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import jax
import jax.numpy as jnp
import ninjax as nj
import numpy as np
from embodied.jax import utils
f32 = jnp.float32
i32 = jnp.int32
class Layer(nj.Module):
units: int = 8
def __call__(self, x, c, k):
assert x.shape[1:] == (self.units,)
assert c.shape == (7,)
assert k.shape == (13, 7)
shape = (x.shape[-1], self.units)
winit = lambda: jax.random.normal(nj.seed(), shape, f32)
x = x @ self.value('kernel', winit)
if 'outer3' not in nj.context():
nj.context()['outer3'] = jnp.zeros((), i32)
nj.context()['outer3'] += 1
nj.context()['outer1'] += 1
inner = self.value('inner', jnp.array(0))
self.write('inner', inner + nj.context()['outer2'])
return x
class Net(nj.Module):
layers: int = 4
units: int = 8
def __call__(self, x):
if 'outer1' not in nj.context():
nj.context()['outer1'] = jnp.ones((), i32)
if 'outer2' not in nj.context():
nj.context()['outer2'] = jnp.ones((), i32)
nj.context()['outer1'] += 1
module = self.sub('linear', Layer, units=self.units)
c = jnp.zeros((self.layers, 7))
k = jnp.zeros((13, 7))
x = utils.LayerScan(module, self.layers)(x, c, k=k)
return x
def loss(self, x):
return self(x).mean()
class TestLayerScan:
def test_init(self, L=4, B=2, D=8):
x = np.random.normal(0, 1, (B, D))
net = Net(layers=L, units=D, name='net')
params = nj.init(net)({}, x, seed=0)
assert set(params.keys()) == {
'outer1', 'outer2', 'outer3',
'net/linear/kernel', 'net/linear/inner'}
assert params['net/linear/kernel'].shape == (L, D, D)
assert params['outer1'] == 1
assert params['outer2'] == 1
assert params['outer3'] == 0
assert params['net/linear/inner'].shape == (L,)
assert (params['net/linear/inner'] == 0).all()
for i in range(1, L):
assert not jnp.allclose(
params['net/linear/kernel'][0],
params['net/linear/kernel'][i])
def test_apply(self, L=4, B=2, D=8):
x = np.random.normal(0, 1, (B, D))
net = Net(layers=L, units=D, name='net')
params = nj.init(net)({}, x, seed=0)
params, out = nj.pure(net)(params, x)
assert out.shape == (B, D)
assert params['outer1'] == L + 2
assert params['outer2'] == 1
assert params['outer3'] == L
assert params['net/linear/inner'].shape == (L,)
assert (params['net/linear/inner'] == 1).all()
def test_grad(self, L=4, B=2, D=8):
x = np.random.normal(0, 1, (B, D))
net = Net(layers=L, units=D, name='net')
def fn(x):
if nj.creating():
net(x)
params = {k: v for k, v in net.values.items() if v.dtype == f32}
params = {net.path + '/' + k: v for k, v in params.items()}
loss, _, grads = nj.grad(lambda x: net(x).mean(), params.keys())(x)
params = {k: v - 0.1 * grads[k] for k, v in params.items()}
nj.context().update(params)
return loss
params = nj.init(net)({}, x, seed=0)
params, loss = nj.pure(fn)(params, x)
assert loss.shape == ()
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