def testVarianceScaling(self, map_in, map_out, fan, distr):
shape = (80, 50, 7)
fan_in, fan_out = jax._src.nn.initializers._compute_fans(
NamedShape(*shape), 0, 1)
key = jax.random.PRNGKey(0)
base_scaling = partial(jax.nn.initializers.variance_scaling, 100, fan,
distr)
ref_sampler = lambda: base_scaling(in_axis=0, out_axis=1)(key, shape)
if map_in and map_out:
out_axes = ['i', 'o', ...]
named_shape = NamedShape(shape[2], i=shape[0], o=shape[1])
xmap_sampler = lambda: base_scaling(in_axis='i', out_axis='o')(
key, named_shape)
elif map_in:
out_axes = ['i', ...]
named_shape = NamedShape(shape[1], shape[2], i=shape[0])
xmap_sampler = lambda: base_scaling(in_axis='i', out_axis=0)(
key, named_shape)
elif map_out:
out_axes = [None, 'o', ...]
named_shape = NamedShape(shape[0], shape[2], o=shape[1])
xmap_sampler = lambda: base_scaling(in_axis=0, out_axis='o')(
key, named_shape)
mapped_sampler = xmap(xmap_sampler,
in_axes=(),
out_axes=out_axes,
axis_sizes={
'i': shape[0],
'o': shape[1]
})
self.assertAllClose(jnp.var(mapped_sampler()),
jnp.var(ref_sampler()),
atol=1e-4,
rtol=2e-2)
def testSamplerSharding(self, distr_sample):
def sample(shape, map_size):
return xmap(lambda: distr_sample(jax.random.PRNGKey(0), shape=shape),
in_axes=(), out_axes=[None, 'i', ...], axis_sizes={'i': map_size})()
replicated = sample((3,), 4)
self.assertTrue((replicated[:,[0]] == replicated).all())
sharded = sample(NamedShape(3, i=4), 4)
self.assertFalse((sharded[:,[0]] == sharded[:,1:]).all(1).any())
error = "The shape of axis i was specified as 4, but it really is 5"
with self.assertRaisesRegex(ValueError, error):
sample(NamedShape(3, i=4), 5)
def sample(axis_resources):
return xmap(lambda: distr_sample(jax.random.PRNGKey(0),
shape=NamedShape(3, i=4, j=6)),
in_axes=(),
out_axes=['i', 'j', ...],
axis_sizes={
'i': 4,
'j': 6
},
axis_resources=axis_resources)()
