) == 2.0
assert len(side) == 1
assert g(2.0) == 4.0
assert len(side) == 1
def test_bad_input(self):
def f(x):
return x
self.assertRaisesRegex(
TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type",
lambda: grad(f)("foo"))
self.assertRaisesRegex(
TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type",
lambda: jit(f)("foo"))
def test_grad_tuple_output(self):
jtu.check_raises(lambda: grad(lambda x: (x,x))(1.0), TypeError,
"Gradient only defined for scalar-output functions. ")
def test_grad_unit_output(self):
jtu.check_raises(lambda: grad(lambda x: ())(onp.zeros(3)), TypeError,
"Gradient only defined for scalar-output functions. ")
def test_grad_nonscalar_output(self):
jtu.check_raises(lambda: grad(lambda x: x)(onp.zeros(3)), TypeError,
"Gradient only defined for scalar-output functions. ")
def test_unwrapped_numpy(self):
def f(x):
return onp.exp(x)
jtu.check_raises(lambda: grad(f)(onp.zeros(3)), Exception,
"Tracer can't be used with raw numpy functions. "
"You might have\n import numpy as np\ninstead of\n"
" import jax.numpy as np")
def test_binop_mismatch(self):
def f(x, y):
return x + y
jtu.check_raises(
lambda: f(np.zeros(3), np.zeros(4)),
TypeError,
"add got incompatible shapes for broadcasting: (3,), (4,).")
jtu.check_raises(
lambda: grad(f)(onp.zeros(3), onp.zeros(4)),
TypeError,
"add got incompatible shapes for broadcasting: (3,), (4,).")
def test_dot_mismatch(self):
def f(x, y):
return np.dot(x, y)
self.assertRaisesRegex(
TypeError, "Incompatible shapes for dot: got \\(3L?,\\) and \\(4L?,\\).",
lambda: grad(f)(onp.zeros(3), onp.zeros(4)))
def test_switch_value_jit(self):
def f(x):
y = x > 0
if y:
return x
else:
return -x
assert grad(f)(1.0) == 1.0
assert grad(f)(-1.0) == -1.0
jtu.check_raises(lambda: jit(f)(1), TypeError, concretization_err_msg(bool))
def test_range_err(self):
def f(x, n):
for i in range(n):
x = x + i
return x
assert jit(f, static_argnums=(1,))(0, 5) == 10
self.assertRaisesRegex(
TypeError,
"('JaxprTracer' object cannot be interpreted as an integer"
"|Abstract value passed to .*)",
lambda: jit(f)(0, 5))
def test_casts(self):
for castfun in [float, complex, hex, oct] + list(six.integer_types):
f = lambda x: castfun(x)
self.assertRaisesRegex(
TypeError,
"('JaxprTracer' object cannot be interpreted as an integer"
"|Abstract value passed to .*)", lambda: jit(f)(0))
def test_unimplemented_interpreter_rules(self):
foo_p = Primitive('foo')
def foo(x):
return foo_p.bind(x)
jtu.check_raises(lambda: foo(1.0), NotImplementedError,
"Evaluation rule for 'foo' not implemented")
jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError,
"Abstract evaluation for 'foo' not implemented")
jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError,
"Forward-mode differentiation rule for 'foo' not implemented")
foo_p.def_abstract_eval(lambda x: x)
jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError,
"XLA translation rule for primitive 'foo' not found")
foo_p.def_impl(lambda x: x)
ad.defjvp(foo_p, lambda g, x: foo(g))
jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError,
"Reverse-mode differentiation rule for 'foo' not implemented")
def test_device_put_and_get(self):
x = onp.arange(12.).reshape((3, 4)).astype("float32")
dx = api.device_put(x)
self.assertIsInstance(dx, DeviceArray)
x2 = api.device_get(dx)
self.assertIsInstance(x2, onp.ndarray)
assert onp.all(x == x2)
y = [x, (2 * x, 3 * x)]
dy = api.device_put(y)
y2 = api.device_get(dy)
self.assertIsInstance(y2, list)
self.assertIsInstance(y2[0], onp.ndarray)
assert onp.all(y2[0] == x)
self.assertIsInstance(y2[1], tuple)
self.assertIsInstance(y2[1][0], onp.ndarray)
assert onp.all(y2[1][0] == 2 * x)
self.assertIsInstance(y2[1][1], onp.ndarray)
assert onp.all(y2[1][1] == 3 * x)
def test_device_put_across_devices(self):
if xb.device_count() == 1:
raise unittest.SkipTest("this test requires multiple devices")
d1, d2 = xb.local_devices()[:2]
x = api.device_put(onp.array([1,2,3]), device=d1)
self.assertEqual(x.device_buffer.device(), d1)
y = api.device_put(x, device=d2)
self.assertEqual(y.device_buffer.device(), d2)
# Make sure these don't crash
api.device_put(x)
api.device_put(y)
@jtu.skip_on_devices("cpu")
def test_device_put_across_platforms(self):
default_device = jax.devices()[0]
cpu_device = jax.devices("cpu")[0]
onp_arr = onp.array([1,2,3])
scalar = 1
device_arr = np.array([1,2,3])
assert device_arr.device_buffer.device() is default_device
for val in [onp_arr, device_arr, scalar]:
x = api.device_put(val, device=cpu_device)
self.assertEqual(x.device_buffer.device(), cpu_device)
y = api.device_put(x)
self.assertEqual(y.device_buffer.device(), default_device)
@jtu.skip_on_devices("tpu")
def test_jacobian(self):
R = onp.random.RandomState(0).randn
A = R(4, 3)
x = R(3)
f = lambda x: np.dot(A, x)
assert onp.allclose(jacfwd(f)(x), A)
assert onp.allclose(jacrev(f)(x), A)
f = lambda x: np.tanh(np.dot(A, x))
assert onp.allclose(jacfwd(f)(x), jacrev(f)(x))
@jtu.skip_on_devices("tpu")
def test_hessian(self):
R = onp.random.RandomState(0).randn
A = R(4, 4)
x = R(4)
f = lambda x: np.dot(x, np.dot(A, x))
assert onp.allclose(hessian(f)(x), A + A.T)
def test_std_basis(self):
basis = api._std_basis(np.zeros(3))
assert getattr(basis, "shape", None) == (3, 3)
assert onp.allclose(basis, onp.eye(3))
basis = api._std_basis(np.zeros((3, 3)))
assert getattr(basis, "shape", None) == (9, 3, 3)
assert onp.allclose(basis, onp.eye(9).reshape(9, 3, 3))
basis = api._std_basis([0., (np.zeros(3), np.zeros((3, 4)))])
assert isinstance(basis, list) and len(basis) == 2
assert getattr(basis[0], "shape", None) == (16,)
assert isinstance(basis[1], tuple) and len(basis[1]) == 2
assert getattr(basis[1][0], "shape", None) == (16, 3)
assert getattr(basis[1][1], "shape", None) == (16, 3, 4)
@jtu.skip_on_devices("tpu")
def test_jacobian_on_pytrees(self):
for jacfun in [jacfwd, jacrev]:
ans = jacfun(lambda x, y: (x, y))(0., 1.)
expected = (1., 0.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = jacfun(lambda x, y: (x, y), 1)(0., 1.)
expected = (0., 1.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = jacfun(lambda x, y: (x, y), (0, 1))(0., 1.)
expected = ((1., 0.),
(0., 1.),)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = jacfun(lambda x: x[:2])((1., 2., 3.))
expected = ((1., 0., 0.),
(0., 1., 0.))
self.assertAllClose(ans, expected, check_dtypes=False)
R = onp.random.RandomState(0).randn
x = R(2)
y = R(3)
ans = jacfun(lambda x, y: {'x': x, 'xy': np.outer(x, y)})(x, y)
expected = {'x': onp.eye(2),
'xy': onp.kron(onp.eye(2), y[:, None]).reshape(2, 3, 2)}
self.assertAllClose(ans, expected, check_dtypes=False)
@jtu.skip_on_devices("tpu")
def test_hessian_on_pytrees(self):
ans = hessian(lambda x: np.array(x)**2)((1., 2.))
expected = ((onp.array([2., 0.]), onp.array([0., 0.])),
(onp.array([0., 0.]), onp.array([0., 2.])))
self.assertAllClose(ans, expected, check_dtypes=False)
@jtu.skip_on_devices("tpu")
def test_issue1372(self):
def quad(x):
return np.dot(x, x)
def f(x, u):
return quad(x) + quad(u)
x, u = np.ones(5), np.ones(2)
rev = jacrev
fwd = jacfwd
# Diagonal entries
self.assertEqual(rev(rev(f, 0), 0)(x, u).shape, (5, 5))
self.assertEqual(rev(fwd(f, 0), 0)(x, u).shape, (5, 5))
self.assertEqual(fwd(rev(f, 0), 0)(x, u).shape, (5, 5))
self.assertEqual(fwd(fwd(f, 0), 0)(x, u).shape, (5, 5))
self.assertEqual(rev(rev(f, 1), 1)(x, u).shape, (2, 2))
self.assertEqual(rev(fwd(f, 1), 1)(x, u).shape, (2, 2))
self.assertEqual(fwd(rev(f, 1), 1)(x, u).shape, (2, 2))
self.assertEqual(fwd(fwd(f, 1), 1)(x, u).shape, (2, 2))
# Off-diagonal entries by reverse-mode on the outside
self.assertEqual(rev(rev(f, 1), 0)(x, u).shape, (2, 5))
self.assertEqual(rev(fwd(f, 1), 0)(x, u).shape, (2, 5))
self.assertEqual(rev(rev(f, 0), 1)(x, u).shape, (5, 2))
self.assertEqual(rev(fwd(f, 0), 1)(x, u).shape, (5, 2))
# Off-diagonal entries by forward-mode on the outside
self.assertEqual(fwd(rev(f, 1), 0)(x, u).shape, (2, 5))
self.assertEqual(fwd(fwd(f, 1), 0)(x, u).shape, (2, 5))
self.assertEqual(fwd(rev(f, 0), 1)(x, u).shape, (5, 2))
self.assertEqual(fwd(fwd(f, 0), 1)(x, u).shape, (5, 2))
def test_disable_jit(self):
effects = []
@api.jit
def f(x):
effects.append(1)
return x
with api.disable_jit():
f(2)
f(2)
assert len(effects) == 2
f(2)
f(2)
assert len(effects) == 3
def test_large_device_constant(self):
ans = jit(lambda x: 2 * x)(np.ones(int(2e6))) # doesn't crash
self.assertAllClose(ans, onp.ones(int(2e6)) * 2., check_dtypes=False)
def test_grad_and_aux_basic(self):
g, aux = grad(lambda x: (x**3, [x**2]), has_aux=True)(3.)
self.assertAllClose(g, grad(lambda x: x**3)(3.), check_dtypes=True)
self.assertAllClose(aux, [9.], check_dtypes=True)
def test_grad_and_aux_nested(self):
def f(x):
g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x)
return aux[0]
f2 = lambda x: x**3
self.assertEqual(grad(f)(4.), grad(f2)(4.))
self.assertEqual(jit(grad(f))(4.), grad(f2)(4.))
self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.))
def f(x):
g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x)
return aux[0] * np.sin(x)
f2 = lambda x: x**3 * np.sin(x)
self.assertEqual(grad(f)(4.), grad(f2)(4.))
self.assertEqual(jit(grad(f))(4.), grad(f2)(4.))
self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.))
def test_grad_and_aux_constant(self):
g, aux = grad(lambda x: (x**3, [4.]), has_aux=True)(4.)
self.assertEqual(g, grad(lambda x: x**3)(4.))
self.assertEqual(aux, [4.])
g, aux = grad(lambda x: (x**3, [x**2, 4.]), has_aux=True)(4.)
self.assertEqual(g, grad(lambda x: x**3)(4.))
self.assertEqual(aux, [4.**2, 4.])
def test_jvp_mismatched_arguments(self):
self.assertRaisesRegex(
TypeError,
("primal and tangent arguments to jax.jvp must have the same tree "
"structure"),
lambda: api.jvp(lambda x, y: x * y, (onp.float32(2),), ()))
# If primals and tangents must both be tuples or both lists
self.assertRaisesRegex(
TypeError,
("primal and tangent arguments to jax.jvp must have the same tree "
"structure"),
lambda: api.jvp(lambda x, y: x * y, (onp.float32(2),), [onp.float32(2)]))
self.assertRaisesRegex(
TypeError,
"primal and tangent arguments to jax.jvp must have equal types",
lambda: api.jvp(lambda x: -x, (onp.float16(2),), (onp.float32(4),)))
def test_jvp_non_tuple_arguments(self):
def f(x, y): return x + y
self.assertRaisesRegex(
TypeError,
"primal and tangent arguments to jax.jvp must be tuples or lists; found float and tuple.",
lambda: partial(api.jvp(f, 0., (1.,))))
self.assertRaisesRegex(
TypeError,
"primal and tangent arguments to jax.jvp must be tuples or lists; found tuple and ndarray.",
lambda: partial(api.jvp(f, (0.,), onp.array([1., 2.]))))
def test_vjp_mismatched_arguments(self):
_, pullback = api.vjp(lambda x, y: x * y, onp.float32(3), onp.float32(4))
self.assertRaisesRegex(
TypeError,
"Tree structure of cotangent input.*does not match",
lambda: pullback((onp.float32(7), onp.float32(100))))
self.assertRaisesRegex(
TypeError,
"Type of cotangent input to vjp pullback.*does not match type",
lambda: pullback((onp.float16(42))))
def test_jarrett_jvps(self):
def f1(x):
return np.sin(np.sin(np.sin(x)))
f2 = api.jarrett(f1)
for x in [3., onp.array([2., 3., 4.])]:
self.assertAllClose(f1(x), f2(x), check_dtypes=True)
_, f1_vjp = api.vjp(f1, x)
_, f2_vjp = api.vjp(f2, x)
self.assertAllClose(f1_vjp(x), f2_vjp(x), check_dtypes=True)
# TODO(mattjj): test that constants/literals are set up properly
# jaxpr2 = api.make_jaxpr(f2_vjp)(x)
# assert len(jaxpr2.constvars) == 1
def test_jarrett_jvps2(self):
def f1(x, y):
return np.sin(x) * np.cos(y) * np.sin(x) * np.cos(y)
f2 = api.jarrett(f1)
# TODO(mattjj): doesn't work for (3., onp.array([4., 5.]))
for x, y in [(3., 4.), (onp.array([5., 6.]), onp.array([7., 8.]))]:
self.assertAllClose(f1(x, y), f2(x, y), check_dtypes=True)
_, f1_vjp = api.vjp(f1, x, y)
_, f2_vjp = api.vjp(f2, x, y)
self.assertAllClose(f1_vjp(y), f2_vjp(y), check_dtypes=True)
# TODO(mattjj): test that constants/literals are set up properly
# jaxpr2 = api.make_jaxpr(f2_vjp)(y)
# assert len(jaxpr2.constvars) == 2
def test_complex_grad_raises_error(self):
self.assertRaises(TypeError, lambda: grad(lambda x: np.sin(x))(1 + 2j))
def test_holomorphic_grad(self):
out = grad(lambda x: np.sin(x), holomorphic=True)(1 + 2j)
expected = 2.0327230070196656 - 3.0518977991518j
self.assertAllClose(out, expected, check_dtypes=False)
def test_nonholomorphic_grad(self):
zs = 0.5j * onp.arange(5) + onp.arange(5)
def f(z):
return np.sum(np.cos(np.abs(z)))
ans = grad(f)(zs)
expected = onp.array([ 0. +0.j,
-0.80430663+0.40215331j,
-0.70368982+0.35184491j,
0.1886467 -0.09432335j,
0.86873727-0.43436864j])
self.assertAllClose(ans, expected, check_dtypes=False,
atol=jtu.default_gradient_tolerance,
rtol=jtu.default_gradient_tolerance)
def test_complex_output_jacrev_raises_error(self):
self.assertRaises(TypeError, lambda: jacrev(lambda x: np.sin(x))(1 + 2j))
def test_nonholomorphic_jacrev(self):
# code based on https://github.com/google/jax/issues/603
zs = 0.5j * onp.arange(5) + onp.arange(5)
def f(z):
return np.cos(np.linalg.norm(2 * z))
ans = jacrev(f)(zs)
expected = grad(f)(zs)
self.assertAllClose(ans, expected, check_dtypes=True)
def test_complex_input_jacfwd_raises_error(self):
self.assertRaises(TypeError, lambda: jacfwd(lambda x: np.sin(x))(1 + 2j))
def test_defvjp_all(self):
foo_p = Primitive('foo')
def foo(x): return 2. * foo_p.bind(x)
ad.defvjp_all(foo_p, lambda x: (x**2, lambda g: (4 * g * np.sin(x),)))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, 2 * 3.**2, check_dtypes=False)
self.assertAllClose(grad_ans, 4 * 2 * onp.sin(3.), check_dtypes=False)
def test_defvjp_all_const(self):
foo_p = Primitive('foo')
def foo(x): return foo_p.bind(x)
ad.defvjp_all(foo_p, lambda x: (x**2, lambda g: (12.,)))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, 9., check_dtypes=False)
self.assertAllClose(grad_ans, 12., check_dtypes=True)
def test_defvjp_all_higher_order_revmode(self):
foo_p = Primitive('foo')
def foo(x): return 2. * foo_p.bind(x)
ad.defvjp_all(foo_p, lambda x: (x**2, lambda g: (g * x ** 2,)))
ans = api.grad(api.grad(foo))(3.)
self.assertAllClose(ans, 2 * 2 * 3., check_dtypes=False)
def test_defvjp_all_multiple_arguments(self):
# also tests passing in symbolic zero tangents b/c we differentiate wrt only
# the first argument in one case
foo_p = Primitive('foo')
def foo(x, y): return foo_p.bind(x, y)
def vjpfun(x, y):
out = x**2 + y**3
vjp = lambda g: (g + x + y, g * x * 9.)
return out, vjp
ad.defvjp_all(foo_p, vjpfun)
val_ans, grad_ans = api.value_and_grad(foo)(3., 4.)
self.assertAllClose(val_ans, 3.**2 + 4.**3, check_dtypes=False)
self.assertAllClose(grad_ans, 1. + 3. + 4., check_dtypes=False)
ans = api.grad(foo, (0, 1))(3., 4.)
self.assertAllClose(ans, (1. + 3. + 4., 1. * 3. * 9.), check_dtypes=False)
def test_defvjp_all(self):
@api.custom_transforms
def foo(x):
return np.sin(x)
api.defvjp_all(foo, lambda x: (np.sin(x), lambda g: (g * x,)))
val_ans, grad_ans = api.value_and_grad(foo)(3.)
self.assertAllClose(val_ans, onp.sin(3.), check_dtypes=False)
self.assertAllClose(grad_ans, 3., check_dtypes=False)
# TODO(mattjj): add defvjp_all test with pytree arguments
def test_defvjp(self):
@api.custom_transforms
def foo(x, y):
return np.sin(x * y)
api.defvjp(foo, None, lambda g, _, x, y: g * x * y)
val_ans, grad_ans = api.value_and_grad(foo)(3., 4.)
self.assertAllClose(val_ans, onp.sin(3. * 4.), check_dtypes=False)
self.assertAllClose(grad_ans, 0., check_dtypes=False)
ans_0, ans_1 = api.grad(foo, (0, 1))(3., 4.)
self.assertAllClose(ans_0, 0., check_dtypes=False)
self.assertAllClose(ans_1, 3. * 4., check_dtypes=False)
def test_defvjp_higher_order(self):
@api.custom_transforms
def foo(x):
return np.sin(2. * x)
api.defvjp(foo, lambda g, _, x: g * np.cos(x))
ans = api.grad(api.grad(foo))(2.)
expected = api.grad(api.grad(np.sin))(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
def test_defvjp_use_ans(self):
@api.custom_transforms
def foo(x, y):
return np.sin(x * y)
api.defvjp(foo, None, lambda g, ans, x, y: g * x * y + np.cos(ans))
val_ans, grad_ans = api.value_and_grad(foo, 1)(3., 4.)
self.assertAllClose(val_ans, onp.sin(3. * 4.), check_dtypes=False)
self.assertAllClose(grad_ans, 3. * 4. + onp.cos(onp.sin(3. * 4)),
check_dtypes=False)
# TODO
# def test_defjvp_closure_error(self):
# def foo(x):
# @api.custom_transforms
# def bar(y):
# return x * y
# api.defjvp(bar, lambda y_dot, ans, y: x * y)
# return bar(x)
# jtu.check_raises(
# lambda: api.jvp(foo, (1.,), (1.,)), ValueError,
# "Detected differentiation with respect to closed-over values with "
# "custom JVP rule, which isn't supported.")
# TODO
# def test_defvjp_closure_error(self):
# def foo(x):
# @api.custom_transforms
# def bar(y):
# return x * y
# api.defvjp(bar, lambda g, ans, y: x * y)
# return bar(x)
# jtu.check_raises(
# lambda: grad(foo)(1.,), ValueError,
# "Detected differentiation w.r.t. variables from outside "
# "the scope of <jax.custom_transforms function bar>, but defvjp and "
# "defvjp_all only support differentiation w.r.t. positional arguments.")
def test_custom_transforms_eval_with_pytrees(self):
@api.custom_transforms
def f(x):
a, b = x[0], x[1]
return {'hi': 2 * a, 'bye': 2 * b}
ans = f((1, 2))
self.assertEqual(ans, {'hi': 2 * 1, 'bye': 2 * 2})
def test_custom_transforms_jit_with_pytrees(self):
@api.custom_transforms
def f(x):
a, b = x[0], x[1]
return {'hi': 2 * a, 'bye': 2 * b}
ans = jit(f)((1, 2))
self.assertEqual(ans, {'hi': 2 * 1, 'bye': 2 * 2})
def test_custom_transforms_jit_with_pytrees_consts(self):
# The purpose of this test is to exercise the custom_transforms default
# translation rule in how it deals with constants that are too large to be
# treated as literals (at the time of writing).
z = onp.arange(10.)
@api.custom_transforms
def f(x):
a, b = x[0], x[1]
return {'hi': 2 * a, 'bye': z * b}
ans = jit(f)((1, 2))
self.assertAllClose(ans, {'hi': 2 * 1, 'bye': z * 2}, check_dtypes=False)
def test_custom_transforms_jvp_with_pytrees(self):
@api.custom_transforms
def f(x):
a, b = x[0], x[1]
return {'hi': 2 * a, 'bye': 2 * b}
ans, out_tangent = api.jvp(f, ((1, 2),), ((3, 4),))
self.assertEqual(ans, {'hi': 2 * 1, 'bye': 2 * 2})
self.assertEqual(out_tangent, {'hi': 2 * 3, 'bye': 2 * 4})
def test_custom_transforms_vmap_with_pytrees(self):
@api.custom_transforms
def f(x):
a, b = x[0], x[1]
return {'hi': 2 * a, 'bye': 2 * b}
ans = api.vmap(f)((onp.arange(3), onp.ones((3, 2))))
expected = {'hi': 2 * onp.arange(3), 'bye': 2 * onp.ones((3, 2))}
self.assertAllClose(ans, expected, check_dtypes=False)
def test_custom_transforms_jvp_with_closure(self):
def f(x):
@api.custom_transforms
def g(y):
return x * y
return g(x)
ans = api.grad(f)(1.)
expected = 2.
self.assertAllClose(ans, expected, check_dtypes=False)
def test_custom_gradient(self):
@api.custom_gradient
def f(x):
return x ** 2, lambda g: (g * x,)
self.assertAllClose(f(3.), 9., check_dtypes=False)
self.assertAllClose(api.grad(f)(3.), 3., check_dtypes=False)
def test_legacy_devicearray_repr(self):
dx = device_put(3.)
str(dx.item()) # doesn't crash
def test_devicearray_repr(self):
x = device_put(np.zeros(3))
self.assertIsInstance(x, DeviceArray)
repr(x) # doesn't crash
x = device_put(np.ones(3) + 1j * np.ones(3))
self.assertIsInstance(x, DeviceArray)
repr(x) # doesn't crash
def test_devicearray_delete(self):
x = device_put(1.)
x.delete()
self.assertRaisesRegex(ValueError, "DeviceValue has been deleted.",
lambda: repr(x))
def test_devicearray_block_until_ready(self):
x = device_put(1.)
y = x.block_until_ready()
# Tests mostly that block_until_ready() does not produce an error.
self.assertTrue(y is x)
def test_namedtuple_transparency(self):
# See https://github.com/google/jax/issues/446
Point = collections.namedtuple("Point", ["x", "y"])
def f(pt):
return np.sqrt(pt.x ** 2 + pt.y ** 2)
pt = Point(1., 2.)
f(pt) # doesn't crash
g = api.grad(f)(pt)
self.assertIsInstance(g, Point)
f_jit = api.jit(f)
self.assertAllClose(f(pt), f_jit(pt), check_dtypes=False)
def test_namedtuple_subclass_transparency(self):
# See https://github.com/google/jax/issues/806
Point = collections.namedtuple("Point", ["x", "y"])
class ZeroPoint(Point):
def is_zero(self):
return (self.x == 0) and (self.y == 0)
pt = ZeroPoint(0., 0.)
def f(pt):
return 0. if pt.is_zero() else np.sqrt(pt.x ** 2 + pt.y ** 2)
f(pt) # doesn't crash
g = api.grad(f)(pt)
self.assertIsInstance(pt, ZeroPoint)
def test_eval_shape(self):
def fun(x, y):
return np.tanh(np.dot(x, y) + 3.)
x = np.ones((2, 3))
y = np.ones((3, 4))
out_shape = api.eval_shape(fun, x, y)
self.assertEqual(out_shape.shape, (2, 4))
def test_eval_shape_constants(self):
def fun():
x = np.ones((2, 3))
y = np.ones((3, 4))
return np.tanh(np.dot(x, y) + 3.)
out_shape = api.eval_shape(fun)
self.assertEqual(out_shape.shape, (2, 4))
def test_eval_shape_tuple_unpacking(self):
def fun(x, y):
a, b = x
return a + b + y
x = (np.ones(2), np.ones(2))
y = 3.
out_shape = api.eval_shape(fun, x, y)
self.assertEqual(out_shape.shape, (2,))
def test_eval_shape_tuple_itemgetting(self):
def fun(x, y):
return x[0] + x[1] + y
x = (np.ones(2), np.ones(2))
y = 3.
out_shape = api.eval_shape(fun, x, y)
self.assertEqual(out_shape.shape, (2,))
def test_eval_shape_output_dict(self):
def fun(x, y):
return {'hi': x[0] + x[1] + y}
x = (np.ones(2), np.ones(2))
y = 3.
out_shape = api.eval_shape(fun, x, y)
out_shape = tree_util.tree_map(onp.shape, out_shape)
self.assertEqual(out_shape, {'hi': (2,)})
def test_eval_shape_shape_error(self):
def fun(x, y):
return np.tanh(np.dot(x, y) + 3.)
x = np.ones((3, 3))
y = np.ones((4, 4))
self.assertRaises(TypeError, lambda: api.eval_shape(fun, x, y))
def test_eval_shape_duck_typing(self):
def fun(A, b, x):
return np.dot(A, x) + b
class MyArgArray(object):
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
A = MyArgArray((3, 4), np.float32)
b = MyArgArray((5,), np.float32)
x = MyArgArray((4, 5), np.float32)
out_shape = api.eval_shape(fun, A, b, x)
self.assertEqual(out_shape.shape, (3, 5))
def test_issue_871(self):
T = np.array([[1., 2.], [3., 4.], [5., 6.]])
x = np.array([1, 2, 3])
y, f_jvp = api.linearize(np.sum, x)
jtu.check_raises(lambda: f_jvp(T), ValueError,
("linearized function called on tangent values "
"inconsistent with the original primal values."))
y, f_jvp = api.linearize(api.jit(np.sum), x)
jtu.check_raises(lambda: f_jvp(T), ValueError,
("linearized function called on tangent values "
"inconsistent with the original primal values."))
def test_partial_eval_lower(self):
# this is a simplified model of a bug that arose when we first used @jit in
# a jvp rule. it's in this file because we want to use make_jaxpr.
@api.jit
def f(a, b, c):
a = lax.broadcast(a, (2,))
return lax.select(a, b, c)
a = onp.ones((3, 3), dtype=onp.bool_)
b = onp.ones((2, 3, 3))
c = onp.ones((2, 3, 3))
jaxpr = api.make_jaxpr(lambda b, c: f(a, b, c))(b, c)
subjaxpr = next(eqn.bound_subjaxprs[0][0] for eqn in jaxpr.jaxpr.eqns
if eqn.bound_subjaxprs)
self.assertEqual(len(subjaxpr.eqns), 1)
def test_grad_of_int_errors(self):
dfn = grad(lambda x: x ** 2)
self.assertRaisesRegex(
TypeError,
"Primal inputs to reverse-mode differentiation must be of float or "
"complex type, got type int..", lambda: dfn(3))
def test_xla_computation(self):
# these tests basically check the examples in the xla_computation docstring
def h(x):
return np.sin(np.cos(x))
c = api.xla_computation(h)(2.)
self.assertIn('cosine', c.GetHloText())
self.assertIn('sine', c.GetHloText())
def f(x):
return x - lax.psum(x, 'i')
axis_env = [('i', 4)]
c = api.xla_computation(f, axis_env=axis_env)(2)
self.assertIn('all-reduce', c.GetHloText())
self.assertIn('replica_groups={{0,1,2,3}}', c.GetHloText())
def g(x):
rowsum = lax.psum(x, 'i')
colsum = lax.psum(x, 'j')
allsum = lax.psum(x, ('i', 'j'))
return rowsum, colsum, allsum
axis_env = [('i', 4), ('j', 2)]
c = api.xla_computation(g, axis_env=axis_env)(5.)
self.assertIn('all-reduce', c.GetHloText())
self.assertIn('replica_groups={{0,2,4,6},{1,3,5,7}}', c.GetHloText())
self.assertIn('replica_groups={{0,1},{2,3},{4,5},{6,7}}', c.GetHloText())
self.assertIn('replica_groups={{0,1,2,3,4,5,6,7}}', c.GetHloText())
def test_xla_computation_args(self):
def foo(x, y, z):
return x + y + z
c = api.xla_computation(foo)(1., 2., 3.)
self.assertEqual(len(c.GetProgramShape().parameter_shapes()), 3)
c = api.xla_computation(foo, tuple_args=True)(1., 2., 3.)
param_shapes = c.GetProgramShape().parameter_shapes()
self.assertEqual(len(param_shapes), 1)
self.assertEqual(param_shapes[0].xla_element_type(),
xb.xla_client.PrimitiveType.TUPLE)
def test_staging_out_multi_replica(self):
def f(x):
return api.pmap(np.mean)(x)
xla_comp = api.xla_computation(f)
xla_comp(np.arange(8)).GetHloText() # doesn't crash
def test_xla_computation_instantiate_constant_outputs(self):
def f():
return np.zeros((3, 4))
xla_comp = api.xla_computation(f, instantiate_const_outputs=True)()
out_shape, = xla_comp.GetReturnValueShape().tuple_shapes()
self.assertEqual(out_shape.dimensions(), (3, 4))
def test_jit_device(self):
device = xb.devices()[-1]
x = api.jit(lambda x: x, device=device)(3.)
self.assertIsInstance(x, DeviceArray)
self.assertEqual(x.device_buffer.device(), device)
def test_jit_of_noncallable(self):
self.assertRaisesRegex(TypeError, "Expected a callable value.*",
lambda: api.jit(3))
def test_issue_1062(self):
# code from https://github.com/google/jax/issues/1062 @shoyer
# this tests, among other things, whether ShardedDeviceTuple constants work
device_count = xb.device_count()
@jit
def multi_step(state, count):
return lax.fori_loop(0, count, lambda i, s: s, state)
@jit
def multi_step_pmap(state, count=2):
@partial(api.pmap, axis_name='x')
def pmapped_multi_step(state):
return multi_step(state, count)
return pmapped_multi_step(state)
u = np.ones((device_count, 100))
u_final = multi_step_pmap(u) # doesn't crash
@unittest.skipIf(six.PY2, "Test requires Python 3")
def test_concurrent_device_get_and_put(self):
def f(x):
for _ in range(100):
y = jax.device_put(x)
x = jax.device_get(y)
return x
xs = [onp.random.randn(i) for i in range(10)]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(partial(f, x)) for x in xs]
ys = [f.result() for f in futures]
for x, y in zip(xs, ys):
self.assertAllClose(x, y, check_dtypes=True)
@unittest.skipIf(six.PY2, "Test requires Python 3")
def test_concurrent_jit(self):
@jit
def f(x):
return x + x - 3.
xs = [onp.random.randn(i) for i in range(10)]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(partial(f, x)) for x in xs]
ys = [f.result() for f in futures]
for x, y in zip(xs, ys):
self.assertAllClose(x * 2 - 3., y, check_dtypes=True)
def test_dtype_warning(self):
# cf. issue #1230
if FLAGS.jax_enable_x64:
return # test only applies when x64 is disabled
def check_warning(warn, nowarn):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
nowarn() # get rid of extra startup warning
prev_len = len(w)
nowarn()
assert len(w) == prev_len
warn()
assert len(w) > 0
msg = str(w[-1].message)
expected_prefix = "Explicitly requested dtype "
self.assertEqual(expected_prefix, msg[:len(expected_prefix)])
prev_len = len(w)
nowarn()
assert len(w) == prev_len
check_warning(lambda: np.array([1, 2, 3], dtype="float64"),
lambda: np.array([1, 2, 3], dtype="float32"),)
check_warning(lambda: np.ones(3, dtype=onp.float64),
lambda: np.ones(3))
check_warning(lambda: np.ones_like(3, dtype=onp.int64),
lambda: np.ones_like(3, dtype=onp.int32))
check_warning(lambda: np.zeros(3, dtype="int64"),
lambda: np.zeros(3, dtype="int32"))
check_warning(lambda: np.zeros_like(3, dtype="float64"),
lambda: np.zeros_like(3, dtype="float32"))
check_warning(lambda: np.full((2, 3), 1, dtype="int64"),
lambda: np.full((2, 3), 1))
check_warning(lambda: np.ones(3).astype("float64"),
lambda: np.ones(3).astype("float32"))
check_warning(lambda: np.eye(3, dtype=onp.float64),
lambda: np.eye(3))
check_warning(lambda: np.arange(3, dtype=onp.float64),
lambda: np.arange(3, dtype=onp.float32))
check_warning(lambda: np.linspace(0, 3, dtype=onp.float64),
lambda: np.linspace(0, 3, dtype=onp.float32))
check_warning(lambda: np.tri(2, dtype="float64"),
lambda: np.tri(2, dtype="float32"))
def test_custom_vjp_zeros(self):
@api.custom_transforms
def f(x, y):
return 2 * x, 3 * y
def f_vjp(x, y):
return (2 * x, 3 * y), lambda ts: (4 * ts[0], 5 * ts[1])
api.defvjp_all(f, f_vjp, )
api.grad(lambda x, y: f(x, y)[0])(1., 2.) # doesn't crash
def test_custom_transforms_vjp_nones(self):
# issue rasied by jsnoek@ and jumper@
@jax.custom_transforms
def solve(a, b):
return np.dot(np.linalg.inv(a), b)
# print(solve(a, b))
def solve_vjp(a, b):
x = solve(a, b)
def vjp(x_tangent):
dx = np.dot(solve(a, x_tangent), x.T)
out = (dx, b * 0.)
return out
return x, vjp
jax.defvjp_all(solve, solve_vjp)
gf = grad(lambda a,b: np.sum(solve(a, b)))
n = 3
a_in = np.linspace(0, 1, n)[:, None]
a = np.dot(a_in, a_in.T) + np.eye(n) * 0.1
real_x = onp.random.RandomState(0).randn(n)
b = np.dot(a + np.eye(a.shape[0]), real_x)
print(gf(a, b)) # doesn't crash
def test_vmap_in_axes_tree_prefix_error(self):
# https://github.com/google/jax/issues/795
self.assertRaisesRegex(
ValueError,
"axes specification must be a tree prefix of the corresponding "
r"value, got specification \(0, 0\) for value "
r"PyTreeDef\(tuple, \[\*\]\).",
lambda: api.vmap(lambda x: x, in_axes=(0, 0))(np.ones(3))
)
def test_vmap_unbatched_object_passthrough_issue_183(self):
# https://github.com/google/jax/issues/183
fun = lambda f, x: f(x)
vfun = api.vmap(fun, (None, 0))
ans = vfun(lambda x: x + 1, np.arange(3))
self.assertAllClose(ans, onp.arange(1, 4), check_dtypes=False)
def test_vmap_mismatched_axis_sizes_error_message_issue_705(self):
# https://github.com/google/jax/issues/705
def h(a, b):
return np.sum(a) + np.sum(b)
X = onp.random.randn(10, 4)
U = onp.random.randn(10, 2)
self.assertRaisesRegex(
ValueError,
"vmap got inconsistent sizes for array axes to be mapped:\n"
r"arg 0 has shape \(10, 4\) and axis 0 is to be mapped" "\n"
r"arg 1 has shape \(10, 2\) and axis 1 is to be mapped" "\n"
"so\n"
"arg 0 has an axis to be mapped of size 10\n"
"arg 1 has an axis to be mapped of size 2",
lambda: api.vmap(h, in_axes=(0, 1))(X, U))
self.assertRaisesRegex(
ValueError,
"vmap got inconsistent sizes for array axes to be mapped:\n"
r"arg 0 has shape \(10, 4\) and axis 0 is to be mapped" "\n"
r"arg 1 has shape \(10, 2\) and axis 1 is to be mapped" "\n"
r"arg 2 has shape \(10, 4\) and axis 0 is to be mapped" "\n"
"so\n"
"args 0, 2 have axes to be mapped of size 10\n"
"arg 1 has an axis to be mapped of size 2",
lambda: api.vmap(lambda x, y, z: None, in_axes=(0, 1, 0))(X, U, X))
self.assertRaisesRegex(
ValueError,
"vmap got inconsistent sizes for array axes to be mapped:\n"
"the tree of axis sizes is:\n"
r"\(10, \[2, 2\]\)",
lambda: api.vmap(h, in_axes=(0, 1))(X, [U, U]))
def test_vmap_structured_in_axes(self):
A, B, C, D = 2, 3, 4, 5
K = 6 # batch size
x = onp.ones((K, A, B)) # batch axis in different locations
y = onp.ones((B, K, C))
z = onp.ones((C, D, K))
def foo(tree_arg):
x, (y, z) = tree_arg
return np.dot(x, np.dot(y, z))
tree = (x, (y, z))
vfoo = api.vmap(foo, in_axes=((0, (1, 2)),))
self.assertEqual(vfoo(tree).shape, (6, 2, 5))
Point = collections.namedtuple("Point", ["x", "y"])
tree = (x, Point(y, z))
vfoo = api.vmap(foo, in_axes=((0, Point(1, 2)),))
self.assertEqual(vfoo(tree).shape, (6, 2, 5))
def foo(tree_arg):
x, dct = tree_arg
y, z = dct['a'], dct['b']
return np.dot(x, np.dot(y, z))
tree = (x, {'a':y, 'b':z})
vfoo = api.vmap(foo, in_axes=((0, {'a':1, 'b':2}),))
self.assertEqual(vfoo(tree).shape, (6, 2, 5))
tree = (x, collections.OrderedDict([('a', y), ('b', z)]))
vfoo = api.vmap(
foo, in_axes=((0, collections.OrderedDict([('a', 1), ('b', 2)])),))
self.assertEqual(vfoo(tree).shape, (6, 2, 5))
def test_jit_reference_dropping(self):
x = onp.ones(10)
f = (lambda x: lambda: x)(x) # reference to x in f's closure
g = jit(f)
x = weakref.ref(x) # no more strong ref to x in this scope
assert x() is not None # x is still around
f() # f runs
g() # g runs
g() # g runs a second time
del f # delete the raw callable
assert x() is not None # x is still around
g() # g still runs
del g # no more references to x
assert x() is None # x is gone
def test_jit_global_cache(self):
def f(x):
assert python_should_be_executing
return x
python_should_be_executing = True
api.jit(f)(2)
python_should_be_executing = False
api.jit(f)(3)
def test_pmap_global_cache(self):
def f(x):
assert python_should_be_executing
return x
x = onp.ones(1)
python_should_be_executing = True
api.pmap(f)(x)
python_should_be_executing = False
api.pmap(f)(x)
python_should_be_executing = True
api.pmap(f, 'i')(x)
python_should_be_executing = False
api.pmap(f, 'i')(x)
def test_repr(self):
rep = repr(np.ones(()) + 1.)
self.assertStartsWith(rep, 'DeviceArray')
def test_grad_without_enough_args_error_message(self):
# https://github.com/google/jax/issues/1696
def f(x, y): return x + y
df = api.grad(f, argnums=0)
self.assertRaisesRegex(
TypeError,
"differentiating with respect to argnums=0 requires at least 1 "
"positional arguments to be passed by the caller, but got only 0 "
"positional arguments.",
lambda: partial(df, x=0.)(y=1.))
def test_grad_of_jit_compilation_caching(self):
if not hasattr(self, "assertLogs"):
raise unittest.SkipTest("test requires assertLogs (python 3)")
lax.add(1, 2) # make sure some initial warnings are already printed
sin = api.jit(np.sin)
prev_level = logging.get_verbosity()
try:
logging.set_verbosity('DEBUG')
with self.assertLogs(level=logging.DEBUG) as l:
ans1 = api.grad(sin)(2.)
ans2 = api.grad(sin)(3.)
finally:
logging.set_verbosity(prev_level)
self.assertLen(l.output, 2)
self.assertAllClose(ans1, onp.cos(2.), check_dtypes=False)
self.assertAllClose(ans2, onp.cos(3.), check_dtypes=False)
def test_remat_basic(self):
@api.remat
def g(x):
return lax.sin(lax.sin(x)), 3.
def f(x):
x, _ = g(x)
return x
ans = f(2.)
expected = onp.sin(onp.sin(2.))
self.assertAllClose(ans, expected, check_dtypes=False)
ans, f_lin = api.linearize(f, 2.)
expected = onp.sin(onp.sin(2.))
self.assertAllClose(ans, expected, check_dtypes=False)
ans = f_lin(3.)
expected = onp.cos(onp.sin(2.)) * onp.cos(2.) * 3.
self.assertAllClose(ans, expected, check_dtypes=False)
sin_calls = []
cos_calls = []
sin_impl = lax.sin_p.impl
cos_impl = lax.cos_p.impl
try:
lax.sin_p.def_impl(lambda x: sin_calls.append(1) or sin_impl(x))
lax.cos_p.def_impl(lambda x: cos_calls.append(1) or cos_impl(x))
f_lin(3.)
finally:
lax.sin_p.def_impl(sin_impl)
lax.cos_p.def_impl(cos_impl)
self.assertEqual(len(sin_calls), 1)
self.assertEqual(len(cos_calls), 2)
def test_remat_freevars(self):
def f1(x):
y = 2 * np.sin(x)
z = np.cos(x) * np.sin(y)
return z
def f2(x):
y = 2 * np.sin(x)
z = api.remat(lambda x: np.cos(x) * np.sin(y))(x)
return z
ans, f_lin = api.linearize(f2, 2.)
expected, f_lin_expected = api.linearize(f1, 2.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = f_lin(3.)
expected = f_lin_expected(3.)
self.assertAllClose(ans, expected, check_dtypes=False)
def test_remat_grad_python_control_flow(self):
@partial(api.remat, concrete=True)
def g(x):
if x > 0:
return lax.sin(x), 3.
else:
return lax.cos(x), 4.
def f(x):
x, _ = g(x)
return x
ans = f(2.)
expected = onp.sin(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.grad(f)(2.)
expected = onp.cos(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
def test_remat_jit(self):
@api.remat
def g(x):
return lax.sin(lax.sin(x))
def f_(x):
return g(x)
f = api.jit(f_)
ans = f(2.)
expected = onp.sin(onp.sin(2.))
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.grad(f)(2.)
expected = onp.cos(onp.sin(2.)) * onp.cos(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.jit(api.grad(f_))(2.)
expected = onp.cos(onp.sin(2.)) * onp.cos(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
def test_remat_vmap(self):
@api.remat
def g(x):
return lax.sin(lax.sin(x))
x = onp.arange(3.)
ans = api.vmap(g)(x)
expected = onp.sin(onp.sin(x))
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.jacfwd(g)(x)
expected = onp.diag(onp.cos(onp.sin(x)) * onp.cos(x))
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.jacrev(g)(x)
expected = onp.diag(onp.cos(onp.sin(x)) * onp.cos(x))
self.assertAllClose(ans, expected, check_dtypes=False)
def test_remat_higher_order_autodiff(self):
def f(x):
return lax.cos(lax.sin(x))
g = api.remat(f)
ans = api.grad(api.grad(g))(3.)
expected = api.grad(api.grad(f))(3.)
self.assertAllClose(ans, expected, check_dtypes=False)
def test_remat_scan(self):
to_scan = lambda c, x: (np.sin(c), None)
def f_noremat(x):
y, _ = lax.scan(to_scan, x, onp.arange(3.))
return y
def f_yesremat(x):
y, _ = lax.scan(api.remat(to_scan), x, onp.arange(3.))
return y
ans = f_yesremat(4.)
expected = f_noremat(4.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.grad(f_yesremat)(4.)
expected = api.grad(f_noremat)(4.)
self.assertAllClose(ans, expected, check_dtypes=False)
jaxpr = api.make_jaxpr(api.linearize(f_yesremat, 4.)[1])(1.)
scan_eqn, = jaxpr.jaxpr.eqns
self.assertIn(' cos ', str(scan_eqn.params['jaxpr']))
jaxpr = api.make_jaxpr(api.vjp(f_yesremat, 4.)[1])(1.)
scan_eqn, = jaxpr.jaxpr.eqns
self.assertIn(' cos ', str(scan_eqn.params['jaxpr']))
def test_remat_no_redundant_flops(self):
# see https://github.com/google/jax/pull/1749#issuecomment-558267584
@api.jit
def g(x):
return f(2., x)
@api.remat
def f(x, y):
return np.sin(x) * y
# We swap out sin_p's impl rule to count how many times it's invoked
called = []
sin_impl = lax.sin_p.impl
try:
lax.sin_p.def_impl(lambda x: called.append(1) or sin_impl(x))
api.grad(g)(3.)
finally:
lax.sin_p.def_impl(sin_impl)
num_calls = len(called)
self.assertEqual(num_calls, 1)
def test_remat_binomial_checkpointing(self):
def binom_checkpoint(funs):
if len(funs) == 1:
return funs[0]
else:
f1 = binom_checkpoint(funs[:len(funs)//2])
f2 = binom_checkpoint(funs[len(funs)//2:])
return api.remat(lambda x: f1(f2(x)))
f1 = binom_checkpoint([np.sin, np.sin, np.sin, np.sin])
f2 = lambda x: np.sin(np.sin(np.sin(np.sin(x))))
x = 4.
self.assertAllClose(f1(x), f2(x), check_dtypes=False)
self.assertAllClose(api.grad(f1)(x), api.grad(f2)(x), check_dtypes=False)
def test_trivial_computations(self):
x = np.array([1, 2, 3])
y = api.jit(lambda x: x)(x)
self.assertIs(x, y)
z1, z2 = api.jit(lambda x: (x, x))(x)
self.assertIs(z1, z2)
x1, x2 = np.array([1, 2]), np.array([2, 3])
z1, z2, z3 = api.jit(lambda x, y: (y, 1, x))(x1, x2)
self.assertIs(z1, x2)
self.assertIs(z3, x1)
self.assertEqual(z2, 1)
class JaxprTest(jtu.JaxTestCase):
def test_scalar_literals(self):
jaxpr = api.make_jaxpr(lambda x: x + 2)(42)
self.assertLen(jaxpr.jaxpr.constvars, 0)
def test_const(self):
def fun(x):
return (x, 1., np.zeros(1))
jaxpr = api.make_jaxpr(fun)(0.)
self.assertMultiLineStrippedEqual(str(jaxpr), """
{ lambda b ; ; a.
let
in [a, 1.0, b] }
""")
def test_cond(self):
def f(x):
return lax.cond(x >= 0.,
x + 1.,
lambda xt: xt + x,
x + 2.,
lambda xf: xf - x)
jaxpr = api.make_jaxpr(f)(3.)
self.assertMultiLineStrippedEqual(str(jaxpr), """
{ lambda ; ; a.
let b = ge a 0.0
c = add a 1.0
d = add a 2.0
e = cond[ false_jaxpr={ lambda ; ; b a.
let c = sub a b
in [c] }
false_nconsts=1
true_jaxpr={ lambda ; ; b a.
let c = add a b
in [c] }
true_nconsts=1 ] b a c a d
in [e] }
""")
if __name__ == '__main__':
absltest.main()
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