mirror of
https://github.com/ROCm/jax.git
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4236eb2b59
This change, when enabled, stages out all primitive calls in the dynamic scope of a jitted, pmapped, or control flow function, rather than only staging out based on data dependence. One improvement is that jitted functions can consume less memory, by avoiding instantiating large constants at trace time, and cause less memory fragmentation as well. It also simplifies several internals. See https://github.com/google/jax/pull/3370 fo more information.
229 lines
7.9 KiB
Python
229 lines
7.9 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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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 numpy as np
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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([
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int, jnp.int32, float, jnp.float64, jnp.float32, jnp.float64,])
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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(*itertools.product(
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(jnp.float32, jnp.bfloat16, jnp.float16),
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(nn.gelu, nn.relu, nn.softplus, nn.sigmoid)))
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def testDtypeMatchesInput(self, dtype, fn):
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if dtype is jnp.float16 and jtu.device_under_test() == "tpu":
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self.skipTest("float16 not supported on TPU")
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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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@jtu.skip_on_devices("gpu", "tpu")
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def testEluMemory(self):
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# see https://github.com/google/jax/pull/1640
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with core.skipping_checks(): # With checks we materialize the array
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jax.make_jaxpr(nn.elu)(jnp.ones((10 ** 12,))) # don't oom
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@jtu.skip_on_devices("gpu", "tpu")
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def testHardTanhMemory(self):
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# see https://github.com/google/jax/pull/1640
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with core.skipping_checks(): # With checks we materialize the array
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jax.make_jaxpr(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"Abstract tracer.*\(in jax.nn.one_hot argument `num_classes`\).*"
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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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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 [np.float32, np.float64]))
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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 [np.float32, np.float64]))
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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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