def testSparseRepeatedIndices(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
repeated_index_update_var = variables.Variable([[1.0], [2.0]],
dtype=dtype)
aggregated_update_var = variables.Variable([[1.0], [2.0]],
dtype=dtype)
grad_repeated_index = ops.IndexedSlices(
constant_op.constant([0.1, 0.1], shape=[2, 1],
dtype=dtype),
constant_op.constant([1, 1]), constant_op.constant([2, 1]))
grad_aggregated = ops.IndexedSlices(
constant_op.constant([0.2], shape=[1, 1], dtype=dtype),
constant_op.constant([1]), constant_op.constant([2, 1]))
repeated_update = lamb.LAMBOptimizer().apply_gradients([
(grad_repeated_index, repeated_index_update_var)
])
aggregated_update = lamb.LAMBOptimizer().apply_gradients([
(grad_aggregated, aggregated_update_var)
])
variables.global_variables_initializer().run()
self.assertAllClose(aggregated_update_var.eval(),
self.evaluate(repeated_index_update_var))
for _ in range(3):
repeated_update.run()
aggregated_update.run()
self.assertAllClose(
aggregated_update_var.eval(),
self.evaluate(repeated_index_update_var))
def test_ops_with_var_and_lamb(self):
var_list = [
deo.get_variable('sp_var', initializer=0.0, dim=2),
]
opt_list = [
lamb.LAMBOptimizer(),
]
self.common_run_context(var_list, opt_list, name='lamb_test')
def testSlotsUniqueEager(self):
with context.eager_mode():
v1 = resource_variable_ops.ResourceVariable(1.)
v2 = resource_variable_ops.ResourceVariable(1.)
opt = lamb.LAMBOptimizer(learning_rate=1.)
opt.minimize(lambda: v1 + v2)
# There should be two non-slot variables, and two unique slot variables
# for v1 and v2 respectively.
self.assertEqual(6, len({id(v) for v in opt.variables()}))
def doTestSparse(self, use_resource=False):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
if use_resource:
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
else:
var0 = variables.RefVariable(var0_np)
var1 = variables.RefVariable(var1_np)
grads0_np_indices = np.array([0, 1], dtype=np.int32)
grads0 = ops.IndexedSlices(
constant_op.constant(grads0_np),
constant_op.constant(grads0_np_indices),
constant_op.constant([2]))
grads1_np_indices = np.array([0, 1], dtype=np.int32)
grads1 = ops.IndexedSlices(
constant_op.constant(grads1_np),
constant_op.constant(grads1_np_indices),
constant_op.constant([2]))
opt = lamb.LAMBOptimizer()
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
beta1_power, beta2_power = opt._get_beta_accumulators()
# Run 3 steps of Lamb
for t in range(1, 4):
self.assertAllCloseAccordingToType(
0.9**t, self.evaluate(beta1_power))
self.assertAllCloseAccordingToType(
0.999**t, self.evaluate(beta2_power))
update.run()
var0_np, m0, v0 = lamb_update_numpy(
var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = lamb_update_numpy(
var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testSparseDevicePlacement(self):
for index_dtype in [dtypes.int32, dtypes.int64]:
with self.cached_session(force_gpu=test.is_gpu_available()):
# If a GPU is available, tests that all optimizer ops can be placed on
# it (i.e. they have GPU kernels).
var = variables.Variable([[1.0], [2.0]])
indices = constant_op.constant([0, 1], dtype=index_dtype)
gathered_sum = math_ops.reduce_sum(
array_ops.gather(var, indices))
optimizer = lamb.LAMBOptimizer(learning_rate=3.0)
minimize_op = optimizer.minimize(gathered_sum)
variables.global_variables_initializer().run()
minimize_op.run()
def testSharing(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = lamb.LAMBOptimizer()
update1 = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
update2 = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
beta1_power, beta2_power = opt._get_beta_accumulators()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 3 steps of intertwined Lamb1 and Lamb2.
for t in range(1, 4):
self.assertAllCloseAccordingToType(
0.9**t, self.evaluate(beta1_power))
self.assertAllCloseAccordingToType(
0.999**t, self.evaluate(beta2_power))
if t % 2 == 0:
update1.run()
else:
update2.run()
var0_np, m0, v0 = lamb_update_numpy(
var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = lamb_update_numpy(
var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testTwoSessions(self):
optimizer = lamb.LAMBOptimizer()
with context.eager_mode():
var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
grads0 = constant_op.constant(np.array([0.1, 0.1]))
optimizer.apply_gradients([(grads0, var0)])
g = ops.Graph()
with g.as_default():
with session.Session():
var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
grads0 = constant_op.constant(np.array([0.1, 0.1]))
optimizer.apply_gradients([(grads0, var0)])
gg = ops.Graph()
with gg.as_default():
with session.Session():
var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
grads0 = constant_op.constant(np.array([0.1, 0.1]))
# If the optimizer saves any state not keyed by graph the following line
# fails.
optimizer.apply_gradients([(grads0, var0)])
def test_lamb_restrict_on_policy(self):
opt = lamb.LAMBOptimizer()
self.common_single_step_restrict_verification(opt)
def test_lamb_restrictor_update(self):
opt = lamb.LAMBOptimizer()
self.common_single_step_update_verification(opt)
def doTestBasic(self, use_resource=False, use_callable_params=False):
if context.executing_eagerly() and not use_resource:
self.skipTest(
"Skipping test with use_resource=False and executing eagerly.")
for i, dtype in enumerate(
[dtypes.half, dtypes.float32, dtypes.float64]):
with self.session(graph=ops.Graph()):
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
if use_resource:
var0 = resource_variable_ops.ResourceVariable(
var0_np, name="var0_%d" % i)
var1 = resource_variable_ops.ResourceVariable(
var1_np, name="var1_%d" % i)
else:
var0 = variables.RefVariable(var0_np)
var1 = variables.RefVariable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
learning_rate = lambda: 0.001
beta1 = lambda: 0.9
beta2 = lambda: 0.999
epsilon = lambda: 1e-8
if not use_callable_params:
learning_rate = learning_rate()
beta1 = beta1()
beta2 = beta2()
epsilon = epsilon()
opt = lamb.LAMBOptimizer(learning_rate=learning_rate)
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
opt_variables = opt.variables()
beta1_power, beta2_power = opt._get_beta_accumulators()
self.assertTrue(beta1_power is not None)
self.assertTrue(beta2_power is not None)
self.assertIn(beta1_power, opt_variables)
self.assertIn(beta2_power, opt_variables)
# Ensure that non-slot variables are the same type as the requested
# variables.
self.assertEqual(
use_resource,
resource_variable_ops.is_resource_variable(beta1_power))
self.assertEqual(
use_resource,
resource_variable_ops.is_resource_variable(beta2_power))
if not context.executing_eagerly():
with ops.Graph().as_default():
# Shouldn't return non-slot variables from other graphs.
self.assertEqual(0, len(opt.variables()))
self.evaluate(variables.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
beta1_power, beta2_power = opt._get_beta_accumulators()
# Run 3 steps of Lamb
for t in range(1, 4):
if not context.executing_eagerly():
self.evaluate(update)
elif t > 1:
opt.apply_gradients(zip([grads0, grads1],
[var0, var1]))
self.assertAllCloseAccordingToType(
0.9**(t + 1), self.evaluate(beta1_power))
self.assertAllCloseAccordingToType(
0.999**(t + 1), self.evaluate(beta2_power))
var0_np, m0, v0 = lamb_update_numpy(
var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = lamb_update_numpy(
var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
if use_resource:
self.assertEqual("var0_%d/LAMB:0" % (i, ),
opt.get_slot(var=var0, name="m").name)
def test_lamb_minimize_trainable(self):
base_opt = lamb.LAMBOptimizer(0.1)
test_opt = lamb.LAMBOptimizer(0.1)
self.common_minimize_trainable(base_opt, test_opt, name='lamb')