mirror of
https://github.com/ROCm/jax.git
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250 lines
8.5 KiB
Python
250 lines
8.5 KiB
Python
# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for nn module."""
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import collections
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from functools import partial
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import itertools
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from absl.testing import absltest
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from absl.testing import parameterized
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import scipy.stats
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from jax import core
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from jax import test_util as jtu
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from jax.test_util import check_grads
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from jax import nn
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from jax import random
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import jax
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import jax.numpy as jnp
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from jax.config import config
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config.parse_flags_with_absl()
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class NNFunctionsTest(jtu.JaxTestCase):
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def setUp(self):
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super().setUp()
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config.update("jax_numpy_rank_promotion", "raise")
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def tearDown(self):
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super().tearDown()
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config.update("jax_numpy_rank_promotion", "allow")
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@jtu.skip_on_flag("jax_skip_slow_tests", True)
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def testSoftplusGrad(self):
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check_grads(nn.softplus, (1e-8,), order=4,
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rtol=1e-2 if jtu.device_under_test() == "tpu" else None)
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def testSoftplusGradZero(self):
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check_grads(nn.softplus, (0.,), order=1,
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rtol=1e-2 if jtu.device_under_test() == "tpu" else None)
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def testSoftplusGradInf(self):
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self.assertAllClose(
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1., jax.grad(nn.softplus)(float('inf')))
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def testSoftplusGradNegInf(self):
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check_grads(nn.softplus, (-float('inf'),), order=1,
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rtol=1e-2 if jtu.device_under_test() == "tpu" else None)
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def testSoftplusGradNan(self):
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check_grads(nn.softplus, (float('nan'),), order=1,
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rtol=1e-2 if jtu.device_under_test() == "tpu" else None)
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@parameterized.parameters([int, float] + jtu.dtypes.floating + jtu.dtypes.integer)
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def testSoftplusZero(self, dtype):
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self.assertEqual(jnp.log(dtype(2)), nn.softplus(dtype(0)))
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def testReluGrad(self):
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rtol = 1e-2 if jtu.device_under_test() == "tpu" else None
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check_grads(nn.relu, (1.,), order=3, rtol=rtol)
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check_grads(nn.relu, (-1.,), order=3, rtol=rtol)
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jaxpr = jax.make_jaxpr(jax.grad(nn.relu))(0.)
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self.assertGreaterEqual(len(jaxpr.jaxpr.eqns), 2)
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def testSoftplusValue(self):
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val = nn.softplus(89.)
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self.assertAllClose(val, 89., check_dtypes=False)
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@jtu.skip_on_flag("jax_skip_slow_tests", True)
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def testEluGrad(self):
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check_grads(nn.elu, (1e4,), order=4, eps=1.)
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def testEluValue(self):
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val = nn.elu(1e4)
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self.assertAllClose(val, 1e4, check_dtypes=False)
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def testGluValue(self):
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val = nn.glu(jnp.array([1.0, 0.0]))
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self.assertAllClose(val, jnp.array([0.5]))
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@parameterized.parameters(False, True)
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def testGelu(self, approximate):
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def gelu_reference(x):
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return x * scipy.stats.norm.cdf(x)
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng((4, 5, 6), jnp.float32)]
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self._CheckAgainstNumpy(
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gelu_reference, partial(nn.gelu, approximate=approximate), args_maker,
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check_dtypes=False, tol=1e-3 if approximate else None)
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@parameterized.parameters(*itertools.product(
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(jnp.float32, jnp.bfloat16, jnp.float16),
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(partial(nn.gelu, approximate=False),
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partial(nn.gelu, approximate=True),
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nn.relu, nn.softplus, nn.sigmoid)))
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def testDtypeMatchesInput(self, dtype, fn):
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x = jnp.zeros((), dtype=dtype)
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out = fn(x)
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self.assertEqual(out.dtype, dtype)
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def testEluMemory(self):
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# see https://github.com/google/jax/pull/1640
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with jax.enable_checks(False): # With checks we materialize the array
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jax.make_jaxpr(lambda: nn.elu(jnp.ones((10 ** 12,)))) # don't oom
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def testHardTanhMemory(self):
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# see https://github.com/google/jax/pull/1640
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with jax.enable_checks(False): # With checks we materialize the array
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jax.make_jaxpr(lambda: nn.hard_tanh(jnp.ones((10 ** 12,)))) # don't oom
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def testOneHot(self):
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actual = nn.one_hot(jnp.array([0, 1, 2]), 3)
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expected = jnp.array([[1., 0., 0.],
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[0., 1., 0.],
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[0., 0., 1.]])
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self.assertAllClose(actual, expected)
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actual = nn.one_hot(jnp.array([1, 2, 0]), 3)
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expected = jnp.array([[0., 1., 0.],
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[0., 0., 1.],
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[1., 0., 0.]])
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self.assertAllClose(actual, expected)
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def testOneHotOutOfBound(self):
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actual = nn.one_hot(jnp.array([-1, 3]), 3)
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expected = jnp.array([[0., 0., 0.],
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[0., 0., 0.]])
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self.assertAllClose(actual, expected)
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def testOneHotNonArrayInput(self):
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actual = nn.one_hot([0, 1, 2], 3)
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expected = jnp.array([[1., 0., 0.],
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[0., 1., 0.],
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[0., 0., 1.]])
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self.assertAllClose(actual, expected)
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def testOneHotCustomDtype(self):
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actual = nn.one_hot(jnp.array([0, 1, 2]), 3, dtype=jnp.bool_)
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expected = jnp.array([[True, False, False],
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[False, True, False],
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[False, False, True]])
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self.assertAllClose(actual, expected)
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def testOneHotConcretizationError(self):
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# https://github.com/google/jax/issues/3654
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msg = r"in jax.nn.one_hot argument `num_classes`"
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with self.assertRaisesRegex(core.ConcretizationTypeError, msg):
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jax.jit(nn.one_hot)(3, 5)
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def testOneHotAxis(self):
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expected = jnp.array([[0., 1., 0.],
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[0., 0., 1.],
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[1., 0., 0.]]).T
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actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=0)
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self.assertAllClose(actual, expected)
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actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=-2)
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self.assertAllClose(actual, expected)
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def testTanhExists(self):
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nn.tanh # doesn't crash
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InitializerRecord = collections.namedtuple(
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"InitializerRecord",
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["name", "initializer", "shapes"])
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ALL_SHAPES = [(2,), (2, 2), (2, 3), (3, 2), (2, 3, 4), (4, 3, 2), (2, 3, 4, 5)]
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def initializer_record(name, initializer, min_dims=2, max_dims=4):
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shapes = [shape for shape in ALL_SHAPES
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if min_dims <= len(shape) <= max_dims]
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return InitializerRecord(name, initializer, shapes)
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INITIALIZER_RECS = [
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initializer_record("uniform", nn.initializers.uniform, 1),
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initializer_record("normal", nn.initializers.normal, 1),
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initializer_record("he_normal", nn.initializers.he_normal),
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initializer_record("he_uniform", nn.initializers.he_uniform),
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initializer_record("glorot_normal", nn.initializers.glorot_normal),
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initializer_record("glorot_uniform", nn.initializers.glorot_uniform),
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initializer_record("lecun_normal", nn.initializers.lecun_normal),
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initializer_record("lecun_uniform", nn.initializers.lecun_uniform),
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initializer_record("orthogonal", nn.initializers.orthogonal, 2, 2),
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initializer_record("delta_orthogonal", nn.initializers.delta_orthogonal, 4, 4)
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]
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class NNInitializersTest(jtu.JaxTestCase):
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def setUp(self):
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super().setUp()
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config.update("jax_numpy_rank_promotion", "raise")
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def tearDown(self):
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super().tearDown()
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config.update("jax_numpy_rank_promotion", "allow")
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name":
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"_{}_{}".format(
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rec.name,
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jtu.format_shape_dtype_string(shape, dtype)),
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"initializer": rec.initializer(),
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"shape": shape, "dtype": dtype}
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for rec in INITIALIZER_RECS
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for shape in rec.shapes
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for dtype in jtu.dtypes.floating))
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def testInitializer(self, initializer, shape, dtype):
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rng = random.PRNGKey(0)
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val = initializer(rng, shape, dtype)
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self.assertEqual(shape, jnp.shape(val))
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self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val))
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name":
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"_{}_{}".format(
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rec.name,
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jtu.format_shape_dtype_string(shape, dtype)),
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"initializer_provider": rec.initializer,
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"shape": shape, "dtype": dtype}
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for rec in INITIALIZER_RECS
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for shape in rec.shapes
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for dtype in jtu.dtypes.floating))
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def testInitializerProvider(self, initializer_provider, shape, dtype):
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rng = random.PRNGKey(0)
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initializer = initializer_provider(dtype=dtype)
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val = initializer(rng, shape)
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self.assertEqual(shape, jnp.shape(val))
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self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val))
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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