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0ff234049b
These docstrings do not make the tests any more clear and typically just duplicate the test module name. PiperOrigin-RevId: 737611977
296 lines
8.6 KiB
Python
296 lines
8.6 KiB
Python
# Copyright 2019 The JAX Authors.
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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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from absl.testing import absltest
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import jax
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import numpy as np
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from unittest import SkipTest
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from jax._src import api
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from jax._src import test_util as jtu
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from jax import numpy as jnp
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from jax.experimental import pjit
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from jax.experimental.shard_map import shard_map
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from jax.sharding import PartitionSpec as P
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jax.config.parse_flags_with_absl()
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@jtu.with_config(jax_debug_nans=True)
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class DebugNaNsTest(jtu.JaxTestCase):
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def testSinc(self):
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# Regression test for #6936
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self.assertEqual(jnp.sinc(0.0), 1.0)
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def testSingleResultPrimitiveNoNaN(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans = jnp.tanh(A)
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ans.block_until_ready()
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def testMultipleResultPrimitiveNoNaN(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans, _ = jnp.linalg.eigh(A)
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ans.block_until_ready()
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def testJitComputationNoNaN(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans = jax.jit(jnp.tanh)(A)
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ans.block_until_ready()
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def testJitComputationNaN(self):
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A = jnp.array(0.)
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with self.assertRaises(FloatingPointError):
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ans = jax.jit(lambda x: 0. / x)(A)
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ans.block_until_ready()
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@jax.debug_nans(False)
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def testJitComputationNaNContextManager(self):
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A = jnp.array(0.)
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f = jax.jit(lambda x: 0. / x)
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ans = f(A)
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ans = f(A)
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with self.assertRaises(FloatingPointError):
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with jax.debug_nans(True):
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ans = f(A)
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ans.block_until_ready()
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def testSingleResultPrimitiveNaN(self):
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A = jnp.array(0.)
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with self.assertRaises(FloatingPointError):
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ans = 0. / A
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ans.block_until_ready()
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@jtu.sample_product(jit=jtu.JIT_IMPLEMENTATION)
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def testCallDeoptimized(self, jit):
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@jit
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def f(x):
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return jax.lax.cond(
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x == 1, lambda _: np.nan, lambda _: 2., operand=None)
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# This makes sure, when using the C++ jit, that the Python code has been
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# run to compile, and the next call won't go through `cache_miss`.
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f(2)
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# 'cond' not 'xla_call'
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msg = r"invalid value \(nan\) encountered in .*cond.*"
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with self.assertRaisesRegex(FloatingPointError, msg):
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f(1)
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def testShardMap(self):
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mesh = jax.make_mesh((1,), ('x',))
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f = shard_map(lambda x: 0. / x, mesh=mesh, in_specs=(P('x')), out_specs=P('x'))
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# For the Cpp pmap, the first execution always goes through Python.
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f(jnp.array([1.]))
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with self.assertRaisesRegex(
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FloatingPointError,
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r"Invalid value \(nan\) encountered in sharded computation"):
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ans = f(jnp.array([0.]))
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ans.block_until_ready()
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if jax.device_count() >= 2:
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with self.assertRaisesRegex(
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FloatingPointError,
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r"Invalid value \(nan\) encountered in sharded computation"):
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ans = f(jnp.array([1., 0.]))
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ans.block_until_ready()
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def testPmap(self):
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pmap_funcs = [api._cpp_pmap]
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for pmap in pmap_funcs:
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f = pmap(lambda x: 0. / x)
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# For the Cpp pmap, the first execution always goes through Python.
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f(jnp.array([1.]))
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with self.assertRaisesRegex(
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FloatingPointError,
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r"invalid value \(nan\) encountered in div"):
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ans = f(jnp.array([0.]))
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ans.block_until_ready()
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if jax.device_count() >= 2:
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with self.assertRaisesRegex(
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FloatingPointError,
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r"Invalid value \(nan\) encountered in parallel computation"):
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ans = f(jnp.array([1., 0.]))
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ans.block_until_ready()
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def testGradPmap(self):
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@jax.jit
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def f(x):
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y = x**2
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return jnp.log(y)
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_, f_vjp = jax.vjp(jax.pmap(f), jnp.zeros([1]))
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with self.assertRaisesRegex(
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FloatingPointError,
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r"invalid value \(nan\) encountered in mul\nWhen differentiating"):
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ans, = f_vjp(jnp.ones([1]))
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ans.block_until_ready()
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def testGradShardMap(self):
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@jax.jit
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def f(x):
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y = x**2
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return jnp.log(y)
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mesh = jax.make_mesh((1,), ('x',))
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shmap_f = shard_map(f, mesh=mesh, in_specs=(P('x')), out_specs=P('x'))
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_, f_vjp = jax.vjp(shmap_f, jnp.zeros([1]))
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with self.assertRaisesRegex(
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FloatingPointError, r"Invalid value \(nan\) encountered"):
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ans, = f_vjp(jnp.ones([1]))
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ans.block_until_ready()
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def testPmapNoNaN(self):
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ans = jax.pmap(lambda x: 0. / x)(jnp.array([1.]))
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ans.block_until_ready()
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@jtu.ignore_warning(message=".*is an experimental.*")
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def testPjit(self):
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if jax.device_count() < 2:
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raise SkipTest("test requires >=2 devices")
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p = jax.sharding.PartitionSpec('x')
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f = pjit.pjit(lambda x: 0. / x, in_shardings=p, out_shardings=p)
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with jax.sharding.Mesh(np.array(jax.local_devices()[:2]), ('x',)):
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with self.assertRaises(FloatingPointError):
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ans = f(jnp.array([0., 1.]))
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ans.block_until_ready()
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def testDebugNansJitWithDonation(self):
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# https://github.com/jax-ml/jax/issues/12514
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a = jnp.array(0.)
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with self.assertRaises(FloatingPointError):
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ans = jax.jit(lambda x: 0. / x, donate_argnums=(0,))(a)
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ans.block_until_ready()
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def testDebugNansPmapWithDonation(self):
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a = jnp.zeros((1,))
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with self.assertRaises(FloatingPointError):
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ans = jax.pmap(lambda x: 0. / x, donate_argnums=(0,))(a)
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ans.block_until_ready()
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@jtu.ignore_warning(message=".*is an experimental.*")
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def testDebugNansPjitWithDonation(self):
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if jax.device_count() < 2:
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raise SkipTest("test requires >=2 devices")
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p = jax.sharding.PartitionSpec('x')
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f = pjit.pjit(lambda x: 0. / x,
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in_shardings=p,
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out_shardings=p,
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donate_argnums=(0,))
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with jax.sharding.Mesh(np.array(jax.local_devices()[:2]), ('x',)):
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with self.assertRaises(FloatingPointError):
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ans = f(jnp.array([0., 1.]))
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ans.block_until_ready()
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def testDebugNansZeroDiv(self):
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inp = jnp.zeros(())
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def f(x, y):
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return x / y
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with self.assertRaisesRegex(
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FloatingPointError,
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r"invalid value \(nan\) encountered in div"):
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f(inp, inp)
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with self.assertRaisesRegex(
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FloatingPointError,
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r"invalid value \(nan\) encountered in div"):
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jax.jit(f)(inp, inp)
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def testDebugNansInput(self):
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@jax.jit
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def f(x):
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return x * 3.
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with self.assertRaisesRegex(FloatingPointError, "the de-optimized function did not .*input"):
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f(np.nan)
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@jtu.with_config(jax_debug_infs=True)
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class DebugInfsTest(jtu.JaxTestCase):
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def testSingleResultPrimitiveNoInf(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans = jnp.tanh(A)
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ans.block_until_ready()
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def testMultipleResultPrimitiveNoInf(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans, _ = jnp.linalg.eigh(A)
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ans.block_until_ready()
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def testJitComputationNoInf(self):
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A = jnp.array([[1., 2.], [2., 3.]])
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ans = jax.jit(jnp.tanh)(A)
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ans.block_until_ready()
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def testSingleResultPrimitiveInf(self):
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A = jnp.array(0.)
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with self.assertRaises(FloatingPointError):
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ans = 1. / A
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ans.block_until_ready()
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@jtu.sample_product(jit=jtu.JIT_IMPLEMENTATION)
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def testCallDeoptimized(self, jit):
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@jit
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def f(x):
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return jax.lax.cond(
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x == 1, lambda _: np.inf, lambda _: 2., operand=None)
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# This makes sure, when using the C++ jit, that the Python code has been
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# run to compile, and the next call won't go through `cache_miss`.
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f(2)
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# 'cond' not 'xla_call'
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msg = r"invalid value \(inf\) encountered in .*cond.*"
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with self.assertRaisesRegex(FloatingPointError, msg):
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f(1)
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def testDebugNansDoesntCorruptCaches(self):
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# https://github.com/jax-ml/jax/issues/6614
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@jax.jit
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def f(x):
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return jnp.divide(x, x)
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for _ in range(2):
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try:
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with jax.debug_nans(True):
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jax.grad(f)(0.)
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except FloatingPointError:
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pass
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def testDebugNansDoesntReturnDeoptimizedResult(self):
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@jax.jit
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def f(x):
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y = x + 2 # avoid trivial dispatch path by adding some eqn
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return jnp.nan, y
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with self.assertRaisesRegex(FloatingPointError, "the de-optimized function did not .*literal"):
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with jax.debug_nans(True):
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f(3)
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if __name__ == '__main__':
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absltest.main(testLoader=jtu.JaxTestLoader())
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