# Copyright 2019 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. """Tests for nn module.""" import collections from functools import partial import itertools from absl.testing import absltest from absl.testing import parameterized import scipy.stats from jax import core from jax import test_util as jtu from jax.test_util import check_grads from jax import nn from jax import random import jax import jax.numpy as jnp from jax.config import config config.parse_flags_with_absl() class NNFunctionsTest(jtu.JaxTestCase): def setUp(self): super().setUp() config.update("jax_numpy_rank_promotion", "raise") def tearDown(self): super().tearDown() config.update("jax_numpy_rank_promotion", "allow") @jtu.skip_on_flag("jax_skip_slow_tests", True) def testSoftplusGrad(self): check_grads(nn.softplus, (1e-8,), order=4, rtol=1e-2 if jtu.device_under_test() == "tpu" else None) def testSoftplusGradZero(self): check_grads(nn.softplus, (0.,), order=1, rtol=1e-2 if jtu.device_under_test() == "tpu" else None) def testSoftplusGradInf(self): self.assertAllClose( 1., jax.grad(nn.softplus)(float('inf'))) def testSoftplusGradNegInf(self): check_grads(nn.softplus, (-float('inf'),), order=1, rtol=1e-2 if jtu.device_under_test() == "tpu" else None) def testSoftplusGradNan(self): check_grads(nn.softplus, (float('nan'),), order=1, rtol=1e-2 if jtu.device_under_test() == "tpu" else None) @parameterized.parameters([int, float] + jtu.dtypes.floating + jtu.dtypes.integer) def testSoftplusZero(self, dtype): self.assertEqual(jnp.log(dtype(2)), nn.softplus(dtype(0))) def testReluGrad(self): rtol = 1e-2 if jtu.device_under_test() == "tpu" else None check_grads(nn.relu, (1.,), order=3, rtol=rtol) check_grads(nn.relu, (-1.,), order=3, rtol=rtol) jaxpr = jax.make_jaxpr(jax.grad(nn.relu))(0.) self.assertGreaterEqual(len(jaxpr.jaxpr.eqns), 2) def testSoftplusValue(self): val = nn.softplus(89.) self.assertAllClose(val, 89., check_dtypes=False) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testEluGrad(self): check_grads(nn.elu, (1e4,), order=4, eps=1.) def testEluValue(self): val = nn.elu(1e4) self.assertAllClose(val, 1e4, check_dtypes=False) def testGluValue(self): val = nn.glu(jnp.array([1.0, 0.0])) self.assertAllClose(val, jnp.array([0.5])) @parameterized.parameters(False, True) def testGelu(self, approximate): def gelu_reference(x): return x * scipy.stats.norm.cdf(x) rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((4, 5, 6), jnp.float32)] self._CheckAgainstNumpy( gelu_reference, partial(nn.gelu, approximate=approximate), args_maker, check_dtypes=False, tol=1e-3 if approximate else None) @parameterized.parameters(*itertools.product( (jnp.float32, jnp.bfloat16, jnp.float16), (partial(nn.gelu, approximate=False), partial(nn.gelu, approximate=True), nn.relu, nn.softplus, nn.sigmoid))) def testDtypeMatchesInput(self, dtype, fn): if dtype is jnp.float16 and jtu.device_under_test() == "tpu": self.skipTest("float16 not supported on TPU") x = jnp.zeros((), dtype=dtype) out = fn(x) self.assertEqual(out.dtype, dtype) def testEluMemory(self): # see https://github.com/google/jax/pull/1640 with jax.enable_checks(False): # With checks we materialize the array jax.make_jaxpr(lambda: nn.elu(jnp.ones((10 ** 12,)))) # don't oom def testHardTanhMemory(self): # see https://github.com/google/jax/pull/1640 with jax.enable_checks(False): # With checks we materialize the array jax.make_jaxpr(lambda: nn.hard_tanh(jnp.ones((10 ** 12,)))) # don't oom def testOneHot(self): actual = nn.one_hot(jnp.array([0, 1, 2]), 3) expected = jnp.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) self.assertAllClose(actual, expected) actual = nn.one_hot(jnp.array([1, 2, 0]), 3) expected = jnp.array([[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]) self.assertAllClose(actual, expected) def testOneHotOutOfBound(self): actual = nn.one_hot(jnp.array([-1, 3]), 3) expected = jnp.array([[0., 0., 0.], [0., 0., 0.]]) self.assertAllClose(actual, expected) def testOneHotNonArrayInput(self): actual = nn.one_hot([0, 1, 2], 3) expected = jnp.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) self.assertAllClose(actual, expected) def testOneHotCustomDtype(self): actual = nn.one_hot(jnp.array([0, 1, 2]), 3, dtype=jnp.bool_) expected = jnp.array([[True, False, False], [False, True, False], [False, False, True]]) self.assertAllClose(actual, expected) def testOneHotConcretizationError(self): # https://github.com/google/jax/issues/3654 msg = r"in jax.nn.one_hot argument `num_classes`" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): jax.jit(nn.one_hot)(3, 5) def testOneHotAxis(self): expected = jnp.array([[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]).T actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=0) self.assertAllClose(actual, expected) actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=-2) self.assertAllClose(actual, expected) InitializerRecord = collections.namedtuple( "InitializerRecord", ["name", "initializer", "shapes"]) ALL_SHAPES = [(2,), (2, 2), (2, 3), (3, 2), (2, 3, 4), (4, 3, 2), (2, 3, 4, 5)] def initializer_record(name, initializer, min_dims=2, max_dims=4): shapes = [shape for shape in ALL_SHAPES if min_dims <= len(shape) <= max_dims] return InitializerRecord(name, initializer, shapes) INITIALIZER_RECS = [ initializer_record("uniform", nn.initializers.uniform, 1), initializer_record("normal", nn.initializers.normal, 1), initializer_record("he_normal", nn.initializers.he_normal), initializer_record("he_uniform", nn.initializers.he_uniform), initializer_record("glorot_normal", nn.initializers.glorot_normal), initializer_record("glorot_uniform", nn.initializers.glorot_uniform), initializer_record("lecun_normal", nn.initializers.lecun_normal), initializer_record("lecun_uniform", nn.initializers.lecun_uniform), initializer_record("orthogonal", nn.initializers.orthogonal, 2, 2), initializer_record("delta_orthogonal", nn.initializers.delta_orthogonal, 4, 4) ] class NNInitializersTest(jtu.JaxTestCase): def setUp(self): super().setUp() config.update("jax_numpy_rank_promotion", "raise") def tearDown(self): super().tearDown() config.update("jax_numpy_rank_promotion", "allow") @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_{}_{}".format( rec.name, jtu.format_shape_dtype_string(shape, dtype)), "initializer": rec.initializer(), "shape": shape, "dtype": dtype} for rec in INITIALIZER_RECS for shape in rec.shapes for dtype in jtu.dtypes.floating)) def testInitializer(self, initializer, shape, dtype): rng = random.PRNGKey(0) val = initializer(rng, shape, dtype) self.assertEqual(shape, jnp.shape(val)) self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val)) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_{}_{}".format( rec.name, jtu.format_shape_dtype_string(shape, dtype)), "initializer_provider": rec.initializer, "shape": shape, "dtype": dtype} for rec in INITIALIZER_RECS for shape in rec.shapes for dtype in jtu.dtypes.floating)) def testInitializerProvider(self, initializer_provider, shape, dtype): rng = random.PRNGKey(0) initializer = initializer_provider(dtype=dtype) val = initializer(rng, shape) self.assertEqual(shape, jnp.shape(val)) self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val)) if __name__ == "__main__": absltest.main(testLoader=jtu.JaxTestLoader())