Exemplo n.º 1
0
def debug_vmap_bfgs():
    import jax.numpy as jnp
    from jax import jit, config
    from jax.scipy.optimize import minimize
    import os
    config.enable_omnistaging()
    ncpu=2
    os.environ['XLA_FLAGS'] = f"--xla_force_host_platform_device_count={ncpu}"

    def cost_fn(x):
        return -jnp.sum(x**2)

    x = random.uniform(random.PRNGKey(0), (3,), minval=-1, maxval=1)
    result = jit(lambda x: minimize(cost_fn, x, method='BFGS'))(x)
    print(result)
Exemplo n.º 2
0
                                        jax.tree_util.tree_leaves(new_dist)):
            self.assertEqual(type(old_param), type(new_param))
            if isinstance(old_param, np.ndarray):
                self.assertTupleEqual(old_param.shape, new_param.shape)

    @test_all_distributions
    @hp.given(hps.data())
    @tfp_hps.tfp_hp_settings(default_max_examples=DEFAULT_MAX_EXAMPLES)
    def testInputOutputOfJittedFunction(self, dist_name, data):

        if (dist_name in PYTREE_BLOCKLIST) != FLAGS.blocklists_only:
            self.skipTest('Distribution currently broken.')

        @jax.jit
        def dist_and_sample(dist):
            return dist, dist.sample(seed=test_util.test_seed())

        dist = data.draw(
            dhps.distributions(enable_vars=False,
                               dist_name=dist_name,
                               validate_args=False,
                               eligibility_filter=lambda dname: dname not in
                               PYTREE_BLOCKLIST))
        dist_and_sample(dist)


if __name__ == '__main__':
    config.enable_omnistaging()
    os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=8'
    tf.test.main()
Exemplo n.º 3
0
# flax

import flax
from flax.core import freeze, unfreeze
from flax import linen as nn

# constants

LARGE_NEG_VALUE = -1e10

# config

from jax.config import config

config.enable_omnistaging()  # Linen requires enabling omnistaging

# helpers


def cross_entropy(logits, targets, axis=-1):
    logprobs = nn.log_softmax(logits, axis=axis)
    nll = np.take_along_axis(logprobs,
                             np.expand_dims(targets, axis=axis),
                             axis=axis)
    ce = -np.mean(nll)
    return ce


def fixed_pos_embedding(seq, dim):
    inv_freq = 1.0 / (10000**(np.arange(0, dim, 2) / dim))
Exemplo n.º 4
0
from jax.config import config as jax_config

from jax import random
import jax.numpy as np
from jax import ops

from jax.api import grad

from jax import test_util as jtu
from jax import jit, vmap

from jax_md import smap, partition, space, energy, quantity
from jax_md.util import *

jax_config.parse_flags_with_absl()
jax_config.enable_omnistaging()
FLAGS = jax_config.FLAGS

PARTICLE_COUNT = 1000
STOCHASTIC_SAMPLES = 10
SPATIAL_DIMENSION = [2, 3]

if FLAGS.jax_enable_x64:
    POSITION_DTYPE = [f32, f64]
else:
    POSITION_DTYPE = [f32]


class CellListTest(jtu.JaxTestCase):
    @parameterized.named_parameters(
        jtu.cases_from_list({
Exemplo n.º 5
0
 def setUp(self):
   super().setUp()
   config.enable_omnistaging()