def streaming_tp_fp_arrays(num_gbboxes, tp, fp,
metrics_collections=None,
updates_collections=None,
name=None):
"""Streaming computation of True and False Positive arrays.
"""
with variable_scope.variable_scope(name, 'streaming_tp_fp',
[num_gbboxes, tp, fp]):
num_gbboxes = tf.cast(num_gbboxes, tf.int32)
tp = tf.cast(tp, tf.bool)
fp = tf.cast(fp, tf.bool)
# Reshape TP and FP tensors and clean away 0 class values.
tp = tf.reshape(tp, [-1])
fp = tf.reshape(fp, [-1])
# Local variables accumlating information over batches.
v_num_objects = _create_local('v_num_gbboxes', shape=[], dtype=tf.int32)
v_tp = _create_local('v_tp', shape=[0, ], dtype=tf.bool)
v_fp = _create_local('v_fp', shape=[0, ], dtype=tf.bool)
# Update operations.
num_objects_op = state_ops.assign_add(v_num_objects,
tf.reduce_sum(num_gbboxes))
tp_op = state_ops.assign(v_tp, tf.concat([v_tp, tp], axis=0),
validate_shape=False)
fp_op = state_ops.assign(v_fp, tf.concat([v_fp, fp], axis=0),
validate_shape=False)
# Value and update ops.
val = (v_num_objects, v_tp, v_fp)
with ops.control_dependencies([num_objects_op, tp_op, fp_op]):
update_op = (num_objects_op, tp_op, fp_op)
return val, update_op
def _Update_global_variables():
global_norm = []
# a = a / t
for g in grad_vars:
global_norm.append(state_ops.assign(g, g / self._period))
# apply
with ops.control_dependencies(global_norm):
apply_global_op = self._opt.apply_gradients(
zip(grad_vars, global_center_vars))
# pull
with ops.control_dependencies([apply_global_op]):
update_ops = []
if global_step:
with ops.colocate_with(global_step):
update_ops.append(state_ops.assign_add(global_step, 1))
for lvar in local_vars:
g_val = self._global_map[lvar].read_value()
update_ops.append(state_ops.assign(lvar, g_val))
for grad_var in grad_vars:
update_ops.append(
state_ops.assign(grad_var, array_ops.zeros_like(grad_var)))
variable_update = control_flow_ops.group(*(update_ops))
return variable_update
def _apply_sparse(self, grad, var):
beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad.values * (1 - beta1_t)
m_t = state_ops.assign(m, m * beta1_t,
use_locking=self._use_locking)
m_t = state_ops.scatter_add(m_t, grad.indices, m_scaled_g_values,
use_locking=self._use_locking)
# u_t = max(beta_2 * u_{t-1}, L1(g_t))
# theta_t = theta_{t-1} - alpha/(1-beta_1).m_t/u_t
v = self.get_slot(var, "v")
g_abs_values = tensorflow.abs(g_t)
v_t = state_ops.assign(v, v * beta_2, use_locking = self._use_locking)
v_t = state_ops.assign_max(v_t, grad.indices, g_abs_values,
use_locking=self._use_locking)
var_update = state_ops.assign_sub(var,
lr*m_t/(v_t*(1 - beta_1)),
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t])
def testMultiplyInverseAgainstExplicit(self):
with ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
input_dim, output_dim = 3, 2
inputs = array_ops.zeros([32, input_dim])
outputs = array_ops.zeros([32, output_dim])
params = array_ops.zeros([input_dim, output_dim])
block = fb.FullyConnectedKFACBasicFB(
lc.LayerCollection(), inputs, outputs, has_bias=False)
grads = outputs**2
damping = 0. # This test is only valid without damping.
block.instantiate_factors((grads,), damping)
sess.run(state_ops.assign(block._input_factor._cov, _make_psd(3)))
sess.run(state_ops.assign(block._output_factor._cov, _make_psd(2)))
sess.run(block._input_factor.make_inverse_update_ops())
sess.run(block._output_factor.make_inverse_update_ops())
v_flat = np.arange(6, dtype=np.float32)
vector = utils.column_to_tensors(params, array_ops.constant(v_flat))
output = block.multiply_inverse(vector)
output_flat = sess.run(utils.tensors_to_column(output)).ravel()
full = sess.run(block.full_fisher_block())
explicit = np.dot(np.linalg.inv(full + damping * np.eye(6)), v_flat)
self.assertAllClose(output_flat, explicit)
def _f():
# Note that there is a race condition here, so we do a best effort
# updates here. We reset update_in_steps first so that other workers
# don't duplicate the updates. Also we update cluster_center_vars
# before resetting total_counts to avoid large updates to
# cluster_centers_updated based on partially updated
# cluster_center_vars.
with ops.control_dependencies([
state_ops.assign(update_in_steps,
self._mini_batch_steps_per_iteration - 1)
]):
with ops.colocate_with(
cluster_centers_updated, ignore_existing=True):
if self._distance_metric == COSINE_DISTANCE:
cluster_centers = nn_impl.l2_normalize(
cluster_centers_updated, dim=1)
else:
cluster_centers = cluster_centers_updated
with ops.colocate_with(cluster_centers_var, ignore_existing=True):
with ops.control_dependencies(
[state_ops.assign(cluster_centers_var, cluster_centers)]):
with ops.colocate_with(None, ignore_existing=True):
with ops.control_dependencies([
state_ops.assign(total_counts,
array_ops.zeros_like(total_counts))
]):
return array_ops.identity(update_in_steps)
def _testDefaultGraphInThread(self, constructed_event, continue_event, i):
with session.Session() as s:
self.assertEqual(ops.get_default_graph(), s.graph)
a = constant_op.constant(1.0, shape=[1, 2])
b = constant_op.constant(2.0, shape=[2, 3])
c = math_ops.matmul(a, b)
v = variables.Variable(c, name='var_%d' % i)
# Block here until all threads have constructed their graph.
constructed_event.set()
continue_event.wait()
assign_c_to_v = state_ops.assign(v, c)
v.initializer.run()
assign_c_to_v.eval()
v_val = v.eval()
self.assertAllEqual([[4.0, 4.0, 4.0]], v_val)
d = constant_op.constant(3.0, shape=[2, 3])
e = math_ops.matmul(a, d)
assign_e_to_v = state_ops.assign(v, e)
e_val = e.eval()
self.assertAllEqual([[6.0, 6.0, 6.0]], e_val)
v_val = v.eval()
self.assertAllEqual([[4.0, 4.0, 4.0]], v_val)
s.run(assign_e_to_v)
v_val = v.eval()
self.assertAllEqual([[6.0, 6.0, 6.0]], v_val)
self.assertEqual(ops.get_default_graph(), s.graph)
def testMultiplyInverseAgainstExplicit(self):
with ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
params = array_ops.zeros((2, 2, 2, 2))
inputs = array_ops.zeros((2, 2, 2, 2))
outputs = array_ops.zeros((2, 2, 2, 2))
block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, (1, 1, 1, 1),
'SAME')
block.register_additional_minibatch(inputs, outputs)
grads = outputs**2
damping = 0. # This test is only valid without damping.
block.instantiate_factors(([grads],), damping)
sess.run(state_ops.assign(block._input_factor._cov, _make_psd(8)))
sess.run(state_ops.assign(block._output_factor._cov, _make_psd(2)))
sess.run(block._input_factor.make_inverse_update_ops())
sess.run(block._output_factor.make_inverse_update_ops())
v_flat = np.arange(16, dtype=np.float32)
vector = utils.column_to_tensors(params, array_ops.constant(v_flat))
output = block.multiply_inverse(vector)
output_flat = sess.run(utils.tensors_to_column(output)).ravel()
full = sess.run(block.full_fisher_block())
explicit = np.dot(np.linalg.inv(full + damping * np.eye(16)), v_flat)
self.assertAllClose(output_flat, explicit)
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [state_ops.assign_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * ( # pylint: disable=g-no-augmented-assignment
1. / (1. + self.decay * math_ops.cast(self.iterations,
K.dtype(self.decay))))
# momentum
shapes = [K.int_shape(p) for p in params]
moments = [K.zeros(shape) for shape in shapes]
self.weights = [self.iterations] + moments
for p, g, m in zip(params, grads, moments):
v = self.momentum * m - lr * g # velocity
self.updates.append(state_ops.assign(m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(state_ops.assign(p, new_p))
return self.updates
def test_fn(a): state_ops.assign(a, a + 1) b = a + 1 state_ops.assign(a, a + 1) c = b + 1 d = c + 1 return d
def get_placements(self, *args, **kwargs):
num_children = self.hparams.num_children
with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)):
actions_cache = variable_scope.get_local_variable(
"actions_cache",
initializer=init_ops.zeros_initializer,
dtype=dtypes.int32,
shape=[num_children, self.num_groups],
trainable=False)
x = array_ops.tile(self.seq2seq_input_layer, [num_children, 1, 1])
last_c, last_h, attn_mem = self.encode(x)
actions, log_probs = {}, {}
actions["sample"], log_probs["sample"] = (
self.decode(
x, last_c, last_h, attn_mem, mode="sample"))
actions["target"], log_probs["target"] = (
self.decode(
x,
last_c,
last_h,
attn_mem,
mode="target",
y=actions_cache))
actions["greedy"], log_probs["greedy"] = (
self.decode(
x, last_c, last_h, attn_mem, mode="greedy"))
actions["sample"] = control_flow_ops.cond(
self.global_step < self.hparams.stop_sampling,
lambda: state_ops.assign(actions_cache, actions["sample"]),
lambda: state_ops.assign(actions_cache, actions["target"]))
self.actions_cache = actions_cache
return actions, log_probs
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
shapes = [K.int_shape(p) for p in params]
accumulators = [K.zeros(shape) for shape in shapes]
delta_accumulators = [K.zeros(shape) for shape in shapes]
self.weights = accumulators + delta_accumulators
self.updates = [state_ops.assign_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * ( # pylint: disable=g-no-augmented-assignment
1. / (1. + self.decay * math_ops.cast(self.iterations,
K.dtype(self.decay))))
for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators):
# update accumulator
new_a = self.rho * a + (1. - self.rho) * math_ops.square(g)
self.updates.append(state_ops.assign(a, new_a))
# use the new accumulator and the *old* delta_accumulator
update = g * K.sqrt(d_a + self.epsilon) / K.sqrt(new_a + self.epsilon)
new_p = p - lr * update
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(state_ops.assign(p, new_p))
# update delta_accumulator
new_d_a = self.rho * d_a + (1 - self.rho) * math_ops.square(update)
self.updates.append(state_ops.assign(d_a, new_d_a))
return self.updates
def testIsVariableInitialized(self):
for use_gpu in [True, False]:
with self.test_session(use_gpu=use_gpu):
v0 = state_ops.variable_op([1, 2], dtypes.float32)
self.assertEqual(False, variables.is_variable_initialized(v0).eval())
state_ops.assign(v0, [[2.0, 3.0]]).eval()
self.assertEqual(True, variables.is_variable_initialized(v0).eval())
def test_stop_based_on_num_step(self):
h = basic_session_run_hooks.StopAtStepHook(num_steps=10)
with ops.Graph().as_default():
global_step = variables.get_or_create_global_step()
no_op = control_flow_ops.no_op()
h.begin()
with session_lib.Session() as sess:
mon_sess = monitored_session._HookedSession(sess, [h])
sess.run(state_ops.assign(global_step, 5))
h.after_create_session(sess, None)
mon_sess.run(no_op)
self.assertFalse(mon_sess.should_stop())
sess.run(state_ops.assign(global_step, 13))
mon_sess.run(no_op)
self.assertFalse(mon_sess.should_stop())
sess.run(state_ops.assign(global_step, 14))
mon_sess.run(no_op)
self.assertFalse(mon_sess.should_stop())
sess.run(state_ops.assign(global_step, 15))
mon_sess.run(no_op)
self.assertTrue(mon_sess.should_stop())
sess.run(state_ops.assign(global_step, 16))
mon_sess._should_stop = False
mon_sess.run(no_op)
self.assertTrue(mon_sess.should_stop())
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
shapes = [K.int_shape(p) for p in params]
accumulators = [K.zeros(shape) for shape in shapes]
self.weights = accumulators
self.updates = [state_ops.assign_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * ( # pylint: disable=g-no-augmented-assignment
1. /
(1. +
self.decay * math_ops.cast(self.iterations, K.dtype(self.decay))))
for p, g, a in zip(params, grads, accumulators):
new_a = a + math_ops.square(g) # update accumulator
self.updates.append(state_ops.assign(a, new_a))
new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(state_ops.assign(p, new_p))
return self.updates
def _apply_dense(self, grad, var):
beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * (1 - beta1_t)
m_t = state_ops.assign(m, beta1_t * m + m_scaled_g_values, use_locking=self._use_locking)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * (1 - beta2_t)
v_t = state_ops.assign(v, beta2_t * v + v_scaled_g_values, use_locking=self._use_locking)
# amsgrad
vhat = self.get_slot(var, "vhat")
vhat_t = state_ops.assign(vhat, math_ops.maximum(v_t, vhat))
v_sqrt = math_ops.sqrt(vhat_t)
var_update = state_ops.assign_sub(var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t, vhat_t])
def _resource_apply_sparse(self, grad, var, indices):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
local_step = math_ops.cast(self.iterations + 1, var_dtype)
beta_1_power = math_ops.pow(beta_1_t, local_step)
beta_2_power = math_ops.pow(beta_2_t, local_step)
epsilon_t = self._get_hyper('epsilon', var_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta_2_power) / (1 - beta_1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, 'm')
m_scaled_g_values = grad * (1 - beta_1_t)
m_t = state_ops.assign(m, m * beta_1_t, use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
# m_bar = (1 - beta1) * g_t + beta1 * m_t
m_bar = m_scaled_g_values + beta_1_t * array_ops.gather(m_t, indices)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, 'v')
v_scaled_g_values = (grad * grad) * (1 - beta_2_t)
v_t = state_ops.assign(v, v * beta_2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
v_t_slice = array_ops.gather(v_t, indices)
v_sqrt = math_ops.sqrt(v_t_slice)
var_update = self._resource_scatter_add(var, indices,
-lr * m_bar / (v_sqrt + epsilon_t))
return control_flow_ops.group(*[var_update, m_bar, v_t])
def testDeferredSlotRestoration(self):
checkpoint_directory = self.get_temp_dir()
root = trackable_utils.Checkpoint()
root.var = trackable_utils.add_variable(
root, name="var", initializer=0.)
optimizer = adam.AdamOptimizer(0.1)
if context.executing_eagerly():
optimizer.minimize(root.var.read_value)
else:
train_op = optimizer.minimize(root.var)
# Note that `optimizer` has not been added as a dependency of
# `root`. Create a one-off grouping so that slot variables for `root.var`
# get initialized too.
self.evaluate(trackable_utils.gather_initializers(
trackable_utils.Checkpoint(root=root, optimizer=optimizer)))
self.evaluate(train_op)
self.evaluate(state_ops.assign(root.var, 12.))
no_slots_path = root.save(os.path.join(checkpoint_directory, "no_slots"))
root.optimizer = optimizer
self.evaluate(state_ops.assign(root.var, 13.))
self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var),
14.))
slots_path = root.save(os.path.join(checkpoint_directory, "with_slots"))
new_root = trackable_utils.Checkpoint()
# Load the slot-containing checkpoint (deferred), then immediately overwrite
# the non-slot variable (also deferred).
slot_status = new_root.restore(slots_path)
no_slot_status = new_root.restore(no_slots_path)
with self.assertRaises(AssertionError):
no_slot_status.assert_consumed()
new_root.var = trackable_utils.add_variable(
new_root, name="var", shape=[])
no_slot_status.assert_consumed()
no_slot_status.run_restore_ops()
self.assertEqual(12., self.evaluate(new_root.var))
new_root.optimizer = adam.AdamOptimizer(0.1)
slot_status.assert_existing_objects_matched()
with self.assertRaisesRegexp(AssertionError, "beta1_power"):
slot_status.assert_consumed()
self.assertEqual(12., self.evaluate(new_root.var))
if context.executing_eagerly():
# Slot variables are only created with restoring initializers when
# executing eagerly.
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
else:
self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var),
None)
if context.executing_eagerly():
new_root.optimizer.minimize(new_root.var.read_value)
else:
train_op = new_root.optimizer.minimize(new_root.var)
# The slot variable now exists; restore() didn't create it, but we should
# now have a restore op for it.
slot_status.run_restore_ops()
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
self.evaluate(train_op)
slot_status.assert_consumed()
def _apply_sparse_shared(self, grad, var, indices, scatter_add):
beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * (1 - beta1_t)
m_t = state_ops.assign(m, m * beta1_t,
use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = scatter_add(m, indices, m_scaled_g_values)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * (1 - beta2_t)
v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = scatter_add(v, indices, v_scaled_g_values)
v_sqrt = math_ops.sqrt(v_t)
var_update = state_ops.assign_sub(var,
lr * m_t / (v_sqrt + epsilon_t),
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t])
def test_fn(a):
with ops.name_scope('foo'):
state_ops.assign(a, a + 1)
b = a + 1
# state_ops.assign(a, b + 1)
c = b + 1
d = c + 1
return d
def testSaveRestore(self):
network = MyNetwork()
optimizer = adam.AdamOptimizer(0.001)
root_checkpointable = checkpointable_utils.Checkpoint(
optimizer=optimizer, network=network)
input_value = constant_op.constant([[3.]])
if context.in_eager_mode():
optimizer.minimize(
lambda: network(input_value))
else:
train_op = optimizer.minimize(network(input_value))
# TODO(allenl): Make initialization more pleasant when graph building.
root_checkpointable.save_counter # pylint: disable=pointless-statement
self.evaluate(checkpointable_utils.gather_initializers(
root_checkpointable))
self.evaluate(train_op)
prefix = os.path.join(self.get_temp_dir(), "ckpt")
self.evaluate(state_ops.assign(network._named_dense.variables[1], [42.]))
m_bias_slot = optimizer.get_slot(network._named_dense.variables[1], "m")
self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
save_path = root_checkpointable.save(file_prefix=prefix)
self.evaluate(state_ops.assign(network._named_dense.variables[1], [43.]))
self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3))
optimizer_variables = self.evaluate(optimizer.variables())
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
# Immediate restoration
status = root_checkpointable.restore(save_path=save_path).assert_consumed()
status.run_restore_ops()
self.assertAllEqual([42.], self.evaluate(network._named_dense.variables[1]))
self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter))
self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
if context.in_graph_mode():
return # Restore-on-create is only supported when executing eagerly
on_create_network = MyNetwork()
on_create_optimizer = adam.AdamOptimizer(0.001)
on_create_root = checkpointable_utils.Checkpoint(
optimizer=on_create_optimizer, network=on_create_network)
# Deferred restoration
status = on_create_root.restore(save_path=save_path)
on_create_network(constant_op.constant([[3.]])) # create variables
self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
self.assertAllEqual([42.],
self.evaluate(
on_create_network._named_dense.variables[1]))
on_create_m_bias_slot = on_create_optimizer.get_slot(
on_create_network._named_dense.variables[1], "m")
# Optimizer slot variables are created when the original variable is
# restored.
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
self.assertAllEqual(optimizer_variables[2:],
self.evaluate(on_create_optimizer.variables()))
on_create_optimizer._create_slots(
[resource_variable_ops.ResourceVariable([1.])])
status.assert_consumed()
beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators()
self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
def testDeferredSlotRestoration(self):
checkpoint_directory = self.get_temp_dir()
root = checkpointable.Checkpointable()
root.var = checkpointable_utils.add_variable(
root, name="var", initializer=0.)
optimizer = CheckpointableAdam(0.1)
if context.in_graph_mode():
train_op = optimizer.minimize(root.var)
self.evaluate(variables.global_variables_initializer())
self.evaluate(train_op)
else:
optimizer.minimize(root.var.read_value)
self.evaluate(state_ops.assign(root.var, 12.))
no_slots_path = checkpointable_utils.Saver(root).save(
os.path.join(checkpoint_directory, "no_slots"))
root.optimizer = optimizer
self.evaluate(state_ops.assign(root.var, 13.))
self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var),
14.))
slots_path = checkpointable_utils.Saver(root).save(
os.path.join(checkpoint_directory, "with_slots"))
new_root = checkpointable.Checkpointable()
# Load the slot-containing checkpoint (deferred), then immediately overwrite
# the non-slot variable (also deferred).
slot_status = checkpointable_utils.Saver(new_root).restore(slots_path)
no_slot_status = checkpointable_utils.Saver(new_root).restore(no_slots_path)
with self.assertRaises(AssertionError):
no_slot_status.assert_consumed()
new_root.var = checkpointable_utils.add_variable(
new_root, name="var", shape=[])
no_slot_status.assert_consumed()
no_slot_status.run_restore_ops()
self.assertEqual(12., self.evaluate(new_root.var))
new_root.optimizer = CheckpointableAdam(0.1)
with self.assertRaisesRegexp(AssertionError, "beta1_power"):
slot_status.assert_consumed()
self.assertEqual(12., self.evaluate(new_root.var))
if context.in_eager_mode():
# Slot variables are only created with restoring initializers when
# executing eagerly.
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
else:
self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var),
None)
if context.in_graph_mode():
train_op = new_root.optimizer.minimize(new_root.var)
# The slot variable now exists; restore() didn't create it, but we should
# now have a restore op for it.
slot_status.run_restore_ops()
self.assertEqual(14., self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
self.evaluate(train_op)
else:
new_root.optimizer.minimize(new_root.var.read_value)
slot_status.assert_consumed()
def test_resource_variable(self):
"""Tests that resource variable usage is allowed."""
a = variable_scope.get_variable(
name='variable_a', shape=(1), use_resource=True)
context = self.create_test_xla_compile_context()
context.Enter()
state_ops.assign(a, a + 1)
context.Exit()
def _resource_apply_sparse(self, grad, var, indices):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
rms = self.get_slot(var, "rms")
rho = self._get_hyper("rho", var_dtype)
momentum = self._get_hyper("momentum", var_dtype)
epsilon = self._get_hyper("epsilon", var_dtype)
if self._momentum:
mom = self.get_slot(var, "momentum")
if self.centered:
mg = self.get_slot(var, "mg")
return training_ops.resource_sparse_apply_centered_rms_prop(
var.handle,
mg.handle,
rms.handle,
mom.handle,
lr_t,
rho,
momentum,
epsilon,
grad,
indices,
use_locking=self._use_locking)
else:
return training_ops.resource_sparse_apply_rms_prop(
var.handle,
rms.handle,
mom.handle,
lr_t,
rho,
momentum,
epsilon,
grad,
indices,
use_locking=self._use_locking)
else:
rms_scaled_g_values = (grad * grad) * (1. - rho)
rms_t = state_ops.assign(rms, rms * rho, use_locking=self._use_locking)
with ops.control_dependencies([rms_t]):
rms_t = self._resource_scatter_add(rms, indices, rms_scaled_g_values)
rms_slice = array_ops.gather(rms_t, indices)
denom_slice = rms_slice
if self.centered:
mg = self.get_slot(var, "mg")
mg_scaled_g_values = grad * (1. - rho)
mg_t = state_ops.assign(mg, mg * rho, use_locking=self._use_locking)
with ops.control_dependencies([mg_t]):
mg_t = self._resource_scatter_add(mg, indices, mg_scaled_g_values)
mg_slice = array_ops.gather(mg_t, indices)
denom_slice = rms_slice - math_ops.square(mg_slice)
var_update = self._resource_scatter_add(
var, indices, -lr_t * grad / (math_ops.sqrt(denom_slice) + epsilon))
if self.centered:
return control_flow_ops.group(*[var_update, rms_t, mg_t])
return control_flow_ops.group(*[var_update, rms_t])
def testDuplicateTemporaryVariable(self):
with test_util.use_gpu():
var1 = gen_state_ops.temporary_variable(
[1, 2], dtypes.float32, var_name="dup")
var1 = state_ops.assign(var1, [[1.0, 2.0]])
var2 = gen_state_ops.temporary_variable(
[1, 2], dtypes.float32, var_name="dup")
var2 = state_ops.assign(var2, [[3.0, 4.0]])
final = var1 + var2
with self.assertRaises(errors.AlreadyExistsError):
self.evaluate(final)
def _cached_copy(self, var, name, pass_through=False):
"""Helper function to create a worker cached copy of a Variable.
This assigns the var (either a single Variable or a list of Variables) to
local transient cache Variable(s). Note that if var is a list of Variables,
the assignment is done sequentially to minimize the memory overheads.
Also note that if pass_through is set to True, this does not create new
Variables but simply return the input back.
Args:
var: A Variable or a list of Variables to cache.
name: name of cached Variable.
pass_through: when set to True, this simply pass through the var back
through identity operator and does not actually creates a cache.
Returns:
Tuple consisting of following three entries:
cache: the new transient Variable or list of transient Variables
corresponding one-to-one with var.
cache_init: op to initialize the Variable or the list of Variables.
cache_reset: op to reset the Variable or the list of Variables to some
default value.
"""
if var is None:
return None, None, None
elif pass_through:
cache = var
cache_init = control_flow_ops.no_op()
cache_reset = control_flow_ops.no_op()
elif isinstance(var, variables.Variable):
cache = WALSModel._transient_var(name=name)
with ops.colocate_with(cache):
cache_init = state_ops.assign(cache, var, validate_shape=False)
cache_reset = state_ops.assign(cache, 1.0, validate_shape=False)
else:
assert isinstance(var, list)
assert var
cache = [
WALSModel._transient_var(name="%s_shard_%d" % (name, i))
for i in xrange(len(var))
]
reset_ops = []
for i, c in enumerate(cache):
with ops.colocate_with(c):
if i == 0:
cache_init = state_ops.assign(c, var[i], validate_shape=False)
else:
with ops.control_dependencies([cache_init]):
cache_init = state_ops.assign(c, var[i], validate_shape=False)
reset_ops.append(state_ops.assign(c, 1.0, validate_shape=False))
cache_reset = control_flow_ops.group(*reset_ops)
return cache, cache_init, cache_reset
def create_axis_ops(sp_input, num_items, update_fn, axis_name):
"""Creates book-keeping and training ops for a given axis.
Args:
sp_input: A SparseTensor corresponding to the row or column batch.
num_items: An integer, the total number of items of this axis.
update_fn: A function that takes one argument (`sp_input`), and that
returns a tuple of
* new_factors: A flot Tensor of the factor values after update.
* update_op: a TensorFlow op which updates the factors.
* loss: A float Tensor, the unregularized loss.
* reg_loss: A float Tensor, the regularization loss.
* sum_weights: A float Tensor, the sum of factor weights.
axis_name: A string that specifies the name of the axis.
Returns:
A tuple consisting of:
* reset_processed_items_op: A TensorFlow op, to be run before the
beginning of any sweep. It marks all items as not-processed.
* axis_train_op: A Tensorflow op, to be run during this axis' sweeps.
"""
processed_items_init = array_ops.fill(dims=[num_items], value=False)
with ops.colocate_with(processed_items_init):
processed_items = variable_scope.variable(
processed_items_init,
collections=[ops.GraphKeys.GLOBAL_VARIABLES],
trainable=False,
name="processed_" + axis_name)
reset_processed_items_op = state_ops.assign(
processed_items, processed_items_init,
name="reset_processed_" + axis_name)
_, update_op, loss, reg, sum_weights = update_fn(sp_input)
input_indices = sp_input.indices[:, 0]
with ops.control_dependencies([
update_op,
state_ops.assign(loss_var, loss + reg),
state_ops.assign(rwse_var, math_ops.sqrt(loss / sum_weights))]):
with ops.colocate_with(processed_items):
update_processed_items = state_ops.scatter_update(
processed_items,
input_indices,
array_ops.ones_like(input_indices, dtype=dtypes.bool),
name="update_processed_{}_indices".format(axis_name))
with ops.control_dependencies([update_processed_items]):
is_sweep_done = math_ops.reduce_all(processed_items)
axis_train_op = control_flow_ops.group(
global_step_incr_op,
state_ops.assign(is_sweep_done_var, is_sweep_done),
state_ops.assign_add(
completed_sweeps_var,
math_ops.cast(is_sweep_done, dtypes.int32)),
name="{}_sweep_train_op".format(axis_name))
return reset_processed_items_op, axis_train_op
def test_non_resource_variable_error(self):
"""Tests that non-resource variable usage is disallowed."""
a = variable_scope.get_variable(
name='variable_a', shape=(1), use_resource=False)
context = self.create_test_xla_compile_context()
context.Enter()
with self.assertRaisesRegexp(
NotImplementedError, 'Non-resource Variables are not supported inside '
r'XLA computations \(operator name: Assign\)'):
state_ops.assign(a, a + 1)
context.Exit()
def _Update(variable, gradients, accum, linear, base_lr, lr_power, l1, l2):
"""Update "variable", "accum", "linear" based on "gradients".
Some notations here: "variable" as W, "accum" as N, "linear" as Z,
"gradients" as G, N(t) means "accum" at t-step.
Assuming lr_power = -0.5 which means using adagrad learning rate.
"accum" updates as: N = N + G^2
"linear" updates as: Z = Z + G - W * (sqrt(N(t)) - sqrt(N(t-1)))/base_lr
REQUIRES: Dimensionality of variable, gradients, accum and linear
must be same.
Args:
variable: A Variable.
gradients: A Tensor of same shape as 'variable'.
accum: A Variable containing the sum of the squares of gradients.
linear: A Variable containing approximation info.
base_lr: A constant represents base learning rate.
lr_power: A constant is used to adjust learning rate.
l1: A constant represents l1 regularization strength.
l2: A constant represents l2 regularization strength.
Returns:
A group op including three Assign ops:
1. Assign for "accum"
2. Assign for "linear"
3. Assign for "variable"
"""
dtype = variable.dtype.base_dtype
base_lr = ops.convert_to_tensor(base_lr, dtype=dtype)
lr_power = ops.convert_to_tensor(lr_power, dtype=dtype)
l1 = ops.convert_to_tensor(l1, dtype=dtype)
l2 = ops.convert_to_tensor(l2, dtype=dtype)
# Compute the new accumulator
sqr_grad = math_ops.square(gradients)
accum_updated = sqr_grad + accum
# Compute the new linear
neg_lr_power = math_ops.neg(lr_power)
sigma = math_ops.pow(accum_updated, neg_lr_power) - math_ops.pow(
accum, neg_lr_power)
sigma /= base_lr
proximal_adjust = sigma * variable
linear_updated = linear + gradients - proximal_adjust
# Compute the "variable"
variable_updated = _Compute(accum_updated, linear_updated, base_lr,
lr_power, l1, l2)
with ops.control_dependencies([sigma]):
accum_update_op = state_ops.assign(accum, accum_updated)
linear_update_op = state_ops.assign(linear, linear_updated)
variable_update_op = state_ops.assign(variable, variable_updated)
group_op = control_flow_ops.group(linear_update_op, accum_update_op,
variable_update_op)
return group_op
def setUp(self): self._num_rows = 5 self._num_cols = 7 self._train_op = control_flow_ops.no_op() self._row_prep_done = variables.Variable(False) self._col_prep_done = variables.Variable(False) self._init_done = variables.Variable(False) self._row_prep_ops = [state_ops.assign(self._row_prep_done, True)] self._col_prep_ops = [state_ops.assign(self._col_prep_done, True)] self._init_ops = [state_ops.assign(self._init_done, True)] self._input_row_indices_ph = array_ops.placeholder(dtypes.int64) self._input_col_indices_ph = array_ops.placeholder(dtypes.int64)
def series_start_updates():
# If this is the lowest-time chunk that we have seen so far, update
# series start moments to reflect that. Note that these statistics are
# "best effort", as there are race conditions in the update (however,
# they should eventually converge if the start of the series is
# presented enough times).
mean, variance = nn.moments(
values[min_time_batch, :self._starting_variance_window_size],
axes=[0])
return control_flow_ops.group(
state_ops.assign(statistics.series_start_moments.mean, mean),
state_ops.assign(statistics.series_start_moments.variance,
variance))
def _resource_apply_dense(self, grad, var):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
epsilon_t = tf.convert_to_tensor(self.epsilon, var_dtype)
local_step = math_ops.cast(self.iterations + 1, var_dtype)
beta_1_power = math_ops.pow(beta_1_t, local_step)
beta_2_power = math_ops.pow(beta_2_t, local_step)
if self._initial_total_steps > 0:
total_steps = self._get_hyper('total_steps', var_dtype)
warmup_steps = total_steps * self._get_hyper(
'warmup_proportion', var_dtype)
min_lr = self._get_hyper('min_lr', var_dtype)
decay_steps = K.maximum(total_steps - warmup_steps, 1)
decay_rate = (min_lr - lr_t) / decay_steps
lr_t = tf.where(
local_step <= warmup_steps,
lr_t * (local_step / warmup_steps),
lr_t +
decay_rate * K.minimum(local_step - warmup_steps, decay_steps),
)
sma_inf = 2.0 / (1.0 - beta_2_t) - 1.0
sma_t = sma_inf - 2.0 * local_step * beta_2_power / (1.0 -
beta_2_power)
m_t = state_ops.assign(m,
beta_1_t * m + (1.0 - beta_1_t) * grad,
use_locking=self._use_locking)
m_corr_t = m_t / (1.0 - beta_1_power)
v_t = state_ops.assign(v,
beta_2_t * v +
(1.0 - beta_2_t) * math_ops.square(grad),
use_locking=self._use_locking)
if self.amsgrad:
vhat = self.get_slot(var, 'vhat')
vhat_t = state_ops.assign(vhat,
math_ops.maximum(vhat, v_t),
use_locking=self._use_locking)
v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta_2_power))
else:
vhat_t = None
v_corr_t = math_ops.sqrt(v_t / (1.0 - beta_2_power))
r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) * (sma_t - 2.0) /
(sma_inf - 2.0) * sma_inf / sma_t)
var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t / (v_corr_t + epsilon_t),
m_corr_t)
if self._initial_weight_decay > 0.0:
var_t += self._get_hyper('weight_decay', var_dtype) * var
var_update = state_ops.assign_sub(var,
lr_t * var_t,
use_locking=self._use_locking)
updates = [var_update, m_t, v_t]
if self.amsgrad:
updates.append(vhat_t)
return control_flow_ops.group(*updates)
def update_var_v1(x): v = variables.Variable(3, name='v') update_op = state_ops.assign(v, x).op return update_op
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
rms = self.get_slot(var, "rms")
if self._momentum:
mom = self.get_slot(var, "momentum")
if self.centered:
mg = self.get_slot(var, "mg")
return training_ops.resource_sparse_apply_centered_rms_prop(
var.handle,
mg.handle,
rms.handle,
mom.handle,
coefficients["lr_t"],
coefficients["rho"],
coefficients["momentum"],
coefficients["epsilon"],
grad,
indices,
use_locking=self._use_locking)
else:
return training_ops.resource_sparse_apply_rms_prop(
var.handle,
rms.handle,
mom.handle,
coefficients["lr_t"],
coefficients["rho"],
coefficients["momentum"],
coefficients["epsilon"],
grad,
indices,
use_locking=self._use_locking)
else:
rms_scaled_g_values = (grad * grad) * coefficients["one_minus_rho"]
rms_t = state_ops.assign(rms,
rms * coefficients["rho"],
use_locking=self._use_locking)
with ops.control_dependencies([rms_t]):
rms_t = self._resource_scatter_add(rms, indices,
rms_scaled_g_values)
rms_slice = array_ops.gather(rms_t, indices)
denom_slice = rms_slice
if self.centered:
mg = self.get_slot(var, "mg")
mg_scaled_g_values = grad * coefficients["one_minus_rho"]
mg_t = state_ops.assign(mg,
mg * coefficients["rho"],
use_locking=self._use_locking)
with ops.control_dependencies([mg_t]):
mg_t = self._resource_scatter_add(mg, indices,
mg_scaled_g_values)
mg_slice = array_ops.gather(mg_t, indices)
denom_slice = rms_slice - math_ops.square(mg_slice)
var_update = self._resource_scatter_add(
var, indices, coefficients["neg_lr_t"] * grad /
(math_ops.sqrt(denom_slice) + coefficients["epsilon"]))
if self.centered:
return control_flow_ops.group(*[var_update, rms_t, mg_t])
return control_flow_ops.group(*[var_update, rms_t])
def build_controller(self):
"""RL optimization interface.
Returns:
ops: A dictionary holding handles of the model used for training.
"""
self._global_step = training_util.get_or_create_global_step()
ops = {}
ops["loss"] = 0
failing_signal = self.compute_reward(self.hparams.failing_signal)
ctr = {}
with tf_ops.name_scope("controller_{}".format(self.ctrl_id)):
with variable_scope.variable_scope("controller_{}".format(
self.ctrl_id)):
ctr["reward"] = {"value": [], "ph": [], "update": []}
ctr["ready"] = {"value": [], "ph": [], "update": []}
ctr["best_reward"] = {"value": [], "update": []}
for i in range(self.hparams.num_children):
reward_value = variable_scope.get_local_variable(
"reward_{}".format(i),
initializer=0.0,
dtype=dtypes.float32,
trainable=False)
reward_ph = array_ops.placeholder(
dtypes.float32,
shape=(),
name="reward_ph_{}".format(i))
reward_update = state_ops.assign(reward_value,
reward_ph,
use_locking=True)
ctr["reward"]["value"].append(reward_value)
ctr["reward"]["ph"].append(reward_ph)
ctr["reward"]["update"].append(reward_update)
best_reward = variable_scope.get_local_variable(
"best_reward_{}".format(i),
initializer=failing_signal,
dtype=dtypes.float32,
trainable=False)
ctr["best_reward"]["value"].append(best_reward)
ctr["best_reward"]["update"].append(
state_ops.assign(
best_reward,
math_ops.minimum(best_reward, reward_update)))
ready_value = variable_scope.get_local_variable(
"ready_{}".format(i),
initializer=True,
dtype=dtypes.bool,
trainable=False)
ready_ph = array_ops.placeholder(
dtypes.bool, shape=(), name="ready_ph_{}".format(i))
ready_update = state_ops.assign(ready_value,
ready_ph,
use_locking=True)
ctr["ready"]["value"].append(ready_value)
ctr["ready"]["ph"].append(ready_ph)
ctr["ready"]["update"].append(ready_update)
ctr["grouping_y_preds"], ctr[
"grouping_log_probs"] = self.get_groupings()
summary.histogram(
"grouping_actions",
array_ops.slice(ctr["grouping_y_preds"]["sample"], [0, 0],
[1, array_ops.shape(self.op_embeddings)[0]]))
with variable_scope.variable_scope("controller_{}".format(
self.ctrl_id)):
ctr["baseline"] = variable_scope.get_local_variable(
"baseline",
initializer=failing_signal
if self.hparams.start_with_failing_signal else 0.0,
dtype=dtypes.float32,
trainable=False)
new_baseline = self.hparams.bl_dec * ctr["baseline"] + (
1 - self.hparams.bl_dec) * math_ops.reduce_mean(
ctr["reward"]["value"])
if not self.hparams.always_update_baseline:
baseline_mask = math_ops.less(ctr["reward"]["value"],
failing_signal)
selected_reward = array_ops.boolean_mask(
ctr["reward"]["value"], baseline_mask)
selected_baseline = control_flow_ops.cond(
math_ops.reduce_any(baseline_mask),
lambda: math_ops.reduce_mean(selected_reward),
lambda: constant_op.constant(0, dtype=dtypes.float32))
ctr["pos_reward"] = selected_baseline
pos_ = math_ops.less(
constant_op.constant(0, dtype=dtypes.float32),
selected_baseline)
selected_baseline = self.hparams.bl_dec * ctr["baseline"] + (
1 - self.hparams.bl_dec) * selected_baseline
selected_baseline = control_flow_ops.cond(
pos_, lambda: selected_baseline, lambda: ctr["baseline"])
new_baseline = control_flow_ops.cond(
math_ops.less(self.global_step,
self.hparams.stop_updating_after_steps),
lambda: new_baseline, lambda: selected_baseline)
ctr["baseline_update"] = state_ops.assign(ctr["baseline"],
new_baseline,
use_locking=True)
ctr["y_preds"], ctr["log_probs"] = self.get_placements()
summary.histogram("actions", ctr["y_preds"]["sample"])
mask = math_ops.less(ctr["reward"]["value"], failing_signal)
ctr["loss"] = ctr["reward"]["value"] - ctr["baseline"]
ctr["loss"] *= (ctr["log_probs"]["sample"] +
ctr["grouping_log_probs"]["sample"])
selected_loss = array_ops.boolean_mask(ctr["loss"], mask)
selected_loss = control_flow_ops.cond(
math_ops.reduce_any(mask),
lambda: math_ops.reduce_mean(-selected_loss),
lambda: constant_op.constant(0, dtype=dtypes.float32))
ctr["loss"] = control_flow_ops.cond(
math_ops.less(self.global_step,
self.hparams.stop_updating_after_steps),
lambda: math_ops.reduce_mean(-ctr["loss"]),
lambda: selected_loss)
ctr["reward_s"] = math_ops.reduce_mean(ctr["reward"]["value"])
summary.scalar("loss", ctr["loss"])
summary.scalar("avg_reward", ctr["reward_s"])
summary.scalar("best_reward_so_far", best_reward)
summary.scalar(
"advantage",
math_ops.reduce_mean(ctr["reward"]["value"] - ctr["baseline"]))
with variable_scope.variable_scope("optimizer",
reuse=variable_scope.AUTO_REUSE):
(ctr["train_op"], ctr["lr"], ctr["grad_norm"],
ctr["grad_norms"]) = self._get_train_ops(
ctr["loss"],
tf_ops.get_collection(tf_ops.GraphKeys.TRAINABLE_VARIABLES),
self.global_step,
grad_bound=self.hparams.grad_bound,
lr_init=self.hparams.lr,
lr_dec=self.hparams.lr_dec,
start_decay_step=self.hparams.start_decay_step,
decay_steps=self.hparams.decay_steps,
optimizer_type=self.hparams.optimizer_type)
summary.scalar("gradnorm", ctr["grad_norm"])
summary.scalar("lr", ctr["lr"])
ctr["summary"] = summary.merge_all()
ops["controller"] = ctr
self.ops = ops
return ops
def _assign_new_value(self, variable, value):
with K.name_scope('AssignNewValue') as scope:
with ops.colocate_with(variable):
return state_ops.assign(variable, value, name=scope)
def LastValueQuantize(inputs,
per_channel=False,
init_min=-6.0,
init_max=6.0,
vars_collection=None,
name_prefix='LastValueQuant',
reuse=None,
is_training=True,
num_bits=8,
narrow_range=False,
symmetric=False):
"""Adds a layer that collects quantization ranges as last input ranges.
LastValueQuantize creates variables called 'min' and 'max', representing the
interval used for quantization and clamping.
Args:
inputs: a tensor containing values to be quantized.
per_channel: (Optional) a boolean specifying whether to use different
quantization ranges per output channel.
init_min: a float scalar, the initial value for variable min.
init_max: a float scalar, the initial value for variable max.
vars_collection: (Optional) collection where to store variables for
quantization interval ends.
name_prefix: name_prefix for created nodes.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
is_training: Whether the op is applied to a training or eval graph.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
symmetric: If true, use symmetric quantization limits instead of training
the minimum and maximum of each quantization range separately.
Returns:
a tensor containing quantized values.
"""
with variable_scope.variable_scope(
None, default_name=name_prefix, values=[inputs], reuse=reuse) as scope:
scope.set_partitioner(None)
input_shape = inputs.get_shape()
input_dim = len(input_shape)
if per_channel:
# Only support quantizing 1-, 2- and 4-dimensional tensors.
assert input_dim in [1, 2, 4], ('Expected 1D, 2D or 4D input, was: %s in '
' scope: %s' % (input_shape, name_prefix))
min_max_shape = [input_shape[-1]]
else:
min_max_shape = []
vars_collections = [vars_collection] if vars_collection else []
min_var = _ModelVariable(
'min',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_min),
collections=vars_collections,
trainable=False)
max_var = _ModelVariable(
'max',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_max),
collections=vars_collections,
trainable=False)
if not is_training:
return _FakeQuantWithMinMaxVars(
inputs,
min_var,
max_var,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
if per_channel:
if input_dim == 2:
reduce_dims = [0]
elif input_dim == 4:
reduce_dims = [0, 1, 2]
if per_channel:
if input_dim >= 2:
batch_min = math_ops.reduce_min(
inputs, axis=reduce_dims, name='BatchMin')
else:
batch_min = inputs
else:
batch_min = math_ops.reduce_min(inputs, name='BatchMin')
if per_channel:
if input_dim >= 2:
batch_max = math_ops.reduce_max(
inputs, axis=reduce_dims, name='BatchMax')
else:
batch_max = inputs
else:
batch_max = math_ops.reduce_max(inputs, name='BatchMax')
if symmetric:
if narrow_range:
min_max_ratio = -1
else:
# In two's complement notation, the negative range is slightly larger
# than the positive range.
min_max_ratio = -((1 << num_bits) - 2) / (1 << num_bits)
# TFLite requires that 0.0 if always in the [min; max] range. Because
# batch_min <= batch_max, it follows that range_min <= 0 <= range_max.
range_min = math_ops.minimum(batch_min, batch_max / min_max_ratio)
range_max = math_ops.maximum(batch_max, batch_min * min_max_ratio)
else:
# TFLite requires that 0.0 if always in the [min; max] range.
range_min = math_ops.minimum(batch_min, 0.0)
range_max = math_ops.maximum(batch_max, 0.0)
assign_min = state_ops.assign(min_var, range_min, name='AssignMinLast')
assign_max = state_ops.assign(max_var, range_max, name='AssignMaxLast')
return _FakeQuantWithMinMaxVars(
inputs,
assign_min,
assign_max,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
def _warm_start_var_with_vocab(var,
current_vocab_path,
current_vocab_size,
prev_ckpt,
prev_vocab_path,
previous_vocab_size=-1,
current_oov_buckets=0,
prev_tensor_name=None,
initializer=None):
"""Warm-starts given variable from `prev_tensor_name` tensor in `prev_ckpt`.
Use this method when the `var` is backed by vocabulary. This method stitches
the given `var` such that values corresponding to individual features in the
vocabulary remain consistent irrespective of changing order of the features
between old and new vocabularies.
Args:
var: Current graph's variable that needs to be warm-started (initialized).
Can be either of the following:
(i) `Variable`
(ii) `ResourceVariable`
(iii) list of `Variable`: The list must contain slices of the same larger
variable.
(iv) `PartitionedVariable`
current_vocab_path: Path to the vocab file used for the given `var`.
current_vocab_size: An `int` specifying the number of entries in the current
vocab.
prev_ckpt: A string specifying the directory with checkpoint file(s) or path
to checkpoint. The given checkpoint must have tensor with name
`prev_tensor_name` (if not None) or tensor with name same as given `var`.
prev_vocab_path: Path to the vocab file used for the tensor in `prev_ckpt`.
previous_vocab_size: If provided, will constrain previous vocab to the first
`previous_vocab_size` entries. -1 means use the entire previous vocab.
current_oov_buckets: An `int` specifying the number of out-of-vocabulary
buckets used for given `var`.
prev_tensor_name: Name of the tensor to lookup in provided `prev_ckpt`. If
None, we lookup tensor with same name as given `var`.
initializer: Variable initializer to be used for missing entries. If None,
missing entries will be zero-initialized.
Raises:
ValueError: If required args are not provided.
"""
if not (current_vocab_path and current_vocab_size and prev_ckpt and
prev_vocab_path):
raise ValueError("Invalid args: Must provide all of [current_vocab_path, "
"current_vocab_size, prev_ckpt, prev_vocab_path}.")
if _is_variable(var):
var = [var]
elif isinstance(var, list) and all(_is_variable(v) for v in var):
var = var
elif isinstance(var, variables_lib.PartitionedVariable):
var = var._get_variable_list()
else:
raise TypeError(
"var MUST be one of the following: a Variable, list of Variable or "
"PartitionedVariable, but is {}".format(type(var)))
if not prev_tensor_name:
# Assume tensor name remains the same.
prev_tensor_name = _infer_var_name(var)
for v in var:
v_shape = v.get_shape().as_list()
slice_info = v._get_save_slice_info()
partition_info = None
if slice_info:
partition_info = variable_scope._PartitionInfo(
full_shape=slice_info.full_shape,
var_offset=slice_info.var_offset)
# TODO(eddz): Support WarmStartSettings where class vocabularies need
# remapping too.
init = checkpoint_ops._load_and_remap_matrix_initializer(
ckpt_path=checkpoint_utils._get_checkpoint_filename(prev_ckpt),
old_tensor_name=prev_tensor_name,
new_row_vocab_size=current_vocab_size,
new_col_vocab_size=v_shape[1],
old_row_vocab_size=previous_vocab_size,
old_row_vocab_file=prev_vocab_path,
new_row_vocab_file=current_vocab_path,
old_col_vocab_file=None,
new_col_vocab_file=None,
num_row_oov_buckets=current_oov_buckets,
num_col_oov_buckets=0,
initializer=initializer)
new_init_val = ops.convert_to_tensor(
init(shape=v_shape, partition_info=partition_info))
v._initializer_op = state_ops.assign(v, new_init_val)
def streaming_tp_fp_arrays(self,
num_gbboxes,
tp,
fp,
scores,
remove_zero_scores=True,
metrics_collections=None,
updates_collections=None,
name=None):
# Input dictionaries: dict outputs as streaming metrics.
if isinstance(scores, dict) or isinstance(fp, dict):
d_values = {}
d_update_ops = {}
for c in num_gbboxes.keys():
scope = 'streaming_tp_fp_%s' % c
v, up = self.streaming_tp_fp_arrays(num_gbboxes[c],
tp[c],
fp[c],
scores[c],
remove_zero_scores,
metrics_collections,
updates_collections,
name=scope)
d_values[c] = v
d_update_ops[c] = up
return d_values, d_update_ops
# Input Tensors...
with variable_scope.variable_scope(name, 'streaming_tp_fp',
[num_gbboxes, tp, fp, scores]):
num_gbboxes = math_ops.to_int64(num_gbboxes)
scores = math_ops.to_float(scores)
stype = tf.bool
tp = tf.cast(tp, stype)
fp = tf.cast(fp, stype)
# Reshape TP and FP tensors and clean away 0 class values.
scores = tf.reshape(scores, [-1])
tp = tf.reshape(tp, [-1])
fp = tf.reshape(fp, [-1])
# Remove TP and FP both false.
mask = tf.logical_or(tp, fp)
if remove_zero_scores:
rm_threshold = 1e-4
mask = tf.logical_and(mask, tf.greater(scores, rm_threshold))
scores = tf.boolean_mask(scores, mask)
tp = tf.boolean_mask(tp, mask)
fp = tf.boolean_mask(fp, mask)
# Local variables accumlating information over batches.
v_nobjects = self._create_local('v_num_gbboxes',
shape=[],
dtype=tf.int64)
v_ndetections = self._create_local('v_num_detections',
shape=[],
dtype=tf.int32)
v_scores = self._create_local('v_scores', shape=[
0,
])
v_tp = self._create_local('v_tp', shape=[
0,
], dtype=stype)
v_fp = self._create_local('v_fp', shape=[
0,
], dtype=stype)
# Update operations.
nobjects_op = state_ops.assign_add(v_nobjects,
tf.reduce_sum(num_gbboxes))
ndetections_op = state_ops.assign_add(
v_ndetections, tf.size(scores, out_type=tf.int32))
scores_op = state_ops.assign(v_scores,
tf.concat([v_scores, scores], axis=0),
validate_shape=False)
tp_op = state_ops.assign(v_tp,
tf.concat([v_tp, tp], axis=0),
validate_shape=False)
fp_op = state_ops.assign(v_fp,
tf.concat([v_fp, fp], axis=0),
validate_shape=False)
# Value and update ops.
val = (v_nobjects, v_ndetections, v_tp, v_fp, v_scores)
with ops.control_dependencies(
[nobjects_op, ndetections_op, scores_op, tp_op, fp_op]):
update_op = (nobjects_op, ndetections_op, tp_op, fp_op,
scores_op)
if metrics_collections:
ops.add_to_collections(metrics_collections, val)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return val, update_op
def call(self, inputs, mask=None, training=None, initial_state=None):
# The input should be dense, padded with zeros. If a ragged input is fed
# into the layer, it is padded and the row lengths are used for masking.
inputs, row_lengths = K.convert_inputs_if_ragged(inputs)
is_ragged_input = (row_lengths is not None)
self._validate_args_if_ragged(is_ragged_input, mask)
# LSTM does not support constants. Ignore it during process.
inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
if isinstance(mask, list):
mask = mask[0]
input_shape = K.int_shape(inputs)
timesteps = input_shape[0] if self.time_major else input_shape[1]
if not self._could_use_gpu_kernel:
# Fall back to use the normal LSTM.
kwargs = {'training': training}
self._maybe_reset_cell_dropout_mask(self.cell)
def step(inputs, states):
return self.cell(inputs, states, **kwargs)
last_output, outputs, states = K.rnn(
step,
inputs,
initial_state,
constants=None,
go_backwards=self.go_backwards,
mask=mask,
unroll=self.unroll,
input_length=row_lengths if row_lengths is not None else timesteps,
time_major=self.time_major,
zero_output_for_mask=self.zero_output_for_mask)
runtime = _runtime(_RUNTIME_UNKNOWN)
else:
# Use the new defun approach for backend implementation swap.
# Note that different implementations need to have same function
# signature, eg, the tensor parameters need to have same shape and dtypes.
# Since the CuDNN has an extra set of bias, those bias will be passed to
# both normal and CuDNN implementations.
self.reset_dropout_mask()
dropout_mask = self.get_dropout_mask_for_cell(inputs, training, count=4)
if dropout_mask is not None:
inputs = inputs * dropout_mask[0]
gpu_lstm_kwargs = {
'inputs': inputs,
'init_h': initial_state[0],
'init_c': initial_state[1],
'kernel': self.cell.kernel,
'recurrent_kernel': self.cell.recurrent_kernel,
'bias': self.cell.bias,
'mask': mask,
'time_major': self.time_major,
'go_backwards': self.go_backwards,
'sequence_lengths': row_lengths
}
normal_lstm_kwargs = gpu_lstm_kwargs.copy()
normal_lstm_kwargs.update({
'activation': self.activation,
'recurrent_activation': self.recurrent_activation,
'zero_output_for_mask': self.zero_output_for_mask,
})
if context.executing_eagerly():
device_type = _get_context_device_type()
can_use_gpu = (
# Either user specified GPU or unspecified but GPU is available.
(device_type == _GPU_DEVICE_NAME
or (device_type is None and context.num_gpus() > 0))
and
(mask is None or is_sequence_right_padded(mask, self.time_major)))
# Under eager context, check the device placement and prefer the
# GPU implementation when GPU is available.
if can_use_gpu:
last_output, outputs, new_h, new_c, runtime = gpu_lstm(
**gpu_lstm_kwargs)
else:
last_output, outputs, new_h, new_c, runtime = standard_lstm(
**normal_lstm_kwargs)
else:
(last_output, outputs, new_h, new_c,
runtime) = lstm_with_backend_selection(**normal_lstm_kwargs)
states = [new_h, new_c]
if self.stateful:
updates = []
for i in range(len(states)):
updates.append(state_ops.assign(self.states[i], states[i]))
self.add_update(updates)
if self.return_sequences:
output = K.maybe_convert_to_ragged(is_ragged_input, outputs, row_lengths)
else:
output = last_output
if self.return_state:
return [output] + list(states)
elif self.return_runtime:
return output, runtime
else:
return output
def _apply_sparse_shared(self, grad, var, indices, scatter_add):
step, beta1_power, beta2_power = self._get_beta_accumulators()
beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
if self._initial_total_steps > 0:
total_steps = math_ops.cast(self._total_steps_t, var.dtype.base_dtype)
warmup_proportion = math_ops.cast(self._warmup_proportion_t, var.dtype.base_dtype)
min_lr = math_ops.cast(self._min_lr_t, var.dtype.base_dtype)
warmup_steps = total_steps * warmup_proportion
decay_steps = math_ops.maximum(total_steps - warmup_steps, 1)
decay_rate = (min_lr - lr_t) / decay_steps
lr_t = tf.where(
step <= warmup_steps,
lr_t * (step / warmup_steps),
lr_t + decay_rate * math_ops.minimum(step - warmup_steps, decay_steps),
)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
sma_inf = 2.0 / (1.0 - beta2_t) - 1.0
sma_t = sma_inf - 2.0 * step * beta2_power / (1.0 - beta2_power)
m = self.get_slot(var, "m")
m_scaled_g_values = grad * (1 - beta1_t)
m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = scatter_add(m, indices, m_scaled_g_values)
m_corr_t = m_t / (1.0 - beta1_power)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * (1 - beta2_t)
v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = scatter_add(v, indices, v_scaled_g_values)
if self._amsgrad:
vhat = self.get_slot(var, 'vhat')
vhat_t = state_ops.assign(vhat, math_ops.maximum(vhat, v_t), use_locking=self._use_locking)
v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta2_power))
else:
v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power))
r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) *
(sma_t - 2.0) / (sma_inf - 2.0) *
sma_inf / sma_t)
var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t / (v_corr_t + epsilon_t), m_corr_t)
if self._initial_weight_decay > 0.0:
var_t += math_ops.cast(self._weight_decay_t, var.dtype.base_dtype) * var
var_t = lr_t * var_t
var_update = state_ops.scatter_sub(
var,
indices,
array_ops.gather(var_t, indices),
use_locking=self._use_locking)
updates = [var_update, m_t, v_t]
if self._amsgrad:
updates.append(vhat_t)
return control_flow_ops.group(*updates)
def _testSaveRestoreOutput(self, rnn_mode, direction, dtype):
with ops.Graph().as_default():
num_layers = 2
num_units = 7
input_size = 7
seq_length = 10
batch_size = 5
dir_count = 1 if direction == cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION else 2
model = _CreateModel(rnn_mode,
num_layers,
num_units,
input_size,
direction=direction,
dtype=dtype)
params_size_t = model.params_size()
params = variables.VariableV1(array_ops.ones([params_size_t],
dtype=dtype),
validate_shape=False,
dtype=dtype)
_CreateParamsSavable(params, model)
save_path = os.path.join(self.get_temp_dir(),
"save-restore-output-test")
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
np.random.seed(1234)
has_input_c = (rnn_mode == cudnn_rnn_ops.CUDNN_LSTM)
input_data = constant_op.constant(np.random.randn(
seq_length, batch_size, input_size),
dtype=dtype)
input_h = constant_op.constant(np.random.randn(
num_layers * dir_count, batch_size, num_units),
dtype=dtype)
if has_input_c:
input_c = constant_op.constant(np.random.randn(
num_layers * dir_count, batch_size, num_units),
dtype=dtype)
outputs = model(input_data=input_data,
input_h=input_h,
input_c=input_c,
params=params,
is_training=False)
else:
outputs = model(input_data=input_data,
input_h=input_h,
params=params,
is_training=False)
total_sum = sum(map(math_ops.reduce_sum, outputs))
# Passing graph explicitly, otherwise an old sess would be reused.
with self.test_session(use_gpu=True,
graph=ops.get_default_graph()) as sess:
sess.run(variables.global_variables_initializer())
total_sum_v = sess.run(total_sum)
val = saver.save(sess, save_path)
self.assertEqual(save_path, val)
# Passing graph explicitly, otherwise an old sess would be reused.
with self.test_session(use_gpu=True,
graph=ops.get_default_graph()) as sess:
reset_params = state_ops.assign(params,
array_ops.zeros(
[params_size_t],
dtype=dtype),
validate_shape=False)
sess.run(reset_params)
saver.restore(sess, save_path)
total_sum_v_restored = sess.run(total_sum)
self.assertAllClose(total_sum_v,
total_sum_v_restored,
atol=1e-5)
def _wals_factorization_model_function(features, labels, mode, params):
"""Model function for the WALSFactorization estimator.
Args:
features: Dictionary of features. See WALSMatrixFactorization.
labels: Must be None.
mode: A model_fn.ModeKeys object.
params: Dictionary of parameters containing arguments passed to the
WALSMatrixFactorization constructor.
Returns:
A ModelFnOps object.
Raises:
ValueError: If `mode` is not recognized.
"""
assert labels is None
use_factors_weights_cache = (params["use_factors_weights_cache_for_training"]
and mode == model_fn.ModeKeys.TRAIN)
use_gramian_cache = (params["use_gramian_cache_for_training"] and
mode == model_fn.ModeKeys.TRAIN)
max_sweeps = params["max_sweeps"]
model = factorization_ops.WALSModel(
params["num_rows"],
params["num_cols"],
params["embedding_dimension"],
unobserved_weight=params["unobserved_weight"],
regularization=params["regularization_coeff"],
row_init=params["row_init"],
col_init=params["col_init"],
num_row_shards=params["num_row_shards"],
num_col_shards=params["num_col_shards"],
row_weights=params["row_weights"],
col_weights=params["col_weights"],
use_factors_weights_cache=use_factors_weights_cache,
use_gramian_cache=use_gramian_cache)
# Get input rows and cols. We either update rows or columns depending on
# the value of row_sweep, which is maintained using a session hook.
input_rows = features[WALSMatrixFactorization.INPUT_ROWS]
input_cols = features[WALSMatrixFactorization.INPUT_COLS]
# TRAIN mode:
if mode == model_fn.ModeKeys.TRAIN:
# Training consists of the following ops (controlled using a SweepHook).
# Before a row sweep:
# row_update_prep_gramian_op
# initialize_row_update_op
# During a row sweep:
# update_row_factors_op
# Before a col sweep:
# col_update_prep_gramian_op
# initialize_col_update_op
# During a col sweep:
# update_col_factors_op
is_row_sweep_var = variable_scope.variable(
True,
trainable=False,
name="is_row_sweep",
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
is_sweep_done_var = variable_scope.variable(
False,
trainable=False,
name="is_sweep_done",
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
completed_sweeps_var = variable_scope.variable(
0,
trainable=False,
name=WALSMatrixFactorization.COMPLETED_SWEEPS,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
loss_var = variable_scope.variable(
0.,
trainable=False,
name=WALSMatrixFactorization.LOSS,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
# The root weighted squared error =
# \\(\sqrt( \sum_{i,j} w_ij * (a_ij - r_ij)^2 / \sum_{i,j} w_ij )\\)
rwse_var = variable_scope.variable(
0.,
trainable=False,
name=WALSMatrixFactorization.RWSE,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
summary.scalar("loss", loss_var)
summary.scalar("root_weighted_squared_error", rwse_var)
summary.scalar("completed_sweeps", completed_sweeps_var)
def create_axis_ops(sp_input, num_items, update_fn, axis_name):
"""Creates book-keeping and training ops for a given axis.
Args:
sp_input: A SparseTensor corresponding to the row or column batch.
num_items: An integer, the total number of items of this axis.
update_fn: A function that takes one argument (`sp_input`), and that
returns a tuple of
* new_factors: A float Tensor of the factor values after update.
* update_op: a TensorFlow op which updates the factors.
* loss: A float Tensor, the unregularized loss.
* reg_loss: A float Tensor, the regularization loss.
* sum_weights: A float Tensor, the sum of factor weights.
axis_name: A string that specifies the name of the axis.
Returns:
A tuple consisting of:
* reset_processed_items_op: A TensorFlow op, to be run before the
beginning of any sweep. It marks all items as not-processed.
* axis_train_op: A Tensorflow op, to be run during this axis' sweeps.
"""
processed_items_init = array_ops.fill(dims=[num_items], value=False)
with ops.colocate_with(processed_items_init):
processed_items = variable_scope.variable(
processed_items_init,
collections=[ops.GraphKeys.GLOBAL_VARIABLES],
trainable=False,
name="processed_" + axis_name)
_, update_op, loss, reg, sum_weights = update_fn(sp_input)
input_indices = sp_input.indices[:, 0]
with ops.control_dependencies([
update_op,
state_ops.assign(loss_var, loss + reg),
state_ops.assign(rwse_var, math_ops.sqrt(loss / sum_weights))]):
with ops.colocate_with(processed_items):
update_processed_items = state_ops.scatter_update(
processed_items,
input_indices,
array_ops.ones_like(input_indices, dtype=dtypes.bool),
name="update_processed_{}_indices".format(axis_name))
with ops.control_dependencies([update_processed_items]):
is_sweep_done = math_ops.reduce_all(processed_items)
axis_train_op = control_flow_ops.group(
state_ops.assign(is_sweep_done_var, is_sweep_done),
state_ops.assign_add(
completed_sweeps_var,
math_ops.cast(is_sweep_done, dtypes.int32)),
name="{}_sweep_train_op".format(axis_name))
return processed_items.initializer, axis_train_op
reset_processed_rows_op, row_train_op = create_axis_ops(
input_rows,
params["num_rows"],
lambda x: model.update_row_factors(sp_input=x, transpose_input=False),
"rows")
reset_processed_cols_op, col_train_op = create_axis_ops(
input_cols,
params["num_cols"],
lambda x: model.update_col_factors(sp_input=x, transpose_input=True),
"cols")
switch_op = control_flow_ops.group(
state_ops.assign(
is_row_sweep_var, math_ops.logical_not(is_row_sweep_var)),
reset_processed_rows_op,
reset_processed_cols_op,
name="sweep_switch_op")
row_prep_ops = [
model.row_update_prep_gramian_op, model.initialize_row_update_op]
col_prep_ops = [
model.col_update_prep_gramian_op, model.initialize_col_update_op]
init_op = model.worker_init
sweep_hook = _SweepHook(
is_row_sweep_var, is_sweep_done_var, init_op,
row_prep_ops, col_prep_ops, row_train_op, col_train_op, switch_op)
global_step_hook = _IncrementGlobalStepHook()
training_hooks = [sweep_hook, global_step_hook]
if max_sweeps is not None:
training_hooks.append(_StopAtSweepHook(max_sweeps))
return model_fn.ModelFnOps(
mode=model_fn.ModeKeys.TRAIN,
predictions={},
loss=loss_var,
eval_metric_ops={},
train_op=control_flow_ops.no_op(),
training_hooks=training_hooks)
# INFER mode
elif mode == model_fn.ModeKeys.INFER:
projection_weights = features.get(
WALSMatrixFactorization.PROJECTION_WEIGHTS)
def get_row_projection():
return model.project_row_factors(
sp_input=input_rows,
projection_weights=projection_weights,
transpose_input=False)
def get_col_projection():
return model.project_col_factors(
sp_input=input_cols,
projection_weights=projection_weights,
transpose_input=True)
predictions = {
WALSMatrixFactorization.PROJECTION_RESULT: control_flow_ops.cond(
features[WALSMatrixFactorization.PROJECT_ROW],
get_row_projection,
get_col_projection)
}
return model_fn.ModelFnOps(
mode=model_fn.ModeKeys.INFER,
predictions=predictions,
loss=None,
eval_metric_ops={},
train_op=control_flow_ops.no_op(),
training_hooks=[])
# EVAL mode
elif mode == model_fn.ModeKeys.EVAL:
def get_row_loss():
_, _, loss, reg, _ = model.update_row_factors(
sp_input=input_rows, transpose_input=False)
return loss + reg
def get_col_loss():
_, _, loss, reg, _ = model.update_col_factors(
sp_input=input_cols, transpose_input=True)
return loss + reg
loss = control_flow_ops.cond(
features[WALSMatrixFactorization.PROJECT_ROW],
get_row_loss,
get_col_loss)
return model_fn.ModelFnOps(
mode=model_fn.ModeKeys.EVAL,
predictions={},
loss=loss,
eval_metric_ops={},
train_op=control_flow_ops.no_op(),
training_hooks=[])
else:
raise ValueError("mode=%s is not recognized." % str(mode))
def _init_from_args(self,
initial_value=None,
trainable=True,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
dtype=None,
expected_shape=None):
"""Creates a new variable from arguments.
Args:
initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
which is the initial value for the Variable. The initial value must have
a shape specified unless `validate_shape` is set to False. Can also be a
callable with no argument that returns the initial value when called.
(Note that initializer functions from init_ops.py must first be bound
to a shape before being used here.)
trainable: If `True`, the default, also adds the variable to the graph
collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
the default list of variables to use by the `Optimizer` classes.
collections: List of graph collections keys. The new variable is added to
these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
validate_shape: If `False`, allows the variable to be initialized with a
value of unknown shape. If `True`, the default, the shape of
`initial_value` must be known.
caching_device: Optional device string or function describing where the
Variable should be cached for reading. Defaults to the Variable's
device. If not `None`, caches on another device. Typical use is to
cache on the device where the Ops using the Variable reside, to
deduplicate copying through `Switch` and other conditional statements.
name: Optional name for the variable. Defaults to `'Variable'` and gets
uniquified automatically.
dtype: If set, initial_value will be converted to the given type.
If None, either the datatype will be kept (if initial_value is
a Tensor) or float32 will be used (if it is a Python object convertible
to a Tensor).
expected_shape: Deprecated. Ignored.
Raises:
ValueError: If the initial value is not specified, or does not have a
shape and `validate_shape` is `True`.
"""
_ = expected_shape
if initial_value is None:
raise ValueError("initial_value must be specified.")
init_from_fn = callable(initial_value)
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
if not isinstance(collections, (list, tuple, set)):
raise ValueError(
"collections argument to Variable constructor must be a list, tuple, "
"or set. Got %s of type %s" % (collections, type(collections)))
if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
with ops.control_dependencies(None):
with ops.name_scope(name, "Variable", [] if init_from_fn else
[initial_value]) as name:
if init_from_fn:
# Use attr_scope and device(None) to simulate the behavior of
# colocate_with when the variable we want to colocate with doesn't
# yet exist.
true_name = ops._name_from_scope_name(name)
attr = attr_value_pb2.AttrValue(
list=attr_value_pb2.AttrValue.ListValue(
s=[compat.as_bytes("loc:@%s" % true_name)]))
# pylint: disable=protected-access
with ops.get_default_graph()._attr_scope({"_class": attr}):
with ops.name_scope("Initializer"), ops.device(None):
self._initial_value = ops.convert_to_tensor(
initial_value(), name="initial_value", dtype=dtype)
shape = (self._initial_value.get_shape()
if validate_shape else tensor_shape.unknown_shape())
self._variable = state_ops.variable_op_v2(
shape,
self._initial_value.dtype.base_dtype,
name=name)
# Or get the initial value from a Tensor or Python object.
else:
self._initial_value = ops.convert_to_tensor(
initial_value, name="initial_value", dtype=dtype)
shape = (self._initial_value.get_shape()
if validate_shape else tensor_shape.unknown_shape())
# In this case, the variable op can't be created until after the
# initial_value has been converted to a Tensor with a known type.
self._variable = state_ops.variable_op_v2(
shape,
self._initial_value.dtype.base_dtype,
name=name)
# Manually overrides the variable's shape with the initial value's.
if validate_shape:
initial_value_shape = self._initial_value.get_shape()
if not initial_value_shape.is_fully_defined():
raise ValueError("initial_value must have a shape specified: %s" %
self._initial_value)
# Assigns initial value.
self._initializer_op = state_ops.assign(
self._variable, self._initial_value,
validate_shape=validate_shape).op
# TODO(vrv): Change this class to not take caching_device, but
# to take the op to colocate the snapshot with, so we can use
# colocation rather than devices.
if caching_device is not None:
with ops.device(caching_device):
self._snapshot = array_ops.identity(self._variable, name="read")
else:
with ops.colocate_with(self._variable.op):
self._snapshot = array_ops.identity(self._variable, name="read")
ops.add_to_collections(collections, self)
self._caching_device = caching_device
self._save_slice_info = None
def _resource_apply_dense(self, grad, var):
var_dtype = var.dtype.base_dtype
lr_t = array_ops.identity(self._get_hyper('learning_rate', var_dtype))
beta_1_t = array_ops.identity(self._get_hyper('beta_1', var_dtype))
beta_2_t = array_ops.identity(self._get_hyper('beta_2', var_dtype))
epsilon_t = ops.convert_to_tensor(self.epsilon, var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
local_step = math_ops.cast(self.iterations + 1, var_dtype)
next_step = math_ops.cast(self.iterations + 2, var_dtype)
decay_base = math_ops.cast(0.96, var_dtype)
# Learning rate multipliers
if self.lr_multipliers is not None:
lr_t = _apply_lr_multiplier(self, lr_t, var)
# Due to the recommendations in [2], i.e. warming momentum schedule
momentum_cache_t = beta_1_t * (
1. - 0.5 *
(math_ops.pow(decay_base, self._initial_decay * local_step)))
momentum_cache_t_1 = beta_1_t * (
1. - 0.5 *
(math_ops.pow(decay_base, self._initial_decay * next_step)))
m_schedule_new = math_ops.cast(self._m_cache_read,
var_dtype) * momentum_cache_t
if var_dtype is self._m_cache.dtype:
m_schedule_new = array_ops.identity(
state_ops.assign(self._m_cache,
m_schedule_new,
use_locking=self._use_locking))
m_schedule_next = m_schedule_new * momentum_cache_t_1
# the following equations given in [1]
g_prime = grad / (1. - m_schedule_new)
m_t = beta_1_t * m + (1. - beta_1_t) * grad
m_t_prime = m_t / (1. - m_schedule_next)
v_t = beta_2_t * v + (1. - beta_2_t) * math_ops.square(grad)
v_t_prime = v_t / (1. - math_ops.pow(beta_2_t, local_step))
m_t_bar = (1. - momentum_cache_t) * g_prime + (momentum_cache_t *
m_t_prime)
m_t = state_ops.assign(m, m_t, use_locking=self._use_locking)
v_t = state_ops.assign(v, v_t, use_locking=self._use_locking)
var_t = math_ops.sub(
var, self.eta_t * lr_t * m_t_bar /
(math_ops.sqrt(v_t_prime + epsilon_t)))
# Weight decays
if var.name in self.weight_decays.keys():
var_t = _apply_weight_decays(self, var, var_t)
var_update = state_ops.assign(var,
var_t,
use_locking=self._use_locking)
# Cosine annealing
(iteration_done, t_cur_update,
eta_t_update) = _update_t_cur_eta_t_v2(self, lr_t, var)
if iteration_done and not self._init_notified:
self._init_notified = True
updates = [var_update, m_t, v_t]
if iteration_done:
updates += [t_cur_update]
if self.use_cosine_annealing and iteration_done:
updates += [eta_t_update]
return control_flow_ops.group(*updates)
def _projection_op(self, state, name=None):
with ops.colocate_with(state):
return state_ops.assign(
state,
_project_stochastic_matrix_wrt_euclidean_norm(state),
name=name)
def testDeferredSlotRestoration(self):
checkpoint_directory = self.get_temp_dir()
root = trackable_utils.Checkpoint()
root.var = trackable_utils.add_variable(root,
name="var",
initializer=0.)
optimizer = adam.AdamOptimizer(0.1)
if context.executing_eagerly():
optimizer.minimize(root.var.read_value)
else:
train_op = optimizer.minimize(root.var)
# Note that `optimizer` has not been added as a dependency of
# `root`. Create a one-off grouping so that slot variables for `root.var`
# get initialized too.
self.evaluate(
trackable_utils.gather_initializers(
trackable_utils.Checkpoint(root=root,
optimizer=optimizer)))
self.evaluate(train_op)
self.evaluate(state_ops.assign(root.var, 12.))
no_slots_path = root.save(
os.path.join(checkpoint_directory, "no_slots"))
root.optimizer = optimizer
self.evaluate(state_ops.assign(root.var, 13.))
self.evaluate(
state_ops.assign(optimizer.get_slot(name="m", var=root.var), 14.))
slots_path = root.save(os.path.join(checkpoint_directory,
"with_slots"))
new_root = trackable_utils.Checkpoint()
# Load the slot-containing checkpoint (deferred), then immediately overwrite
# the non-slot variable (also deferred).
slot_status = new_root.restore(slots_path)
no_slot_status = new_root.restore(no_slots_path)
with self.assertRaises(AssertionError):
no_slot_status.assert_consumed()
new_root.var = trackable_utils.add_variable(new_root,
name="var",
shape=[])
no_slot_status.assert_consumed()
no_slot_status.run_restore_ops()
self.assertEqual(12., self.evaluate(new_root.var))
new_root.optimizer = adam.AdamOptimizer(0.1)
slot_status.assert_existing_objects_matched()
with self.assertRaisesRegexp(AssertionError, "beta1_power"):
slot_status.assert_consumed()
self.assertEqual(12., self.evaluate(new_root.var))
if context.executing_eagerly():
# Slot variables are only created with restoring initializers when
# executing eagerly.
self.assertEqual(
14.,
self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
else:
self.assertIs(
new_root.optimizer.get_slot(name="m", var=new_root.var), None)
if context.executing_eagerly():
new_root.optimizer.minimize(new_root.var.read_value)
else:
train_op = new_root.optimizer.minimize(new_root.var)
# The slot variable now exists; restore() didn't create it, but we should
# now have a restore op for it.
slot_status.run_restore_ops()
self.assertEqual(
14.,
self.evaluate(
new_root.optimizer.get_slot(name="m", var=new_root.var)))
self.evaluate(train_op)
slot_status.assert_consumed()
def doBasicsOneExportPath(self,
export_path,
clear_devices=False,
global_step=GLOBAL_STEP,
sharded=True,
export_count=1):
# Build a graph with 2 parameter nodes on different devices.
ops.reset_default_graph()
with session.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
# v2 is an unsaved variable derived from v0 and v1. It is used to
# exercise the ability to run an init op when restoring a graph.
with sess.graph.device("/cpu:0"):
v0 = variables.Variable(10, name="v0")
with sess.graph.device("/cpu:1"):
v1 = variables.Variable(20, name="v1")
v2 = variables.Variable(1,
name="v2",
trainable=False,
collections=[])
assign_v2 = state_ops.assign(v2, math_ops.add(v0, v1))
init_op = control_flow_ops.group(assign_v2, name="init_op")
ops.add_to_collection("v", v0)
ops.add_to_collection("v", v1)
ops.add_to_collection("v", v2)
named_tensor_bindings = {
"logical_input_A": v0,
"logical_input_B": v1
}
signatures = {
"foo":
exporter.regression_signature(input_tensor=v0,
output_tensor=v1),
"generic":
exporter.generic_signature(named_tensor_bindings)
}
asset_filepath_orig = os.path.join(test.get_temp_dir(),
"hello42.txt")
asset_file = constant_op.constant(asset_filepath_orig,
name="filename42")
ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, asset_file)
with gfile.FastGFile(asset_filepath_orig, "w") as f:
f.write("your data here")
assets_collection = ops.get_collection(
ops.GraphKeys.ASSET_FILEPATHS)
ignored_asset = os.path.join(test.get_temp_dir(), "ignored.txt")
with gfile.FastGFile(ignored_asset, "w") as f:
f.write("additional data here")
variables.global_variables_initializer().run()
# Run an export.
save = saver.Saver({
"v0": v0,
"v1": v1
},
restore_sequentially=True,
sharded=sharded,
write_version=saver_pb2.SaverDef.V1)
export = exporter.Exporter(save)
compare_def = ops.get_default_graph().as_graph_def()
export.init(
compare_def,
init_op=init_op,
clear_devices=clear_devices,
default_graph_signature=exporter.classification_signature(
input_tensor=v0),
named_graph_signatures=signatures,
assets_collection=assets_collection)
for x in range(export_count):
export.export(export_path,
constant_op.constant(global_step + x),
sess,
exports_to_keep=gc.largest_export_versions(2))
# Set global_step to the last exported version, as the rest of the test
# uses it to construct model export path, loads model from it, and does
# verifications. We want to make sure to always use the last exported
# version, as old ones may have be garbage-collected.
global_step += export_count - 1
# Restore graph.
ops.reset_default_graph()
with session.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
save = saver.import_meta_graph(
os.path.join(export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.META_GRAPH_DEF_FILENAME))
self.assertIsNotNone(save)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
# Validate custom graph_def.
graph_def_any = collection_def[constants.GRAPH_KEY].any_list.value
self.assertEquals(len(graph_def_any), 1)
graph_def = graph_pb2.GraphDef()
graph_def_any[0].Unpack(graph_def)
if clear_devices:
for node in compare_def.node:
node.device = ""
self.assertProtoEquals(compare_def, graph_def)
# Validate init_op.
init_ops = collection_def[constants.INIT_OP_KEY].node_list.value
self.assertEquals(len(init_ops), 1)
self.assertEquals(init_ops[0], "init_op")
# Validate signatures.
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
self.assertEqual(
default_signature.classification_signature.input.tensor_name,
"v0:0")
bindings = signatures.named_signatures[
"generic"].generic_signature.map
self.assertEquals(bindings["logical_input_A"].tensor_name, "v0:0")
self.assertEquals(bindings["logical_input_B"].tensor_name, "v1:0")
read_foo_signature = (
signatures.named_signatures["foo"].regression_signature)
self.assertEquals(read_foo_signature.input.tensor_name, "v0:0")
self.assertEquals(read_foo_signature.output.tensor_name, "v1:0")
# Validate the assets.
assets_any = collection_def[constants.ASSETS_KEY].any_list.value
self.assertEquals(len(assets_any), 1)
asset = manifest_pb2.AssetFile()
assets_any[0].Unpack(asset)
assets_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "hello42.txt")
asset_contents = gfile.GFile(assets_path).read()
self.assertEqual(asset_contents, b"your data here")
self.assertEquals("hello42.txt", asset.filename)
self.assertEquals("filename42:0", asset.tensor_binding.tensor_name)
ignored_asset_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "ignored.txt")
self.assertFalse(gfile.Exists(ignored_asset_path))
# Validate graph restoration.
if sharded:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME_PATTERN))
else:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME))
self.assertEqual(10, ops.get_collection("v")[0].eval())
self.assertEqual(20, ops.get_collection("v")[1].eval())
ops.get_collection(constants.INIT_OP_KEY)[0].run()
self.assertEqual(30, ops.get_collection("v")[2].eval())
def testDeferredSlotRestoration(self):
with self.test_session():
checkpoint_directory = self.get_temp_dir()
root = trackable_utils.Checkpoint()
root.var = trackable_utils.add_variable(root,
name="var",
initializer=0.)
optimizer = adam.Adam(0.1)
variables = [root.var]
gradients = [1.]
train_op = optimizer.apply_gradients(zip(gradients, variables))
# Note that `optimizer` has not been added as a dependency of
# `root`. Create a one-off grouping so that slot variables for `root.var`
# get initialized too.
self.evaluate(
trackable_utils.gather_initializers(
trackable_utils.Checkpoint(root=root,
optimizer=optimizer)))
self.evaluate(train_op)
self.evaluate(state_ops.assign(root.var, 12.))
no_slots_path = root.save(
os.path.join(checkpoint_directory, "no_slots"))
root.optimizer = optimizer
self.evaluate(state_ops.assign(root.var, 13.))
self.evaluate(
state_ops.assign(
optimizer.get_slot(slot_name="m", var=root.var), 14.))
slots_path = root.save(
os.path.join(checkpoint_directory, "with_slots"))
new_root = trackable_utils.Checkpoint()
# Load the slot-containing checkpoint (deferred), then immediately
# overwrite the non-slot variable (also deferred).
slot_status = new_root.restore(slots_path)
no_slot_status = new_root.restore(no_slots_path)
with self.assertRaises(AssertionError):
no_slot_status.assert_consumed()
new_root.var = trackable_utils.add_variable(new_root,
name="var",
shape=[])
no_slot_status.assert_consumed()
no_slot_status.run_restore_ops()
self.assertEqual(12., self.evaluate(new_root.var))
new_root.optimizer = adam.Adam(0.1)
slot_status.assert_existing_objects_matched()
if not context.executing_eagerly():
with self.assertRaisesRegex(AssertionError,
"Unresolved object"):
slot_status.assert_consumed()
self.assertEqual(12., self.evaluate(new_root.var))
if context.executing_eagerly():
# Slot variables are only created with restoring initializers when
# executing eagerly.
self.assertEqual(
14.,
self.evaluate(
new_root.optimizer.get_slot(slot_name="m",
var=new_root.var)))
else:
# Slot variables are not created eagerly when graph building.
with self.assertRaises(KeyError):
new_root.optimizer.get_slot(slot_name="m",
var=new_root.var)
variables = [new_root.var]
gradients = [1.]
train_op = new_root.optimizer.apply_gradients(
zip(gradients, variables))
# The slot variable now exists; restore() didn't create it, but we should
# now have a restore op for it.
slot_status.run_restore_ops()
if not context.executing_eagerly():
# The train op hasn't run when graph building, so the slot variable has
# its restored value. It has run in eager, so the value will
# be different.
self.assertEqual(
14.,
self.evaluate(
new_root.optimizer.get_slot(slot_name="m",
var=new_root.var)))
self.evaluate(train_op)
slot_status.assert_consumed()
def apply_gradients(self,
grads_and_vars,
worker_id,
global_step=None,
name=None,
collect_cdfs=False):
"""Apply gradients to variables.
This contains most of the synchronization implementation and also wraps the
apply_gradients() from the real optimizer.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
compute_gradients().
global_step: Optional Variable to increment by one after the
variables have been updated.
name: Optional name for the returned operation. Default to the
name passed to the Optimizer constructor.
Returns:
train_op: The op to dequeue a token so the replicas can exit this batch
and start the next one. This is executed by each replica.
Raises:
ValueError: If the grads_and_vars is empty.
ValueError: If global step is not provided, the staleness cannot be
checked.
"""
if not grads_and_vars:
raise ValueError("Must supply at least one variable")
if global_step is None:
raise ValueError("Global step is required to check staleness")
self._global_step = global_step
train_ops = []
aggregated_grad = []
var_list = []
self._local_step = variables.Variable(
initial_value=0,
trainable=False,
collections=[ops.GraphKeys.LOCAL_VARIABLES],
dtype=global_step.dtype.base_dtype,
name="sync_rep_local_step")
self.local_step_init_op = state_ops.assign(self._local_step,
global_step._ref())
chief_init_ops = [self.local_step_init_op]
self.ready_for_local_init_op = variables.report_uninitialized_variables(
variables.all_variables())
# The wait op waits for the current worker to dequeue a token from its respective token queue
self._wait_op = self._sync_token_queues[worker_id].dequeue()
# Replicas have to wait until they can get a token from the token queue
# BEFORE begining to compute gradients.
with ops.device(global_step.device):
queue_size = self._sync_token_queues[worker_id].size()
update_local_step_op = state_ops.assign(self._local_step,
global_step._ref())
# Gradient accum creation
with ops.name_scope(None, self._name):
for grad, var in grads_and_vars:
var_list.append(var)
tf.logging.info("Grad " + str(grad) + " assigned to " +
str(var.device))
with ops.device(var.device):
if grad is None:
continue
elif isinstance(grad, ops.Tensor):
grad_accum = data_flow_ops.ConditionalAccumulator(
grad.dtype,
shape=var.get_shape(),
shared_name=var.name + "/grad_accum")
else:
if not isinstance(grad, ops.IndexedSlices):
raise ValueError("Unknown grad type!")
grad_accum = data_flow_ops.SparseConditionalAccumulator(
grad.dtype,
shape=(),
shared_name=var.name + "/grad_accum")
self._accumulator_list.append((grad_accum, var))
"""# Phase 1 gradient computation
with ops.control_dependencies([update_local_step_op]):
for index, (grad, var) in enumerate(grads_and_vars):
with ops.device(var.device):
if grad is None:
continue
elif isinstance(grad, ops.Tensor):
grad_accum = self._accumulator_list[index][0]
train_ops.append(grad_accum.apply_grad(grad,
local_step=self._local_step._ref()))
else:
if not isinstance(grad, ops.IndexedSlices):
raise ValueError("Unknown grad type!")
grad_accum = self._accumulator_list[index][0]
train_ops.append(grad_accum.apply_indexed_slices_grad(
grad, local_step=self._local_step._ref()))"""
# Phase 1 gradient computation
with ops.control_dependencies([update_local_step_op]):
for index, (grad, var) in enumerate(grads_and_vars):
print_start_op = logging_ops.Print(
global_step, [global_step],
message="Starting to apply grads for variable %d" %
index)
with ops.device(var.device):
if grad is None:
continue
elif isinstance(grad, ops.Tensor):
grad_accum = self._accumulator_list[index][0]
with ops.control_dependencies([print_start_op]):
with tf.device("job:worker/task:%d" %
worker_id):
apply_grad_op = grad_accum.apply_grad(
grad,
local_step=self._local_step._ref())
with ops.control_dependencies(
[apply_grad_op]):
finished_print_op = logging_ops.Print(
global_step, [global_step],
message=
"Done applying grads for variable %d"
% index)
train_ops.append(finished_print_op)
else:
if not isinstance(grad, ops.IndexedSlices):
raise ValueError("Unknown grad type!")
grad_accum = self._accumulator_list[index][0]
with ops.control_dependencies([print_start_op]):
with tf.device("job:worker/task:%d" %
worker_id):
apply_grad_op = grad_accum.apply_indexed_slices_grad(
grad,
local_step=self._local_step._ref())
with ops.control_dependencies(
[apply_grad_op]):
finished_print_op = logging_ops.Print(
global_step, [global_step],
message=
"Done applying grads for variable %d"
% index)
train_ops.append(finished_print_op)
# Phase 2 gradient applying
for index, (grad, var) in enumerate(grads_and_vars):
with ops.device(var.device):
grad_accum = self._accumulator_list[index][0]
if grad is None:
aggregated_grad.append(None)
elif isinstance(grad, ops.Tensor):
if collect_cdfs:
aggregated_grad.append(
grad_accum.take_grad(self._total_num_replicas))
else:
aggregated_grad.append(grad_accum.take_grad(1))
else:
if collect_cdfs:
aggregated_grad.append(
grad_accum.take_grad(self._total_num_replicas))
else:
aggregated_grad.append(
grad_accum.take_indexed_slices_grad(1))
aggregated_grads_and_vars = zip(aggregated_grad, var_list)
# Some debug operations
self.print_sizes = logging_ops.Print(global_step, [
self._sync_token_queues[i].size()
for i in range(self._total_num_replicas)
],
message="queue sizes")
self.print_accum_sizes = logging_ops.Print(
self._local_step,
[x[0].num_accumulated()
for x in self._accumulator_list] + [worker_id],
message="Accum sizes")
self.print_local_step = logging_ops.Print(
self._local_step,
[self._local_step._ref(),
global_step._ref()],
message="local vs global step")
# sync_op will be assigned to the same device as the global step.
with ops.device(global_step.device), ops.name_scope(""):
with ops.control_dependencies([self.print_accum_sizes]):
update_op = self._opt.apply_gradients(
aggregated_grads_and_vars, global_step)
self._update_op = update_op
with ops.control_dependencies([update_op]):
sync_op = []
for cur_worker_id in range(self._total_num_replicas):
sync_op.append(
self._sync_token_queues[cur_worker_id].enqueue(
global_step))
sync_op = control_flow_ops.group(*(sync_op))
# dummy_queue is passed to the queue runner. Don't use the real queues
# because the queue runner doesn't automatically reopen it once it
# closed queues in PS devices.
dummy_queue = (data_flow_ops.FIFOQueue(
1,
types_pb2.DT_INT32,
shapes=(),
shared_name="dummy_queue"))
self._chief_queue_runner = queue_runner.QueueRunner(
dummy_queue, [sync_op])
with ops.device(global_step.device), ops.name_scope(""):
with ops.control_dependencies(train_ops):
# Worker finished applying gradients. Add token to phase1_finished_queue
train_op = logging_ops.Print(
self._local_step._ref(), [
x[0].num_accumulated()
for x in self._accumulator_list
] + [worker_id],
message="Finished worker updates",
name="FinishedWorkerUpdatesPrint")
for accum, var in self._accumulator_list:
with ops.device(var.device):
chief_init_ops.append(
accum.set_global_step(global_step,
name="SetGlobalStep"))
self.chief_init_op = control_flow_ops.group(*(chief_init_ops))
self._gradients_applied = True
return train_op
def training_graph(self,
input_data,
input_labels,
random_seed,
data_spec,
epoch=None,
input_weights=None):
"""Constructs a TF graph for training a random tree.
Args:
input_data: A tensor or SparseTensor or placeholder for input data.
input_labels: A tensor or placeholder for labels associated with
input_data.
random_seed: The random number generator seed to use for this tree. 0
means use the current time as the seed.
data_spec: A list of tf.dtype values specifying the original types of
each column.
epoch: A tensor or placeholder for the epoch the training data comes from.
input_weights: A float tensor or placeholder holding per-input weights,
or None if all inputs are to be weighted equally.
Returns:
The last op in the random tree training graph.
"""
epoch = [0] if epoch is None else epoch
if input_weights is None:
input_weights = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if isinstance(input_data, ops.SparseTensor):
sparse_indices = input_data.indices
sparse_values = input_data.values
sparse_shape = input_data.shape
input_data = []
# Count extremely random stats.
(node_sums, node_squares, splits_indices, splits_sums, splits_squares,
totals_indices, totals_sums, totals_squares,
input_leaves) = (self.training_ops.count_extremely_random_stats(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
data_spec,
input_labels,
input_weights,
self.variables.tree,
self.variables.tree_thresholds,
self.variables.node_to_accumulator_map,
self.variables.candidate_split_features,
self.variables.candidate_split_thresholds,
self.variables.start_epoch,
epoch,
num_classes=self.params.num_output_columns,
regression=self.params.regression))
node_update_ops = []
node_update_ops.append(
state_ops.assign_add(self.variables.node_sums, node_sums))
splits_update_ops = []
splits_update_ops.append(self.training_ops.scatter_add_ndim(
self.variables.candidate_split_sums,
splits_indices, splits_sums))
splits_update_ops.append(self.training_ops.scatter_add_ndim(
self.variables.accumulator_sums, totals_indices,
totals_sums))
if self.params.regression:
node_update_ops.append(state_ops.assign_add(self.variables.node_squares,
node_squares))
splits_update_ops.append(self.training_ops.scatter_add_ndim(
self.variables.candidate_split_squares,
splits_indices, splits_squares))
splits_update_ops.append(self.training_ops.scatter_add_ndim(
self.variables.accumulator_squares, totals_indices,
totals_squares))
# Sample inputs.
update_indices, feature_updates, threshold_updates = (
self.training_ops.sample_inputs(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_weights,
self.variables.node_to_accumulator_map,
input_leaves,
self.variables.candidate_split_features,
self.variables.candidate_split_thresholds,
split_initializations_per_input=(
self.params.split_initializations_per_input),
split_sampling_random_seed=random_seed))
update_features_op = state_ops.scatter_update(
self.variables.candidate_split_features, update_indices,
feature_updates)
update_thresholds_op = state_ops.scatter_update(
self.variables.candidate_split_thresholds, update_indices,
threshold_updates)
# Calculate finished nodes.
with ops.control_dependencies(splits_update_ops):
children = array_ops.squeeze(array_ops.slice(
self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
is_leaf = math_ops.equal(constants.LEAF_NODE, children)
leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
squeeze_dims=[1]))
finished, stale = self.training_ops.finished_nodes(
leaves, self.variables.node_to_accumulator_map,
self.variables.candidate_split_sums,
self.variables.candidate_split_squares,
self.variables.accumulator_sums,
self.variables.accumulator_squares,
self.variables.start_epoch, epoch,
num_split_after_samples=self.params.split_after_samples,
min_split_samples=self.params.min_split_samples)
# Update leaf scores.
non_fertile_leaves = array_ops.boolean_mask(
leaves, math_ops.less(array_ops.gather(
self.variables.node_to_accumulator_map, leaves), 0))
# TODO(gilberth): It should be possible to limit the number of non
# fertile leaves we calculate scores for, especially since we can only take
# at most array_ops.shape(finished)[0] of them.
with ops.control_dependencies(node_update_ops):
sums = array_ops.gather(self.variables.node_sums, non_fertile_leaves)
if self.params.regression:
squares = array_ops.gather(self.variables.node_squares,
non_fertile_leaves)
non_fertile_leaf_scores = self._variance(sums, squares)
else:
non_fertile_leaf_scores = self._weighted_gini(sums)
# Calculate best splits.
with ops.control_dependencies(splits_update_ops):
split_indices = self.training_ops.best_splits(
finished, self.variables.node_to_accumulator_map,
self.variables.candidate_split_sums,
self.variables.candidate_split_squares,
self.variables.accumulator_sums,
self.variables.accumulator_squares,
regression=self.params.regression)
# Grow tree.
with ops.control_dependencies([update_features_op, update_thresholds_op]):
(tree_update_indices, tree_children_updates, tree_threshold_updates,
new_eot) = (self.training_ops.grow_tree(
self.variables.end_of_tree, self.variables.node_to_accumulator_map,
finished, split_indices, self.variables.candidate_split_features,
self.variables.candidate_split_thresholds))
tree_update_op = state_ops.scatter_update(
self.variables.tree, tree_update_indices, tree_children_updates)
thresholds_update_op = state_ops.scatter_update(
self.variables.tree_thresholds, tree_update_indices,
tree_threshold_updates)
# TODO(thomaswc): Only update the epoch on the new leaves.
new_epoch_updates = epoch * array_ops.ones_like(tree_threshold_updates,
dtype=dtypes.int32)
epoch_update_op = state_ops.scatter_update(
self.variables.start_epoch, tree_update_indices,
new_epoch_updates)
# Update fertile slots.
with ops.control_dependencies([tree_update_op]):
(node_map_updates, accumulators_cleared, accumulators_allocated) = (
self.training_ops.update_fertile_slots(
finished,
non_fertile_leaves,
non_fertile_leaf_scores,
self.variables.end_of_tree,
self.variables.accumulator_sums,
self.variables.node_to_accumulator_map,
stale,
regression=self.params.regression))
# Ensure end_of_tree doesn't get updated until UpdateFertileSlots has
# used it to calculate new leaves.
gated_new_eot, = control_flow_ops.tuple([new_eot],
control_inputs=[node_map_updates])
eot_update_op = state_ops.assign(self.variables.end_of_tree, gated_new_eot)
updates = []
updates.append(eot_update_op)
updates.append(tree_update_op)
updates.append(thresholds_update_op)
updates.append(epoch_update_op)
updates.append(state_ops.scatter_update(
self.variables.node_to_accumulator_map,
array_ops.squeeze(array_ops.slice(node_map_updates, [0, 0], [1, -1]),
squeeze_dims=[0]),
array_ops.squeeze(array_ops.slice(node_map_updates, [1, 0], [1, -1]),
squeeze_dims=[0])))
cleared_and_allocated_accumulators = array_ops.concat(
0, [accumulators_cleared, accumulators_allocated])
# Calculate values to put into scatter update for candidate counts.
# Candidate split counts are always reset back to 0 for both cleared
# and allocated accumulators. This means some accumulators might be doubly
# reset to 0 if the were released and not allocated, then later allocated.
split_values = array_ops.tile(
array_ops.expand_dims(array_ops.expand_dims(
array_ops.zeros_like(cleared_and_allocated_accumulators,
dtype=dtypes.float32), 1), 2),
[1, self.params.num_splits_to_consider, self.params.num_output_columns])
updates.append(state_ops.scatter_update(
self.variables.candidate_split_sums,
cleared_and_allocated_accumulators, split_values))
if self.params.regression:
updates.append(state_ops.scatter_update(
self.variables.candidate_split_squares,
cleared_and_allocated_accumulators, split_values))
# Calculate values to put into scatter update for total counts.
total_cleared = array_ops.tile(
array_ops.expand_dims(
math_ops.neg(array_ops.ones_like(accumulators_cleared,
dtype=dtypes.float32)), 1),
[1, self.params.num_output_columns])
total_reset = array_ops.tile(
array_ops.expand_dims(
array_ops.zeros_like(accumulators_allocated,
dtype=dtypes.float32), 1),
[1, self.params.num_output_columns])
accumulator_updates = array_ops.concat(0, [total_cleared, total_reset])
updates.append(state_ops.scatter_update(
self.variables.accumulator_sums,
cleared_and_allocated_accumulators, accumulator_updates))
if self.params.regression:
updates.append(state_ops.scatter_update(
self.variables.accumulator_squares,
cleared_and_allocated_accumulators, accumulator_updates))
# Calculate values to put into scatter update for candidate splits.
split_features_updates = array_ops.tile(
array_ops.expand_dims(
math_ops.neg(array_ops.ones_like(
cleared_and_allocated_accumulators)), 1),
[1, self.params.num_splits_to_consider])
updates.append(state_ops.scatter_update(
self.variables.candidate_split_features,
cleared_and_allocated_accumulators, split_features_updates))
updates += self.finish_iteration()
return control_flow_ops.group(*updates)
def LastValueQuantize(inputs,
per_channel=False,
init_min=-6.0,
init_max=6.0,
updates_collection=ops.GraphKeys.UPDATE_OPS,
vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
name_prefix='LastValueQuant',
reuse=None,
is_training=True,
num_bits=8,
narrow_range=False):
"""Adds a layer that collects quantization ranges as last input ranges.
LastValueQuantize creates variables called 'min' and 'max', representing the
interval used for quantization and clamping.
Args:
inputs: a tensor containing values to be quantized.
per_channel: (Optional) a boolean specifying whether to use different
quantization ranges per output channel.
init_min: a float scalar, the initial value for variable min.
init_max: a float scalar, the initial value for variable max.
updates_collection: (Optional) collections to collect the update ops for
computation.
vars_collection: (Optional) collection where to store variables for
quantization interval ends.
name_prefix: name_prefix for created nodes.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
is_training: Whether the op is applied to a training or eval graph.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
Returns:
a tensor containing quantized values.
"""
with variable_scope.variable_scope(
None, default_name=name_prefix, values=[inputs], reuse=reuse):
input_shape = inputs.get_shape()
input_dim = len(input_shape)
if per_channel:
# Only support quantizing 1-, 2- and 4-dimensional tensors.
assert input_dim in [1, 2, 4], ('Expected 1D, 2D or 4D input, was: %s in '
' scope: %s' % (input_shape, name_prefix))
min_max_shape = [input_shape[-1]]
else:
min_max_shape = []
min_var = model_variable(
'min',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_min),
collections=[vars_collection],
trainable=False)
max_var = model_variable(
'max',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_max),
collections=[vars_collection],
trainable=False)
if not is_training:
return _FakeQuantWithMinMaxVars(
inputs,
min_var,
max_var,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
if per_channel:
if input_dim == 2:
reduce_dims = [0]
elif input_dim == 4:
reduce_dims = [0, 1, 2]
if per_channel:
if input_dim >= 2:
batch_min = math_ops.reduce_min(
inputs, reduction_indices=reduce_dims, name='BatchMin')
else:
batch_min = inputs
else:
batch_min = math_ops.reduce_min(inputs, name='BatchMin')
# TFLite requires that 0.0 if always in the [min; max] range.
batch_min = math_ops.minimum(batch_min, 0.0)
assign_min = state_ops.assign(min_var, batch_min, name='AssignMinLast')
ops.add_to_collection(updates_collection, assign_min.op)
if per_channel:
if input_dim >= 2:
batch_max = math_ops.reduce_max(
inputs, reduction_indices=reduce_dims, name='BatchMax')
else:
batch_max = inputs
else:
batch_max = math_ops.reduce_max(inputs, name='BatchMax')
# TFLite requires that 0.0 if always in the [min; max] range.
batch_max = math_ops.maximum(batch_max, 0.0)
assign_max = state_ops.assign(max_var, batch_max, name='AssignMaxLast')
ops.add_to_collection(updates_collection, assign_max.op)
return _FakeQuantWithMinMaxVars(
inputs,
assign_min,
assign_max,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
def testSaveRestore(self):
with self.test_session():
model = MyModel()
optimizer = adam.AdamOptimizer(0.001)
root_trackable = trackable_utils.Checkpoint(optimizer=optimizer,
model=model)
input_value = constant_op.constant([[3.]])
if context.executing_eagerly():
optimizer.minimize(lambda: model(input_value))
else:
train_op = optimizer.minimize(model(input_value))
# TODO(allenl): Make initialization more pleasant when graph building.
root_trackable.save_counter # pylint: disable=pointless-statement
self.evaluate(
trackable_utils.gather_initializers(root_trackable))
self.evaluate(train_op)
prefix = os.path.join(self.get_temp_dir(), "ckpt")
self.evaluate(
state_ops.assign(model._named_dense.variables[1], [42.]))
m_bias_slot = optimizer.get_slot(model._named_dense.variables[1],
"m")
self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
save_path = root_trackable.save(file_prefix=prefix)
self.evaluate(
state_ops.assign(model._named_dense.variables[1], [43.]))
self.evaluate(state_ops.assign(root_trackable.save_counter, 3))
optimizer_variables = self.evaluate(optimizer.variables())
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
# Immediate restoration
status = root_trackable.restore(
save_path=save_path).assert_consumed()
status.run_restore_ops()
self.assertAllEqual([42.],
self.evaluate(model._named_dense.variables[1]))
self.assertAllEqual(1, self.evaluate(root_trackable.save_counter))
self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
if not context.executing_eagerly():
return # Restore-on-create is only supported when executing eagerly
on_create_model = MyModel()
on_create_optimizer = adam.AdamOptimizer(
0.001,
# Preserve beta1_power and beta2_power when applying gradients
# so we can test that they've been restored correctly.
beta1=1.0,
beta2=1.0)
on_create_root = trackable_utils.Checkpoint(
optimizer=on_create_optimizer, model=on_create_model)
# Deferred restoration
status = on_create_root.restore(save_path=save_path)
status.assert_nontrivial_match()
status.assert_existing_objects_matched()
with self.assertRaises(AssertionError):
status.assert_consumed()
on_create_model(constant_op.constant([[3.]])) # create variables
self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
self.assertAllEqual([42.],
self.evaluate(
on_create_model._named_dense.variables[1]))
on_create_m_bias_slot = on_create_optimizer.get_slot(
on_create_model._named_dense.variables[1], "m")
status.assert_existing_objects_matched()
with self.assertRaises(AssertionError):
status.assert_consumed()
# Optimizer slot variables are created when the original variable is
# restored.
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
self.assertAllEqual(optimizer_variables[2:],
self.evaluate(on_create_optimizer.variables()))
dummy_var = variables.Variable([1.])
on_create_optimizer.minimize(loss=dummy_var.read_value)
status.assert_existing_objects_matched()
status.assert_consumed()
beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators(
)
self.assertAllEqual(optimizer_variables[0],
self.evaluate(beta1_power))
self.assertAllEqual(optimizer_variables[1],
self.evaluate(beta2_power))
def testSaveRestore(self):
with self.test_session():
model = MyModel()
optimizer = adam.Adam(0.001)
root_trackable = trackable_utils.Checkpoint(optimizer=optimizer,
model=model)
input_value = constant_op.constant([[3.]])
with backprop.GradientTape() as tape:
loss = model(input_value)
variables = model.trainable_variables
gradients = tape.gradient(loss, variables)
train_op = optimizer.apply_gradients(zip(gradients, variables))
self.assertFalse(root_trackable.save_counter.trainable)
self.evaluate(trackable_utils.gather_initializers(root_trackable))
self.evaluate(train_op)
prefix = os.path.join(self.get_temp_dir(), "ckpt")
self.evaluate(
state_ops.assign(model._named_dense.variables[1], [42.]))
m_bias_slot = optimizer.get_slot(model._named_dense.variables[1],
"m")
self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
save_path = root_trackable.save(file_prefix=prefix)
self.evaluate(
state_ops.assign(model._named_dense.variables[1], [43.]))
self.evaluate(state_ops.assign(root_trackable.save_counter, 3))
optimizer_variables = self.evaluate(
sorted(optimizer.variables(), key=lambda v: v.name))
self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
# Immediate restoration
status = root_trackable.restore(
save_path=save_path).assert_consumed()
status.run_restore_ops()
self.assertAllEqual([42.],
self.evaluate(model._named_dense.variables[1]))
self.assertAllEqual(1, self.evaluate(root_trackable.save_counter))
self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
if not context.executing_eagerly():
return # Restore-on-create is only supported when executing eagerly
on_create_model = MyModel()
on_create_optimizer = adam.Adam(0.001)
on_create_root = trackable_utils.Checkpoint(
optimizer=on_create_optimizer, model=on_create_model)
# Deferred restoration
status = on_create_root.restore(save_path=save_path)
status.assert_nontrivial_match()
status.assert_existing_objects_matched()
with self.assertRaises(AssertionError):
status.assert_consumed()
on_create_model(constant_op.constant([[3.]])) # create variables
self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
self.assertAllEqual([42.],
self.evaluate(
on_create_model._named_dense.variables[1]))
on_create_m_bias_slot = on_create_optimizer.get_slot(
on_create_model._named_dense.variables[1], "m")
status.assert_existing_objects_matched()
if not context.executing_eagerly():
with self.assertRaises(AssertionError):
status.assert_consumed()
# Optimizer slot variables are created when the original variable is
# restored.
self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
dummy_var = resource_variable_ops.ResourceVariable([1.])
on_create_optimizer.minimize(loss=dummy_var.read_value,
var_list=[dummy_var])
status.assert_existing_objects_matched()
status.assert_consumed()
self.assertAllEqual(
optimizer_variables,
# Creation order is different, so .variables() needs to be re-sorted.
self.evaluate(
sorted(optimizer.variables(), key=lambda v: v.name)))
def _update_statistics_from_mini_batch(self, statistics,
auxiliary_variables, times, values):
"""Given mini-batch input, update `statistics` and `auxiliary_variables`."""
values = math_ops.cast(values, self._dtype)
# The density (measured in times per observation) that we see in each part
# of the mini-batch.
batch_inter_observation_duration = (
math_ops.cast(
math_ops.reduce_max(times, axis=1) -
math_ops.reduce_min(times, axis=1), self._dtype) /
math_ops.cast(array_ops.shape(times)[1] - 1, self._dtype))
# Co-locate updates with their variables to minimize race conditions when
# updating statistics.
with ops.colocate_with(auxiliary_variables.max_time_seen):
# There is a race condition if this value is being updated from multiple
# workers. However, it should eventually reach the correct value if the
# last chunk is presented enough times.
max_time_seen_assign = state_ops.assign(
auxiliary_variables.max_time_seen,
gen_math_ops.maximum(auxiliary_variables.max_time_seen,
math_ops.reduce_max(times)))
with ops.colocate_with(auxiliary_variables.chunk_count):
chunk_count_assign = state_ops.assign_add(
auxiliary_variables.chunk_count,
array_ops.shape(times, out_type=dtypes.int64)[0])
with ops.colocate_with(
auxiliary_variables.inter_observation_duration_sum):
inter_observation_duration_assign = state_ops.assign_add(
auxiliary_variables.inter_observation_duration_sum,
math_ops.reduce_sum(batch_inter_observation_duration))
with ops.colocate_with(auxiliary_variables.example_count):
example_count_assign = state_ops.assign_add(
auxiliary_variables.example_count,
array_ops.size(times, out_type=dtypes.int64))
# Note: These mean/variance updates assume that all points are equally
# likely, which is not true if _chunks_ are sampled uniformly from the space
# of all possible contiguous chunks, since points at the start and end of
# the series are then members of fewer chunks. For series which are much
# longer than the chunk size (the usual/expected case), this effect becomes
# irrelevant.
with ops.colocate_with(auxiliary_variables.overall_feature_sum):
overall_feature_sum_assign = state_ops.assign_add(
auxiliary_variables.overall_feature_sum,
math_ops.reduce_sum(values, axis=[0, 1]))
with ops.colocate_with(
auxiliary_variables.overall_feature_sum_of_squares):
overall_feature_sum_of_squares_assign = state_ops.assign_add(
auxiliary_variables.overall_feature_sum_of_squares,
math_ops.reduce_sum(values**2, axis=[0, 1]))
per_chunk_aux_updates = control_flow_ops.group(
max_time_seen_assign, chunk_count_assign,
inter_observation_duration_assign, example_count_assign,
overall_feature_sum_assign, overall_feature_sum_of_squares_assign)
with ops.control_dependencies([per_chunk_aux_updates]):
example_count_float = math_ops.cast(
auxiliary_variables.example_count, self._dtype)
new_feature_mean = (auxiliary_variables.overall_feature_sum /
example_count_float)
overall_feature_mean_update = state_ops.assign(
statistics.overall_feature_moments.mean, new_feature_mean)
overall_feature_var_update = state_ops.assign(
statistics.overall_feature_moments.variance,
# De-biased n / (n - 1) variance correction
example_count_float / (example_count_float - 1.) *
(auxiliary_variables.overall_feature_sum_of_squares /
example_count_float - new_feature_mean**2))
# TODO(b/35675805): Remove this cast
min_time_batch = math_ops.cast(math_ops.argmin(times[:, 0]),
dtypes.int32)
def series_start_updates():
# If this is the lowest-time chunk that we have seen so far, update
# series start moments to reflect that. Note that these statistics are
# "best effort", as there are race conditions in the update (however,
# they should eventually converge if the start of the series is
# presented enough times).
mean, variance = nn.moments(values[
min_time_batch, :self._starting_variance_window_size],
axes=[0])
return control_flow_ops.group(
state_ops.assign(statistics.series_start_moments.mean,
mean),
state_ops.assign(statistics.series_start_moments.variance,
variance))
with ops.colocate_with(statistics.start_time):
series_start_update = control_flow_ops.cond(
# Update moments whenever we even match the lowest time seen so far,
# to ensure that series start statistics are eventually updated to
# their correct values, despite race conditions (i.e. eventually
# statistics.start_time will reflect the global lowest time, and
# given that we will eventually update the series start moments to
# their correct values).
math_ops.less_equal(times[min_time_batch, 0],
statistics.start_time),
series_start_updates,
control_flow_ops.no_op)
with ops.control_dependencies([series_start_update]):
# There is a race condition if this update is performed in parallel on
# multiple workers. Since models may be sensitive to being presented
# with times before the putative start time, the value of this
# variable is post-processed above to guarantee that each worker is
# presented with a start time which is at least as low as the lowest
# time in its current mini-batch.
start_time_update = state_ops.assign(
statistics.start_time,
gen_math_ops.minimum(statistics.start_time,
math_ops.reduce_min(times)))
inter_observation_duration_estimate = (
auxiliary_variables.inter_observation_duration_sum /
math_ops.cast(auxiliary_variables.chunk_count, self._dtype))
# Estimate the total number of observations as:
# (end time - start time + 1) * average intra-chunk time density
total_observation_count_update = state_ops.assign(
statistics.total_observation_count,
math_ops.cast(
gen_math_ops.round(
math_ops.cast(
max_time_seen_assign - start_time_update + 1,
self._dtype) /
inter_observation_duration_estimate), dtypes.int64))
per_chunk_stat_updates = control_flow_ops.group(
overall_feature_mean_update, overall_feature_var_update,
series_start_update, start_time_update,
total_observation_count_update)
return per_chunk_stat_updates
def _initialized_limit_check():
return control_flow_ops.cond(
math_ops.reduce_any(non_decreasing),
lambda: state_ops.assign(discarded_windows_limiter, 0),
lambda: discarded_windows_limiter.count_up_to(
self._discard_limit))
def _apply_dense(self, grad, var):
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
m = self.get_slot(var, "m")
v = self.get_slot(var, "v")
g = self.get_slot(var, "g")
z = self.get_slot(var, "z")
b1p = self.get_slot(var, "b1p")
b2p = self.get_slot(var, "b2p")
m_t = state_ops.assign(m, beta1_t * m + grad * (1 - beta1_t), use_locking=self._use_locking)
if self._pred_g_op == 'none':
v_t = state_ops.assign(v, v * beta2_t + tf.square(g) * (1 - beta2_t), use_locking=self._use_locking)
elif self._pred_g_op == 'max':
v_t = state_ops.assign(v, v * beta2_t + tf.reduce_max(tf.square(g)) * (1 - beta2_t), use_locking=self._use_locking)
elif self._pred_g_op == 'mean':
v_t = state_ops.assign(v, v * beta2_t + tf.reduce_mean(tf.square(g)) * (1 - beta2_t), use_locking=self._use_locking)
else:
assert False
# v_t = tf.cond(tf.less(self._current_iter, tf.constant(self._init_step)),
# lambda: state_ops.assign(v, v * beta2_t + (grad * grad) * (1 - beta2_t), use_locking=self._use_locking),
# lambda: state_ops.assign(v, v * beta2_t + (g * g) * (1 - beta2_t), use_locking=self._use_locking))
# cond = (tf.sign(tf.cast(self._current_iter - tf.constant(self._init_step), tf.float32) + tf.constant(0.5)) + tf.constant(1.0)) / tf.constant(2.0)
# v_a = v * beta2_t + (grad * grad) * (1 - beta2_t)
# v_b = v * beta2_t + (g * g) * (1 - beta2_t)
# v_t = state_ops.assign(v, v_a * (1 - cond) + v_b * cond, use_locking=self._use_locking)
# cond = tf.abs(tf.sign(g))
# v_t = state_ops.assign(v, v * (1 - cond) + (v * beta2_t + (g * g) * (1 - beta2_t)) * cond, use_locking=self._use_locking)
# v_t = state_ops.assign(v, v * beta2_t + (g * g) * (1 - beta2_t), use_locking=self._use_locking)
# v_t = state_ops.assign(v, tf.maximum(grad * grad * beta2_fix, v * beta2_t + (g * g) * (1 - beta2_t)), use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
z_t = state_ops.assign(z, tf.cast(tf.logical_or(v_t > 0.0, z > 0.0), tf.float32))
g_t = state_ops.assign(g, grad, use_locking=self._use_locking)
b1p_t = state_ops.assign(b1p, b1p * beta1_t * tf.sign(z_t) + (1.0 - tf.sign(z_t)), use_locking=self._use_locking)
b2p_t = state_ops.assign(b2p, b2p * beta2_t * tf.sign(z_t) + (1.0 - tf.sign(z_t)), use_locking=self._use_locking)
b1_fix = tf.maximum(1e-8, 1.0 - b1p_t)
b2_fix = tf.maximum(1e-8, 1.0 - b2p_t)
step_t = z_t * (m_t / b1_fix) / (math_ops.sqrt(v_t / b2_fix) + epsilon_t)
# if var.name == self.first_var.name: #'discriminator/final_linear/w:0':
# idx = 0
# step_t = tf.Print(step_t, [z_t[idx]], 'z_t', summarize=1000)
# step_t = tf.Print(step_t, [g[idx]], 'g', summarize=1000)
# step_t = tf.Print(step_t, [grad[idx]], 'grad', summarize=1000)
# step_t = tf.Print(step_t, [b2p_t[idx]], 'b2p_t', summarize=1000)
# step_t = tf.Print(step_t, [b2_fix], 'beta2_fix', summarize=1000)
# step_t = tf.Print(step_t, [tf.sqrt(v_t / b2_fix)[idx]], 'v_t', summarize=1000)
# step_t = tf.Print(step_t, [step_t], 'step', summarize=1000)
var_update = state_ops.assign_sub(var, lr_t * step_t, use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, g_t])
def set_model(self, model):
"""Sets Keras model and creates summary ops."""
self.model = model
self._init_writer(model)
# histogram summaries only enabled in graph mode
if not context.executing_eagerly():
self._make_histogram_ops(model)
self.merged = tf_summary.merge_all()
# If both embedding_freq and embeddings_data are available, we will
# visualize embeddings.
if self.embeddings_freq and self.embeddings_data is not None:
# Avoid circular dependency.
from tensorflow.python.keras.engine import training_utils_v1 # pylint: disable=g-import-not-at-top
self.embeddings_data = training_utils_v1.standardize_input_data(
self.embeddings_data, model.input_names)
# If embedding_layer_names are not provided, get all of the embedding
# layers from the model.
embeddings_layer_names = self.embeddings_layer_names
if not embeddings_layer_names:
embeddings_layer_names = [
layer.name for layer in self.model.layers
if type(layer).__name__ == 'Embedding'
]
self.assign_embeddings = []
embeddings_vars = {}
self.batch_id = batch_id = array_ops.placeholder(dtypes.int32)
self.step = step = array_ops.placeholder(dtypes.int32)
for layer in self.model.layers:
if layer.name in embeddings_layer_names:
embedding_input = self.model.get_layer(layer.name).output
embedding_size = np.prod(embedding_input.shape[1:])
embedding_input = array_ops.reshape(
embedding_input, (step, int(embedding_size)))
shape = (self.embeddings_data[0].shape[0],
int(embedding_size))
embedding = variables.Variable(array_ops.zeros(shape),
name=layer.name +
'_embedding')
embeddings_vars[layer.name] = embedding
batch = state_ops.assign(
embedding[batch_id:batch_id + step], embedding_input)
self.assign_embeddings.append(batch)
self.saver = saver.Saver(list(embeddings_vars.values()))
# Create embeddings_metadata dictionary
if isinstance(self.embeddings_metadata, str):
embeddings_metadata = {
layer_name: self.embeddings_metadata
for layer_name in embeddings_vars.keys()
}
else:
# If embedding_metadata is already a dictionary
embeddings_metadata = self.embeddings_metadata
try:
from tensorboard.plugins import projector
except ImportError:
raise ImportError(
'Failed to import TensorBoard. Please make sure that '
'TensorBoard integration is complete."')
# TODO(psv): Add integration tests to test embedding visualization
# with TensorBoard callback. We are unable to write a unit test for this
# because TensorBoard dependency assumes TensorFlow package is installed.
config = projector.ProjectorConfig()
for layer_name, tensor in embeddings_vars.items():
embedding = config.embeddings.add()
embedding.tensor_name = tensor.name
if (embeddings_metadata is not None
and layer_name in embeddings_metadata):
embedding.metadata_path = embeddings_metadata[layer_name]
projector.visualize_embeddings(self.writer, config)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_dtype = var.dtype.base_dtype
lr_t = array_ops.identity(self._get_hyper('learning_rate', var_dtype))
beta_1_t = array_ops.identity(self._get_hyper('beta_1', var_dtype))
beta_2_t = array_ops.identity(self._get_hyper('beta_2', var_dtype))
epsilon_t = ops.convert_to_tensor(self.epsilon, var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
local_step = math_ops.cast(self.iterations + 1, var_dtype)
next_step = math_ops.cast(self.iterations + 2, var_dtype)
decay_base = math_ops.cast(0.96, var_dtype)
# Learning rate multipliers
if self.lr_multipliers is not None:
lr_t = _apply_lr_multiplier(self, lr_t, var)
momentum_cache_t = beta_1_t * (
1. - 0.5 *
(math_ops.pow(decay_base, self._initial_decay * local_step)))
momentum_cache_t_1 = beta_1_t * (
1. - 0.5 *
(math_ops.pow(decay_base, self._initial_decay * next_step)))
m_schedule_new = math_ops.cast(self._m_cache_read,
var_dtype) * momentum_cache_t
if var_dtype is self._m_cache.dtype:
m_schedule_new = array_ops.identity(
state_ops.assign(self._m_cache,
m_schedule_new,
use_locking=self._use_locking))
m_schedule_next = m_schedule_new * momentum_cache_t_1
m_scaled_g_values = grad * (1. - beta_1_t)
m_t = state_ops.assign(m, m * beta_1_t, use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
m_t_slice = array_ops.gather(m_t, indices)
m_t_prime = m_t_slice / (1. - m_schedule_next)
g_prime = grad / (1. - m_schedule_new)
m_t_bar = (1. - momentum_cache_t) * g_prime + (momentum_cache_t_1 *
m_t_prime)
v_scaled_g_values = (grad * grad) * (1. - beta_2_t)
v_t = state_ops.assign(v, v * beta_2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
v_t_slice = array_ops.gather(v_t, indices)
v_t_prime_denominator = 1. - math_ops.pow(beta_2_t, local_step)
v_t_prime = v_t_slice / v_t_prime_denominator
v_prime_sqrt_plus_eps = math_ops.sqrt(v_t_prime) + epsilon_t
var_t = self._resource_scatter_add(
var, indices, -self.eta_t * lr_t * m_t_bar / v_prime_sqrt_plus_eps)
# Weight decays
if var.name in self.weight_decays.keys():
var_t = _apply_weight_decays(self, var, var_t)
var_update = state_ops.assign(var,
var_t,
use_locking=self._use_locking)
# Cosine annealing
(iteration_done, t_cur_update,
eta_t_update) = _update_t_cur_eta_t_v2(self, lr_t, var)
if iteration_done and not self._init_notified:
self._init_notified = True
updates = [var_update, m_t_bar, v_t]
if iteration_done:
updates += [t_cur_update]
if self.use_cosine_annealing and iteration_done:
updates += [eta_t_update]
return control_flow_ops.group(*updates)
def streaming_precision_recall_arrays(n_gbboxes,
rclasses,
rscores,
tp_tensor,
fp_tensor,
remove_zero_labels=True,
metrics_collections=None,
updates_collections=None,
name=None):
"""Streaming computation of precision / recall arrays. This metrics
keeps tracks of boolean True positives and False positives arrays.
"""
with variable_scope.variable_scope(
name, 'stream_precision_recall',
[n_gbboxes, rclasses, tp_tensor, fp_tensor]):
n_gbboxes = math_ops.to_int64(n_gbboxes)
rclasses = math_ops.to_int64(rclasses)
rscores = math_ops.to_float(rscores)
stype = tf.int32
tp_tensor = tf.cast(tp_tensor, stype)
fp_tensor = tf.cast(fp_tensor, stype)
# Reshape TP and FP tensors and clean away 0 class values.
rclasses = tf.reshape(rclasses, [-1])
rscores = tf.reshape(rscores, [-1])
tp_tensor = tf.reshape(tp_tensor, [-1])
fp_tensor = tf.reshape(fp_tensor, [-1])
if remove_zero_labels:
mask = tf.greater(rclasses, 0)
rclasses = tf.boolean_mask(rclasses, mask)
rscores = tf.boolean_mask(rscores, mask)
tp_tensor = tf.boolean_mask(tp_tensor, mask)
fp_tensor = tf.boolean_mask(fp_tensor, mask)
# Local variables accumlating information over batches.
v_nobjects = _create_local('v_nobjects', shape=[], dtype=tf.int64)
v_ndetections = _create_local('v_ndetections',
shape=[],
dtype=tf.int32)
v_scores = _create_local('v_scores', shape=[
0,
])
v_tp = _create_local('v_tp', shape=[
0,
], dtype=stype)
v_fp = _create_local('v_fp', shape=[
0,
], dtype=stype)
# Update operations.
nobjects_op = state_ops.assign_add(v_nobjects,
tf.reduce_sum(n_gbboxes))
ndetections_op = state_ops.assign_add(
v_ndetections, tf.size(rscores, out_type=tf.int32))
scores_op = state_ops.assign(v_scores,
tf.concat([v_scores, rscores], axis=0),
validate_shape=False)
tp_op = state_ops.assign(v_tp,
tf.concat([v_tp, tp_tensor], axis=0),
validate_shape=False)
fp_op = state_ops.assign(v_fp,
tf.concat([v_fp, fp_tensor], axis=0),
validate_shape=False)
# Precision and recall computations.
# r = _precision_recall(nobjects_op, scores_op, tp_op, fp_op, 'value')
r = _precision_recall(v_nobjects, v_ndetections, v_scores, v_tp, v_fp,
'value')
with ops.control_dependencies(
[nobjects_op, ndetections_op, scores_op, tp_op, fp_op]):
update_op = _precision_recall(nobjects_op, ndetections_op,
scores_op, tp_op, fp_op, 'update_op')
# update_op = tf.Print(update_op,
# [tf.reduce_sum(tf.cast(mask, tf.int64)),
# tf.reduce_sum(tf.cast(mask2, tf.int64)),
# tf.reduce_min(rscores),
# tf.reduce_sum(n_gbboxes)],
# 'Metric: ')
# Some debugging stuff!
# update_op = tf.Print(update_op,
# [tf.shape(tp_op),
# tf.reduce_sum(tf.cast(tp_op, tf.int64), axis=0)],
# 'TP and FP shape: ')
# update_op[0] = tf.Print(update_op,
# [nobjects_op],
# '# Groundtruth bboxes: ')
# update_op = tf.Print(update_op,
# [update_op[0][0],
# update_op[0][-1],
# tf.reduce_min(update_op[0]),
# tf.reduce_max(update_op[0]),
# tf.reduce_min(update_op[1]),
# tf.reduce_max(update_op[1])],
# 'Precision and recall :')
if metrics_collections:
ops.add_to_collections(metrics_collections, r)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return r, update_op
def test_save_restore_multi_variables(self, rnn_mode, num_units, input_size,
num_layers, direction):
# Verify the restored opaque param, once converted to tf_canonical format,
# is the same as the tf canonicals of the pre-restored param.
if not context.context().num_gpus():
self.skipTest("No GPUs found")
with self.session(use_gpu=True) as sess:
opaque_params = []
saveables = []
num_opaque_params = 2
for i in range(num_opaque_params):
opaque_params.append(
self._create_opaque_param(
rnn_mode,
num_units,
input_size,
num_layers,
direction,
name="opaque_param_%d" % i))
saveable = self._create_saveable(opaque_params[i], rnn_mode, num_units,
input_size, num_layers, direction)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable)
saveables.append(saveable)
weights_ops, biases_ops = [], []
for i in range(num_opaque_params):
weights_op, biases_op = (
saveables[i].format_converter.opaque_to_tf_canonical(
saveables[i]._variables))
weights_ops.append(weights_op)
biases_ops.append(biases_op)
save_path = os.path.join(self.get_temp_dir(), "save_restore_var_test")
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
init_op = variables.global_variables_initializer()
reset_ops = []
for i in range(num_opaque_params):
reset_ops.append(
state_ops.assign(opaque_params[i],
array_ops.zeros_like(opaque_params[i])))
sess.run(init_op)
self.assertEqual(save_path, saver.save(sess, save_path))
# Get the tf canonical vals before reset-restore
for i in range(num_opaque_params):
weights, biases = sess.run([weights_ops[i], biases_ops[i]])
# Reset the opaque param value
sess.run(reset_ops[i])
# Assert reset happened.
weights_z, biases_z = sess.run([weights_ops[i], biases_ops[i]])
for w in weights_z:
self.assertAllClose(w, np.zeros_like(w))
for b in biases_z:
self.assertAllClose(b, np.zeros_like(b))
# Restore opaque param value from checkpoint.
saver.restore(sess, save_path)
weights_r, biases_r = sess.run([weights_ops[i], biases_ops[i]])
self._compare_weights(weights, weights_r)
self._compare_biases(biases, biases_r)
