def test_parallel_in_out_empirical(self, same_inputs):
test_utils.stub_out_pmap(batch, 2)
rng = random.PRNGKey(0)
input_key1, input_key2, net_key = random.split(rng, 3)
x1_1, x1_2, x1_3 = random.normal(input_key1, (3, 4, 10))
x2_1, x2_2, x2_3 = random.normal(input_key2, (3, 8, 10))
x1 = (x1_1, (x1_2, x1_3))
x2 = (x2_1, (x2_2, x2_3))
def net(N_out):
return stax.parallel(
stax.Dense(N_out),
stax.parallel(stax.Dense(N_out + 1), stax.Dense(N_out + 2)))
# Check NNGP.
init_fn, apply_fn, _ = net(WIDTH)
_, params = init_fn(net_key, ((-1, 10), ((-1, 10), (-1, 10))))
kernel_fn = jit(empirical.empirical_nngp_fn(apply_fn))
batch_kernel_fn = jit(batch.batch(kernel_fn, 2))
test_utils.assert_close_matrices(self, kernel_fn(x1, x2, params),
batch_kernel_fn(x1, x2, params), RTOL)
# Check NTK.
init_fn, apply_fn, _ = stax.serial(net(WIDTH), net(1))
_, params = init_fn(net_key, ((-1, 10), ((-1, 10), (-1, 10))))
kernel_fn = jit(empirical.empirical_ntk_fn(apply_fn))
batch_kernel_fn = jit(batch.batch(kernel_fn, 2))
test_utils.assert_close_matrices(self, kernel_fn(x1, x2, params),
batch_kernel_fn(x1, x2, params), RTOL)
def test_sample_vs_analytic_nngp(self, batch_size, device_count,
store_on_device):
test_utils.stub_out_pmap(batch, device_count)
x1, x2, init_fn, apply_fn, stax_kernel_fn, key = _get_inputs_and_model(
WIDTH, 256,
xla_bridge.get_backend().platform == 'tpu')
sample = monte_carlo.monte_carlo_kernel_fn(init_fn, apply_fn, key, 200,
batch_size, device_count,
store_on_device)
ker_empirical = sample(x1, x2, 'nngp')
ker_analytic = stax_kernel_fn(x1, x2, 'nngp')
test_utils.assert_close_matrices(self, ker_analytic, ker_empirical,
2e-2)
def test_parallel_in_out(self, same_inputs):
test_utils.stub_out_pmap(batch, 2)
rng = random.PRNGKey(0)
input_key1, input_key2, mc_key = random.split(rng, 3)
x1_1, x1_2, x1_3 = random.normal(input_key1, (3, 4, 10))
x2_1, x2_2, x2_3 = random.normal(input_key2, (3, 8, 10))
x1 = (x1_1, (x1_2, x1_3))
x2 = (x2_1, (x2_2, x2_3))
N = WIDTH
def net(N_out):
return stax.parallel(
stax.Dense(N_out),
stax.parallel(stax.Dense(N_out + 1), stax.Dense(N_out + 2)))
# Check NNGP.
readin = net(N)
readout = net(1)
K_readin_fn = jit(readin[2])
K_readout_fn = jit(partial(readout[2], get='nngp'))
batch_K_readin_fn = batch.batch(K_readin_fn, 2)
batch_K_readout_fn = batch.batch(K_readout_fn, 2)
test_utils.assert_close_matrices(
self, K_readout_fn(K_readin_fn(x1, x2)),
batch_K_readout_fn(batch_K_readin_fn(x1, x2)), RTOL)
# Check Both.
K_readin_fn = jit(readin[2])
K_readout_fn = jit(partial(readout[2], get=('nngp', 'ntk')))
batch_K_readin_fn = batch.batch(K_readin_fn, 2)
batch_K_readout_fn = batch.batch(K_readout_fn, 2)
get_ntk = utils.nt_tree_fn()(lambda k: k.ntk)
test_utils.assert_close_matrices(
self, get_ntk(K_readout_fn(K_readin_fn(x1, x2))),
get_ntk(batch_K_readout_fn(batch_K_readin_fn(x1, x2))), RTOL)
def test_monte_carlo_vs_analytic_ntk(self, batch_size, device_count,
store_on_device):
test_utils.stub_out_pmap(batch, device_count)
x1, x2, init_fn, apply_fn, stax_kernel_fn, key = _get_inputs_and_model(
256, 2,
xla_bridge.get_backend().platform == 'tpu')
sample = monte_carlo.monte_carlo_kernel_fn(init_fn, apply_fn, key, 100,
batch_size, device_count,
store_on_device)
ker_empirical = sample(x1, x2, 'ntk')
ker_empirical = (np.sum(ker_empirical, axis=(-1, -2)) /
ker_empirical.shape[-1])
ker_analytic = stax_kernel_fn(x1, x2, 'ntk')
test_utils.assert_close_matrices(self, ker_analytic, ker_empirical,
2e-2)
def _check_agreement_with_empirical(self, net, same_inputs, is_conv,
use_dropout, is_ntk, proj_into_2d):
(init_fn, apply_fn, kernel_fn), input_shape = net
num_samples = N_SAMPLES * 5 if use_dropout else N_SAMPLES
key = random.PRNGKey(1)
x1, x2 = _get_inputs(key, is_conv, same_inputs, input_shape)
x1_out_shape, params = init_fn(key, x1.shape)
if same_inputs:
assert (x2 is None)
if x2 is None:
x2_out_shape = x1_out_shape
else:
x2_out_shape, params = init_fn(key, x2.shape)
del (params)
def _get_empirical(n_samples, get):
kernel_fn_empirical = monte_carlo.monte_carlo_kernel_fn(
init_fn, apply_fn, key, n_samples)
if same_inputs:
assert (x2 is None)
return kernel_fn_empirical(x1, x2, get)
if proj_into_2d == 'ATTN_PARAM':
# no analytic kernel available, just test forward/backward pass
_get_empirical(1, 'ntk' if is_ntk else 'nngp')
else:
if is_ntk:
exact, shape1, shape2 = kernel_fn(x1, x2,
('ntk', 'shape1', 'shape2'))
empirical = np.reshape(_get_empirical(num_samples, 'ntk'),
exact.shape)
else:
exact, shape1, shape2 = kernel_fn(x1, x2,
('nngp', 'shape1', 'shape2'))
empirical = _get_empirical(num_samples, 'nngp')
test_utils.assert_close_matrices(self, exact, empirical, RTOL)
self.assertEqual(shape1, x1_out_shape)
self.assertEqual(shape2, x2_out_shape)
def test_fan_in_conv(self, same_inputs, axis, n_branches, get, branch_in,
readout):
if xla_bridge.get_backend().platform == 'cpu':
raise jtu.SkipTest('Not running CNNs on CPU to save time.')
if axis in (None, 0, 1, 2) and branch_in == 'dense_after_branch_in':
raise jtu.SkipTest('`FanInSum` and `FanInConcat(0/1/2)` '
'require `is_gaussian`.')
if axis == 3 and branch_in == 'dense_before_branch_in':
raise jtu.SkipTest(
'`FanInConcat` on feature axis requires a dense layer '
'after concatenation.')
key = random.PRNGKey(1)
X0_1 = random.normal(key, (2, 5, 6, 3))
X0_2 = None if same_inputs else random.normal(key, (3, 5, 6, 3))
if xla_bridge.get_backend().platform == 'tpu':
width = 2048
n_samples = 1024
tol = 0.02
else:
width = 1024
n_samples = 512
tol = 0.01
conv = stax.Conv(out_chan=width,
filter_shape=(3, 3),
padding='SAME',
W_std=1.25,
b_std=0.1)
input_layers = [conv, stax.FanOut(n_branches)]
branches = []
for b in range(n_branches):
branch_layers = [FanInTest._get_phi(b)]
for i in range(b):
branch_layers += [
stax.Conv(out_chan=width,
filter_shape=(i + 1, 4 - i),
padding='SAME',
W_std=1.25 + i,
b_std=0.1 + i),
FanInTest._get_phi(i)
]
if branch_in == 'dense_before_branch_in':
branch_layers += [conv]
branches += [stax.serial(*branch_layers)]
output_layers = [
stax.FanInSum() if axis is None else stax.FanInConcat(axis),
stax.Relu(),
stax.GlobalAvgPool() if readout == 'pool' else stax.Flatten()
]
if branch_in == 'dense_after_branch_in':
output_layers.insert(1, conv)
nn = stax.serial(*(input_layers + [stax.parallel(*branches)] +
output_layers))
init_fn, apply_fn, kernel_fn = stax.serial(
nn, stax.Dense(1 if get == 'ntk' else width, 1.25, 0.5))
kernel_fn_mc = monte_carlo.monte_carlo_kernel_fn(
init_fn,
apply_fn,
key,
n_samples,
device_count=0 if axis in (0, -4) else -1)
exact = kernel_fn(X0_1, X0_2, get=get)
empirical = kernel_fn_mc(X0_1, X0_2, get=get)
empirical = empirical.reshape(exact.shape)
test_utils.assert_close_matrices(self, empirical, exact, tol)
def test_fan_in_fc(self, same_inputs, axis, n_branches, get, branch_in):
if axis in (None, 0) and branch_in == 'dense_after_branch_in':
raise jtu.SkipTest('`FanInSum` and `FanInConcat(0)` '
'require `is_gaussian`.')
if axis == 1 and branch_in == 'dense_before_branch_in':
raise jtu.SkipTest(
'`FanInConcat` on feature axis requires a dense layer'
'after concatenation.')
key = random.PRNGKey(1)
X0_1 = random.normal(key, (10, 20))
X0_2 = None if same_inputs else random.normal(key, (8, 20))
if xla_bridge.get_backend().platform == 'tpu':
width = 2048
n_samples = 1024
tol = 0.02
else:
width = 1024
n_samples = 256
tol = 0.01
dense = stax.Dense(width, 1.25, 0.1)
input_layers = [dense, stax.FanOut(n_branches)]
branches = []
for b in range(n_branches):
branch_layers = [FanInTest._get_phi(b)]
for i in range(b):
branch_layers += [
stax.Dense(width, 1. + 2 * i, 0.5 + i),
FanInTest._get_phi(i)
]
if branch_in == 'dense_before_branch_in':
branch_layers += [dense]
branches += [stax.serial(*branch_layers)]
output_layers = [
stax.FanInSum() if axis is None else stax.FanInConcat(axis),
stax.Relu()
]
if branch_in == 'dense_after_branch_in':
output_layers.insert(1, dense)
nn = stax.serial(*(input_layers + [stax.parallel(*branches)] +
output_layers))
if get == 'nngp':
init_fn, apply_fn, kernel_fn = nn
elif get == 'ntk':
init_fn, apply_fn, kernel_fn = stax.serial(
nn, stax.Dense(1, 1.25, 0.5))
else:
raise ValueError(get)
kernel_fn_mc = monte_carlo.monte_carlo_kernel_fn(init_fn,
apply_fn,
key,
n_samples,
device_count=0)
exact = kernel_fn(X0_1, X0_2, get=get)
empirical = kernel_fn_mc(X0_1, X0_2, get=get)
empirical = empirical.reshape(exact.shape)
test_utils.assert_close_matrices(self, empirical, exact, tol)