def testSparseRepeatedIndices(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
for dtype in [tf.half, tf.float32, tf.float64]:
with tf.Graph().as_default(), self.cached_session():
repeated_index_update_var = tf.Variable(
[[1.0], [2.0]], dtype=dtype)
aggregated_update_var = tf.Variable(
[[1.0], [2.0]], dtype=dtype)
grad_repeated_index = tf.IndexedSlices(
tf.constant(
[0.1, 0.1], shape=[2, 1], dtype=dtype),
tf.constant([1, 1]),
tf.constant([2, 1]))
grad_aggregated = tf.IndexedSlices(
tf.constant(
[0.2], shape=[1, 1], dtype=dtype),
tf.constant([1]),
tf.constant([2, 1]))
repeated_update = adam.NonFusedAdam().apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
aggregated_update = adam.NonFusedAdam().apply_gradients(
[(grad_aggregated, aggregated_update_var)])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(aggregated_update_var,
self.evaluate(repeated_index_update_var))
for _ in range(3):
repeated_update.run()
aggregated_update.run()
self.assertAllClose(aggregated_update_var,
self.evaluate(repeated_index_update_var))
def testSparseWithAmsgrad(self):
# dtypes.half does not work on gpu + eager.
for dtype in [tf.float32, tf.float64]:
with self.cached_session():
m0 = np.array([[0.0], [0.0]])
v0 = np.array([[0.0], [0.0]])
v0hat = np.array([[0.0], [0.0]])
indices_np = np.array([1])
indices = tf.constant(indices_np, dtype=tf.int32)
var0_np = np.array([[1.0], [2.0]], dtype=dtype.as_numpy_dtype)
repeated_index_update_var = tf.Variable(var0_np, dtype=dtype)
aggregated_update_var = tf.Variable(var0_np, dtype=dtype)
grads0_np = np.array([[0.2]], dtype=dtype.as_numpy_dtype)
grad_repeated_index = tf.IndexedSlices(
tf.constant([0.1, 0.1], shape=[2, 1], dtype=dtype),
tf.constant([1, 1]), tf.constant([2, 1]))
grad_aggregated = tf.IndexedSlices(grads0_np, indices,
tf.constant([2, 1]))
opt_repeated = adam.NonFusedAdam(amsgrad=True)
opt_aggregated = adam.NonFusedAdam(amsgrad=True)
if not tf.executing_eagerly():
repeated_update = opt_repeated.apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
aggregated_update = opt_aggregated.apply_gradients(
[(grad_aggregated, aggregated_update_var)])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(
self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
for t in range(3):
if not tf.executing_eagerly():
self.evaluate(repeated_update)
self.evaluate(aggregated_update)
else:
opt_repeated.apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
opt_aggregated.apply_gradients(
[(grad_aggregated, aggregated_update_var)])
var0_np, m0, v0, v0hat = adam_sparse_update_numpy_amsgrad(
var0_np, indices_np, grads0_np, t, m0, v0, v0hat)
# Validate updated params
self.assertAllCloseAccordingToType(
var0_np, self.evaluate(aggregated_update_var))
self.assertAllCloseAccordingToType(
self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
def doTestBasic(self, use_callable_params=False):
for i, dtype in enumerate([tf.half, tf.float32, tf.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 = tf.Variable(var0_np, name="var0_%d" % i)
var1 = tf.Variable(var1_np, name="var1_%d" % i)
grads0 = tf.constant(grads0_np)
grads1 = tf.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 = adam.NonFusedAdam(learning_rate=learning_rate)
if not tf.executing_eagerly():
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Run 3 steps of NonFusedAdam
for t in range(3):
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
if not tf.executing_eagerly():
self.evaluate(update)
else:
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(
var0_np, self.evaluate(var0), rtol=1e-4, atol=1e-4)
self.assertAllCloseAccordingToType(
var1_np, self.evaluate(var1), rtol=1e-4, atol=1e-4)
def testSparseDevicePlacement(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
for index_dtype in [tf.int32, tf.int64]:
with tf.Graph().as_default(), self.cached_session(
force_gpu=tf.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 = tf.Variable([[1.0], [2.0]])
indices = tf.constant([0, 1], dtype=index_dtype)
g_sum = lambda: tf.reduce_sum(tf.gather(var, indices)) # pylint: disable=cell-var-from-loop
optimizer = adam.NonFusedAdam(3.0)
minimize_op = optimizer.minimize(g_sum, var_list=[var])
self.evaluate(tf.compat.v1.global_variables_initializer())
minimize_op.run()
def testBasicWithLearningRateInverseTimeDecay(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
for i, dtype in enumerate([tf.half, tf.float32, tf.float64]):
with tf.Graph().as_default(), 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 = tf.Variable(var0_np, name="var0_%d" % i)
var1 = tf.Variable(var1_np, name="var1_%d" % i)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 0.001
decay = 0.5
lr_schedule = learning_rate_schedule.InverseTimeDecay(
learning_rate, decay_steps=1.0, decay_rate=decay)
beta_1 = 0.9
beta_2 = 0.999
epsilon = 1e-7
opt = adam.NonFusedAdam(
learning_rate=lr_schedule,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Run 3 steps of NonFusedAdam
for t in range(3):
self.evaluate(update)
lr_np = learning_rate / (1 + decay * t)
var0_np, m0, v0 = adam_update_numpy(
var0_np, grads0_np, t, m0, v0, lr=lr_np)
var1_np, m1, v1 = adam_update_numpy(
var1_np, grads1_np, t, m1, v1, lr=lr_np)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
def testSparse(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
for dtype in [tf.half, tf.float32, tf.float64]:
with tf.Graph().as_default(), 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, 1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.0, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.0, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0_np_indices = np.array([0, 2], dtype=np.int32)
grads0 = tf.IndexedSlices(
tf.constant(grads0_np[grads0_np_indices]),
tf.constant(grads0_np_indices), tf.constant([3]))
grads1_np_indices = np.array([0, 2], dtype=np.int32)
grads1 = tf.IndexedSlices(
tf.constant(grads1_np[grads1_np_indices]),
tf.constant(grads1_np_indices), tf.constant([3]))
opt = adam.NonFusedAdam()
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 3.0, 4.0], self.evaluate(var1))
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
# Run 3 steps of NonFusedAdam
for t in range(3):
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
update.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_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 testBasicWithAmsgrad(self):
for i, dtype in enumerate([tf.half, tf.float32, tf.float64]):
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, v0hat, m1, v1, v1hat = 0.0, 0.0, 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 = tf.Variable(var0_np, name="var0_%d" % i)
var1 = tf.Variable(var1_np, name="var1_%d" % i)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
opt = adam.NonFusedAdam(amsgrad=True)
if not tf.executing_eagerly():
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Run 3 steps of NonFusedAdam
for t in range(3):
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
if not tf.executing_eagerly():
self.evaluate(update)
else:
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
var0_np, m0, v0, v0hat = adam_update_numpy_amsgrad(
var0_np, grads0_np, t, m0, v0, v0hat)
var1_np, m1, v1, v1hat = adam_update_numpy_amsgrad(
var1_np, grads1_np, t, m1, v1, v1hat)
# Validate updated params
self.assertAllCloseAccordingToType(
var0_np, self.evaluate(var0), rtol=1e-4, atol=1e-4)
self.assertAllCloseAccordingToType(
var1_np, self.evaluate(var1), rtol=1e-4, atol=1e-4)
class KerasOptimizerBenchmark(tf.test.Benchmark,
metaclass=ParameterizedBenchmark):
"""Keras optimizer benchmarks."""
# The parameter of each benchmark test is a tuple, and the first one is
# the optimizer name.
_benchmark_parameters = benchmark_util.generate_benchmark_params_cpu_gpu([
("Adam", tf.keras.optimizers.Adam(), 10),
("NonFusedAdam", adam.NonFusedAdam(), 10),
])
def benchmark_optimizer(self, optimizer, num_iters):
"""Optimizer benchmark with Bidirectional LSTM model on IMDB data.
Arguments:
optimizer: The optimizer instance to be benchmarked.
num_iters: The number of iterations to run for performance measurement.
"""
model, train_x, train_y = bidirect_imdb_lstm_config()
metrics, wall_time, extras = benchmark_util.measure_performance(
model,
x=train_x,
y=train_y,
batch_size=512,
optimizer=optimizer,
loss="binary_crossentropy",
metrics=["accuracy"])
name = benchmark_util.get_benchmark_name(self._get_name())
metadata = {
"implementation": name[0],
"model_name": "optimizers",
"parameters": "lstm.512",
}
extras.update(metadata)
self.report_benchmark(iters=num_iters,
wall_time=wall_time,
metrics=metrics,
extras=extras)