def _finish(self, update_ops, name_scope):
with tf.control_dependencies(update_ops):
ops1 = self.magnitude_optimizer._finish([], name_scope + "_m") # pylint: disable=protected-access
ops2 = self.direction_optimizer._finish([], name_scope + "_d") # pylint: disable=protected-access
if self.use_global_norm: # apply global grafting
with tf.control_dependencies([ops1, ops2]):
m_global_norm = tf.Variable(0.)
d_global_norm = tf.Variable(0.)
for var in self._variables:
m_step_norm = self.get_slot(var, "m_step_norm")
d_step_norm = self.get_slot(var, "d_step_norm")
tf.assign_add(m_global_norm, m_step_norm**2)
tf.assign_add(d_global_norm, d_step_norm**2)
multiplier = tf.sqrt(m_global_norm /
tf.maximum(d_global_norm, 1e-30))
step_ops = []
for var in self._variables:
d_step = self.get_slot(var, "scratch_copy")
step = tf.where(tf.greater(d_step_norm, 0),
multiplier * d_step,
tf.zeros_like(d_step))
step_op = tf.assign_add(
var, self._learning_rate_tensor * step)
step_ops.append(step_op)
return tf.group(*step_ops, name=name_scope)
return tf.group(*([ops1, ops2] + update_ops), name=name_scope)
def _Apply2(proj_layer, opt):
inputs1 = np_input1
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
loss2_1 = tf.reduce_sum(output1)
var_grads2_1 = py_utils.ComputeGradients(loss2_1, proj_layer.vars)
grads2_1 = var_grads2_1.Transform(tuple)
inputs1 = np_input2
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
loss2_2 = tf.reduce_sum(output1)
var_grads2_2 = py_utils.ComputeGradients(loss2_2, proj_layer.vars)
grads2_2 = var_grads2_2.Transform(tuple)
with cluster_factory.ForTestingWorker(add_summary=True):
_ = opt.Apply(lr, var_grads2_1)
# Get `snapshots` of the intermediate variables
vars2_intermediate = [v.read_value() for v in proj_layer.vars.Flatten()]
tf.assign_add(py_utils.GetOrCreateGlobalStepVar(), 1)
with cluster_factory.ForTestingWorker(add_summary=True):
_ = opt.Apply(lr, var_grads2_2)
vars2_1 = proj_layer.vars.Flatten()
return vars2_intermediate, vars2_1, grads2_1, grads2_2
def testDecoderFPropDeterministicAttentionDropout(self):
"""Verify that attention dropout is deterministic given fixed seeds."""
with self.session(use_gpu=False) as sess:
tf.set_random_seed(8372749040)
p = self._DecoderParams(
py_utils.VariationalNoiseParams(None, True, False, seed=1792))
p.use_while_loop_based_unrolling = False
p.attention.atten_dropout_prob = 0.5
p.attention.atten_dropout_deterministic = True
loss, per_sequence_loss = self._testDecoderFPropHelper(params=p)
global_step = py_utils.GetGlobalStep()
tf.global_variables_initializer().run()
loss_val, per_sequence_loss_val, global_steps_val = sess.run(
[loss, per_sequence_loss, global_step])
print('loss = ', loss_val, 'per sequence loss = ', per_sequence_loss_val)
self.assertAllClose([3.587372, 15.0], loss_val)
self.assertAllClose([14.171288, 9.965696, 10.221684, 19.451914],
per_sequence_loss_val)
self.assertEqual(0, global_steps_val)
# Run another step to test global_step and time_step are incremented
# correctly.
sess.run(tf.assign_add(global_step, 1))
loss_val, per_sequence_loss_val, global_steps_val = sess.run(
[loss, per_sequence_loss, global_step])
print('loss = ', loss_val, 'per sequence loss = ', per_sequence_loss_val)
self.assertAllClose([3.626164, 15.0], loss_val)
self.assertAllClose([14.70993, 10.572938, 10.516836, 18.592758],
per_sequence_loss_val)
self.assertEqual(1, global_steps_val)
def ComputePredictions(self, theta, input_batch):
input_data = tf.random.normal([1, 10], dtype=tf.float32) + tf.cast(
input_batch, tf.float32)
add = tf.assign_add(self.vars.counter1, 1.)
input_data += add
result = self.ffn.FProp(theta.ffn, input_data)
return {'result': result}
def testWeightSpecificSparsity(self):
param_list = [
"begin_pruning_step=1", "pruning_frequency=1",
"end_pruning_step=100", "target_sparsity=0.5",
"weight_sparsity_map=[layer1:0.6,layer2/weights:0.75,.*kernel:0.6]",
"threshold_decay=0.0"
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
with tf.variable_scope("layer1"):
w1 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="weights")
_ = pruning.apply_mask(w1)
with tf.variable_scope("layer2"):
w2 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="weights")
_ = pruning.apply_mask(w2)
with tf.variable_scope("layer3"):
w3 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="kernel")
_ = pruning.apply_mask(w3)
p = pruning.Pruning(pruning_hparams)
mask_update_op = p.conditional_mask_update_op()
increment_global_step = tf.assign_add(self.global_step, 1)
with self.cached_session() as session:
tf.global_variables_initializer().run()
for _ in range(110):
session.run(mask_update_op)
session.run(increment_global_step)
self.assertAllClose(session.run(pruning.get_weight_sparsity()),
[0.6, 0.75, 0.6])
def testConditionalMaskUpdate(self):
param_list = [
"pruning_frequency=2", "begin_pruning_step=1",
"end_pruning_step=6", "nbins=100"
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
weights = tf.Variable(tf.linspace(1.0, 100.0, 100), name="weights")
masked_weights = pruning.apply_mask(weights)
sparsity = tf.Variable(0.00, name="sparsity")
# Set up pruning
p = pruning.Pruning(pruning_hparams, sparsity=sparsity)
p._spec.threshold_decay = 0.0
mask_update_op = p.conditional_mask_update_op()
sparsity_val = tf.linspace(0.0, 0.9, 10)
increment_global_step = tf.assign_add(self.global_step, 1)
non_zero_count = []
with self.cached_session() as session:
tf.global_variables_initializer().run()
for i in range(10):
session.run(tf.assign(sparsity, sparsity_val[i]))
session.run(mask_update_op)
session.run(increment_global_step)
non_zero_count.append(np.count_nonzero(masked_weights.eval()))
# Weights pruned at steps 0,2,4,and,6
expected_non_zero_count = [100, 100, 80, 80, 60, 60, 40, 40, 40, 40]
self.assertAllEqual(expected_non_zero_count, non_zero_count)
def IncBy(self, delta):
"""Increment the counter by delta and return the new value."""
# NOTE: We must ensure _value is computed (_var + 0) before
# updating _var with delta.
delta = tf.cast(delta, tf.int64)
with tf.control_dependencies([self._value]):
scalar(self._name, self._value)
return tf.identity(tf.assign_add(self._var, delta))
def _ApplyAndReset():
normalized_accums = accums
if self._apply_crs_to_grad:
normalized_accums = [
tf.tpu.cross_replica_sum(accum.read_value()) for accum in accums
]
apply_op = self._opt.apply_gradients(
list(zip(normalized_accums, variables)))
with tf.control_dependencies([apply_op]):
zero_op = [tf.assign(accum, tf.zeros_like(accum)) for accum in accums]
return tf.group(zero_op, tf.assign_add(global_step, 1))
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
if self._num_micro_batches == 1:
return self._opt.apply_gradients(grads_and_vars, global_step)
global_step = global_step or py_utils.GetOrCreateGlobalStepVar()
with tf.init_scope():
self._create_slots([v for (_, v) in grads_and_vars])
accums = []
variables = []
for g, v in grads_and_vars:
accum = self.get_slot(v, 'grad_accum')
variables.append(v)
# pytype: disable=attribute-error
if isinstance(g, tf.IndexedSlices):
scaled_grad = tf.IndexedSlices(g.values /
self._num_micro_batches,
g.indices,
dense_shape=g.dense_shape)
else:
scaled_grad = g / self._num_micro_batches
accum_tensor = accum.read_value()
accums.append(accum.assign(accum_tensor + scaled_grad))
# pytype: enable=attribute-error
def _ApplyAndReset():
normalized_accums = accums
if self._apply_crs_to_grad:
normalized_accums = [
tf.tpu.cross_replica_sum(accum.read_value())
for accum in accums
]
apply_op = self._opt.apply_gradients(
list(zip(normalized_accums, variables)))
with tf.control_dependencies([apply_op]):
zero_op = [
tf.assign(accum, tf.zeros_like(accum)) for accum in accums
]
return tf.group(zero_op, tf.assign_add(global_step, 1))
def _Accum():
return tf.no_op()
accum_step = tf.cond(
tf.equal(
tf.math.floormod(self._counter + 1, self._num_micro_batches),
0),
_ApplyAndReset, # Apply the accumulated gradients and reset.
_Accum) # Accumulate gradients.
with tf.control_dependencies([tf.group(accums)]):
return tf.group(accum_step, tf.assign_add(self._counter, 1))
def _Acc(vg):
"""Updating accumulators."""
v, g = vg
with tf.variable_scope(v.op.name):
_, a = py_utils.CreateVariable(
'grad_accumulator',
py_utils.WeightParams(v.get_shape(),
py_utils.WeightInit.Constant(0.0),
self.params.dtype),
trainable=False)
a = tf.assign_add(a, g)
return py_utils.VarGrad(v, a)
def _Acc(vg):
"""Updating accumulators."""
v, g = vg
scope_name = v.name
if scope_name.endswith(':0'):
scope_name = scope_name[:-2]
with tf.variable_scope(scope_name):
a = py_utils.CreateVariable(
'grad_accumulator',
py_utils.WeightParams(v.get_shape(),
py_utils.WeightInit.Constant(0.0),
self.params.dtype),
trainable=False)
a = tf.assign_add(a, g)
return py_utils.VarGrad(v, a)
def testDecoderFPropDeterministicAttentionDropout(self):
"""Verify that attention dropout is deterministic given fixed seeds."""
with self.session(use_gpu=False):
tf.random.set_seed(8372749040)
p = _DecoderParams(
py_utils.VariationalNoiseParams(None, True, False, seed=1792))
p.use_while_loop_based_unrolling = False
p.attention.atten_dropout_prob = 0.5
p.attention.atten_dropout_deterministic = True
loss, per_sequence_loss = self._testDecoderFPropHelper(params=p)
global_step = py_utils.GetGlobalStep()
self.evaluate(tf.global_variables_initializer())
loss_val, per_sequence_loss_val, global_steps_val = self.evaluate(
[loss, per_sequence_loss, global_step])
print('loss = ', loss_val, 'per sequence loss = ',
per_sequence_loss_val)
self.assertAllClose([3.332992, 15.0], loss_val)
self.assertAllClose([13.942583, 9.632538, 9.677502, 16.742266],
per_sequence_loss_val)
self.assertEqual(0, global_steps_val)
# Run another step to test global_step and time_step are incremented
# correctly.
self.evaluate(tf.assign_add(global_step, 1))
loss_val, per_sequence_loss_val, global_steps_val = self.evaluate(
[loss, per_sequence_loss, global_step])
print('loss = ', loss_val, 'per sequence loss = ',
per_sequence_loss_val)
self.assertAllClose([3.565631, 15.0], loss_val)
self.assertAllClose([14.560061, 10.566417, 10.554007, 17.803982],
per_sequence_loss_val)
self.assertEqual(1, global_steps_val)
def testAccumulator(self):
# testAccumulator compares
# - explicit averaging of independently computed var_grads1 and
# var_grads2,
# - Accumulator(SGD) optimizer effectively doing this over 2 steps.
np.random.seed(12345)
np_input1 = np.random.normal(0.1, 0.5, [2, 4, 3])
np.random.seed(12346)
np_input2 = np.random.normal(0.1, 0.5, [2, 4, 3])
with self.session(use_gpu=True, graph=tf.Graph()) as sess:
tf.random.set_seed(123456)
params = layers.ProjectionLayer.Params()
params.name = 'proj'
params.dtype = tf.float64
params.input_dim = 3
params.output_dim = 2
params.params_init = py_utils.WeightInit.Gaussian(0.01, 123456)
params.batch_norm = False
proj_layer = layers.ProjectionLayer(params)
inputs1 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
in_padding1 = tf.zeros([2, 4, 1], dtype=tf.float64)
inputs2 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
in_padding2 = tf.zeros([2, 4, 1], dtype=tf.float64)
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
output2 = proj_layer.FPropDefaultTheta(inputs2, in_padding2)
loss1 = tf.reduce_sum(output1)
loss2 = tf.reduce_sum(output2)
var_grads1 = py_utils.ComputeGradients(loss1, proj_layer.vars)
var_grads2 = py_utils.ComputeGradients(loss2, proj_layer.vars)
op = optimizer.SGD.Params()
opt = op.Instantiate()
lr = 1e-1
with tf.control_dependencies([loss1, loss2]):
var_update_op1 = opt.Apply(
lr, py_utils.ApplyGradMultiplier(var_grads1, 1. / 2.))
with tf.control_dependencies([var_update_op1]):
var_update_op2 = opt.Apply(
lr, py_utils.ApplyGradMultiplier(var_grads2, 1. / 2.))
self.evaluate(tf.global_variables_initializer())
vars1 = self.evaluate(proj_layer.vars.Flatten())
loss1_1, grads1_1, loss1_2, grads1_2 = sess.run(
[
loss1,
var_grads1.Transform(tuple), loss2,
var_grads2.Transform(tuple)
],
feed_dict={
inputs1: np_input1,
inputs2: np_input2,
},
)
sess.run([var_update_op2],
feed_dict={
inputs1: np_input1,
inputs2: np_input2,
})
vars1_1 = self.evaluate(proj_layer.vars.Flatten())
with self.session(use_gpu=True, graph=tf.Graph()) as sess:
tf.random.set_seed(123456)
params = layers.ProjectionLayer.Params()
params.name = 'proj'
params.dtype = tf.float64
params.input_dim = 3
params.output_dim = 2
params.params_init = py_utils.WeightInit.Gaussian(0.01, 123456)
params.batch_norm = False
proj_layer = layers.ProjectionLayer(params)
in_padding1 = tf.zeros([2, 4, 1], dtype=tf.float64)
inputs1 = tf.placeholder(shape=[2, 4, 3], dtype=tf.float64)
output1 = proj_layer.FPropDefaultTheta(inputs1, in_padding1)
loss = tf.reduce_sum(output1)
var_grads = py_utils.ComputeGradients(loss, proj_layer.vars)
op = optimizer.Accumulator.Params().Set(
accum_steps=2,
dtype=tf.float64,
optimizer_tpl=optimizer.SGD.Params())
opt = op.Instantiate()
lr = 1e-1
with cluster_factory.ForTestingWorker(add_summary=True):
var_update_op = opt.Apply(lr, var_grads)
increment_global_step_op = tf.assign_add(
py_utils.GetOrCreateGlobalStepVar(), 1)
self.evaluate(tf.global_variables_initializer())
vars2 = self.evaluate(proj_layer.vars.Flatten())
loss2_1, grads2_1 = sess.run(
[loss, var_grads.Transform(tuple)],
feed_dict={
inputs1: np_input1,
})
loss2_2, grads2_2 = sess.run(
[loss, var_grads.Transform(tuple)],
feed_dict={
inputs1: np_input2,
})
acc_0 = self.evaluate([
v for v in tf.global_variables()
if 'grad_accumulator' in v.name
])[0]
sess.run([var_update_op], feed_dict={
inputs1: np_input1,
})
acc_1 = self.evaluate([
v for v in tf.global_variables()
if 'grad_accumulator' in v.name
])[0]
vars2_intermediate = self.evaluate(proj_layer.vars.Flatten())
self.evaluate(increment_global_step_op)
sess.run([var_update_op], feed_dict={
inputs1: np_input2,
})
acc_2 = self.evaluate([
v for v in tf.global_variables()
if 'grad_accumulator' in v.name
])[0]
vars2_1 = self.evaluate(proj_layer.vars.Flatten())
summary = tf.Summary.FromString(
self.evaluate(tf.summary.merge_all()))
tf.logging.info(f'summary: {summary}')
self.assertEqual(summary.value[0].tag, 'sgd_lr')
self.assertAllClose(vars1, vars2)
self.assertAllClose(acc_0, np.zeros_like(acc_0))
self.assertAllClose(acc_1, grads2_1['w'][1])
self.assertAllClose(acc_2, np.zeros_like(acc_0))
self.assertAllClose(loss1_1, loss2_1)
self.assertAllClose(loss1_2, loss2_2)
self.assertAllClose(grads1_1, grads2_1)
self.assertAllClose(grads1_2, grads2_2)
self.assertAllClose(vars1, vars2_intermediate)
self.assertAllClose(vars2[0], grads2_1['w'][0])
self.assertAllClose(vars2[0], grads2_2['w'][0])
self.assertAllClose(
vars1[0] - 0.5 * lr * (grads1_1['w'][1] + grads1_2['w'][1]),
vars1_1[0])
self.assertAllClose(
vars2[0] - 0.5 * lr * (grads2_1['w'][1] + grads2_2['w'][1]),
vars2_1[0])
self.assertAllClose(vars2, vars2_intermediate)
self.assertAllClose(vars1_1, vars2_1)
def _BPropGenTrainOps(self, vmap, metrics=None, add_summary=True):
"""Populates the train_ops dictionary in a backwards pass."""
metrics = metrics or self._metrics
bprop_variable_filters = self.input_generator.GetBpropVariableFilters()
# Only compute the mask if the variable filters are not empty.
if bprop_variable_filters != [''] * len(bprop_variable_filters):
self._ComputeGradientMask(bprop_variable_filters)
train_ops = {} # mapping from op name to op.
gradient_mask = None
if self._per_input_gradient_mask:
# TODO(neerajgaur): Change this to use source_selected from input_batch.
onehot = self.input_generator.GetInputSourceOneHot()
gradient_mask = {
k: tf.tensordot(v, onehot, 1)
for k, v in self._per_input_gradient_mask.items()
}
all_losses = []
for optimization in self.learners:
learner_name = optimization.params.name
(losses, train_ops['train/%s' % learner_name],
eval_metrics) = optimization.Apply(
metrics,
vmap,
gradient_mask=gradient_mask,
gradient_adjuster=self.AdjustGradients)
all_losses.extend(losses)
if add_summary:
for key, (value, weight) in eval_metrics.items():
self.AddEvalMetric(key + '/' + learner_name, value, weight)
relevant_bn_updates, _ = py_utils.FindRelevantBatchNormUpdates(
all_losses, tf.get_collection(py_utils.BATCH_NORM_UPDATES))
train_ops['bn_updates'] = relevant_bn_updates
var_update_ops = [
tf.group(*tf.nest.flatten(train_ops), name='var_update_ops')
]
# Post training step update.
with tf.control_dependencies(var_update_ops):
post_step_op = self.PostTrainingStepUpdate()
train_ops = {}
with tf.control_dependencies([post_step_op]):
# Get the op to update the weight masks and thresholds
mask_update_op = self._GetMaskUpdateOp()
train_ops['mask_updates'] = mask_update_op
with tf.control_dependencies([mask_update_op]):
true_global_step = py_utils.GetOrCreateGlobalStepVar()
with tf.ops.colocate_with(true_global_step):
if self.params.defer_global_step_update:
increment_global_steps = true_global_step
else:
increment_global_steps = tf.assign_add(true_global_step, 1)
if self._global_step_var != true_global_step:
with tf.ops.colocate_with(self._global_step_var):
increment_global_steps = tf.group(
increment_global_steps, tf.assign_add(self._global_step_var, 1))
train_ops['global_step'] = increment_global_steps
# If we are using Tpu Embeddings, generate the monolithic send
# gradient op.
if tf.get_collection(py_utils.TPU_EMBEDDING):
tpu_embedding = tf.get_collection(py_utils.TPU_EMBEDDING)[0]
sparse_grads = (
tpu_embedding_gradient.get_gradients_through_dummy_table_variables(
tpu_embedding))
tpu_embedding_send_gradient_op = tpu_embedding.generate_send_gradients_op(
sparse_grads, py_utils.GetGlobalStep())
train_ops['tpu_embedding'] = tpu_embedding_send_gradient_op
tpu_embedding_summary_tensors = tf.get_collection(
py_utils.TPU_EMBEDDING_SUMMARY_TENSORS)
if add_summary:
for name, value, weight in tpu_embedding_summary_tensors:
self.AddEvalMetric(name, value, weight, raise_if_already_added=False)
for op_name, op in train_ops.items():
assert op is not None, op_name
return train_ops
def FProp(self, theta, x, paddings=None, update=False):
"""Computes distances of the given input 'x' to all centroids.
This implementation applies layer normalization on 'x' internally first,
and the returned 'dists' is computed using the normalized 'x'.
Args:
theta: A `.NestedMap` of weights' values of this layer.
x: A tensor of shape [B, L, N, H].
paddings: If not None, a tensor of shape [B, L].
update: bool, whether to update centroids using x.
Returns:
dists: "distances" of the given input 'x' to all centroids.
Shape [B, L, N, K].
k_means_loss: the average squared Euclidean distances to the closest
centroid, a scalar.
"""
p = self.params
if paddings is None:
paddings = tf.zeros_like(x[:, :, 0, 0])
# Shape [B, L, 1, 1]
paddings_4d = paddings[:, :, None, None]
if p.apply_layer_norm:
x = KMeansClusteringForAtten.LayerNorm(x, p.epsilon)
# 'x' is normalized (but theta.means is not), we use negative dot product to
# approximate the Euclidean distance here.
dists = -tf.einsum('BLNH, NKH -> BLNK', x, theta.means)
# For padded positions we update the distances to very large numbers.
very_large_dists = tf.ones_like(dists) * tf.constant(
0.1, dtype=dists.dtype) * dists.dtype.max
paddings_tiled = tf.tile(paddings_4d, [1, 1, p.num_heads, p.num_clusters])
dists = tf.where(paddings_tiled > 0.0, very_large_dists, dists)
# Shape [B, L, N, K], the same as 'dists' above.
nearest_one_hot = tf.one_hot(
tf.math.argmin(dists, axis=-1),
p.num_clusters,
dtype=py_utils.FPropDtype(p))
# Same shape as the input 'x'.
nearest_centroid = tf.einsum('BLNK, NKH -> BLNH', nearest_one_hot,
theta.means)
diff = tf.math.squared_difference(x, tf.stop_gradient(nearest_centroid))
diff = py_utils.ApplyPadding(paddings_4d, diff)
diff = tf.math.reduce_mean(diff, axis=2)
# The commitment loss which when back proped against encourages the 'x'
# values to commit to their chosen centroids.
k_means_loss = tf.math.reduce_sum(diff) / tf.math.reduce_sum(1.0 - paddings)
summary_utils.scalar('k_means/squared_distance_loss', k_means_loss)
# TODO(zhouwk): investigate normalizing theta.means after each update.
means_norm = tf.norm(theta.means)
summary_utils.scalar('k_means/centroid_l2_norm/min',
tf.math.reduce_min(means_norm))
summary_utils.scalar('k_means/centroid_l2_norm/mean',
tf.math.reduce_mean(means_norm))
if not update:
return dists, k_means_loss
# To update the centroids (self.vars.means), we apply gradient descent on
# the mini-batch of input 'x', which yields the following:
# new_centroid = centroid + (1 - decay) * (x_mean - centroid)
# where x_mean is the average over all the input vectors closest to this
# centroid.
#
# Note that this approach is equivalent with backprop via
# loss = tf.math.reduce_mean(
# tf.math.squared_difference(tf.stop_gradient(x), nearest_centroid)))
# , except that here the learning rate is independently set via 'decay'.
# Ensure that the padded positions are not used to update the centroids.
nearest_one_hot = py_utils.ApplyPadding(paddings_4d, nearest_one_hot)
# Sum away batch and sequence length dimensions to get per cluster count.
# Shape: [N, K]
per_cluster_count = tf.reduce_sum(nearest_one_hot, axis=[0, 1])
summary_utils.histogram('k_means/per_cluster_vec_count', per_cluster_count)
# Sum of the input 'x' per each closest centroid.
sum_x = tf.einsum('BLNK, BLNH -> NKH', nearest_one_hot, x)
if py_utils.use_tpu():
per_cluster_count = tf.tpu.cross_replica_sum(per_cluster_count)
sum_x = tf.tpu.cross_replica_sum(sum_x)
# If per_cluster_count for a cluster is 0, then 'nearest_one_hot' in that
# cluster's position will always be 0, hence 'sum_x' in that dimension will
# be 0.
new_means = sum_x / tf.maximum(
tf.constant(1.0, dtype=per_cluster_count.dtype),
tf.expand_dims(per_cluster_count, axis=-1))
# We use exponential moving average. TODO(zhouwk): investigate smooth this
# over an exponentially moving averaged per cluster count.
#
# Note that we intentionally do not normalize the means after this update
# as empirically this works better.
update_means_diff = tf.cast((1.0 - p.decay) * (new_means - theta.means),
self.vars.means.dtype)
return py_utils.with_dependencies(
[tf.assign_add(self.vars.means, update_means_diff)],
dists), k_means_loss
def _BPropGenTrainOps(self, vmap, metrics=None, add_summary=True):
"""Populates the train_ops dictionary in a backwards pass."""
metrics = metrics or self._metrics
bprop_variable_filters = self.input_generator.GetBpropVariableFilters()
# Only compute the mask if the variable filters are not empty.
if bprop_variable_filters != [''] * len(bprop_variable_filters):
self._ComputeGradientMask(bprop_variable_filters)
train_ops = {} # mapping from op name to op.
gradient_mask = None
if self._per_input_gradient_mask:
# TODO(neerajgaur): Change this to use source_selected from input_batch.
onehot = self.input_generator.GetInputSourceOneHot()
gradient_mask = {
k: tf.tensordot(v, onehot, 1)
for k, v in self._per_input_gradient_mask.items()
}
all_losses = []
for optimization in self.learners:
learner_name = optimization.params.name
loss_name = optimization.params.loss_name or learner_name
metric = metrics.get(loss_name, None)
if metric is None:
raise ValueError('Loss %s not found in metrics %s' %
(loss_name, list(metrics.keys())))
loss = metric[0]
all_losses.append(loss)
train_ops['train/%s' % learner_name], eval_metrics = optimization.Apply(
loss,
vmap,
gradient_mask=gradient_mask,
gradient_adjuster=self.AdjustGradients)
if add_summary:
for key, (value, weight) in eval_metrics.items():
self.AddEvalMetric(key + '/' + learner_name, value, weight)
relevant_bn_updates, _ = py_utils.FindRelevantBatchNormUpdates(
all_losses, tf.get_collection(py_utils.BATCH_NORM_UPDATES))
train_ops['bn_updates'] = relevant_bn_updates
var_update_ops = [
tf.group(*tf.nest.flatten(train_ops), name='var_update_ops')
]
# Post training step update.
with tf.control_dependencies(var_update_ops):
post_step_op = self.PostTrainingStepUpdate(self.global_step)
train_ops = {}
with tf.control_dependencies([post_step_op]):
# Get the op to update the weight masks and thresholds
mask_update_op = self._GetMaskUpdateOp()
train_ops['mask_updates'] = mask_update_op
with tf.control_dependencies([mask_update_op]):
true_global_step = py_utils.GetOrCreateGlobalStepVar()
with tf.ops.colocate_with(true_global_step):
increment_global_steps = tf.assign_add(true_global_step, 1)
if self._global_step_var != true_global_step:
with tf.ops.colocate_with(self._global_step_var):
increment_global_steps = tf.group(
increment_global_steps, tf.assign_add(self._global_step_var, 1))
train_ops['global_step'] = increment_global_steps
# If we are using Tpu Embeddings, generate the monolithic send
# gradient op.
tpu_embedding_activations = tf.get_collection(
py_utils.TPU_EMBEDDING_ACTIVATIONS)
if tpu_embedding_activations:
tpu_embedding_activations_dict = tpu_embedding_activations[0]
tpu_embedding = tf.get_collection(py_utils.TPU_EMBEDDING)[0]
tpu_embedding_send_gradient_op = py_utils.ComputeTpuEmbeddingGradients(
self.loss, tpu_embedding_activations_dict, tpu_embedding)
train_ops['tpu_embedding'] = tpu_embedding_send_gradient_op
for op_name, op in train_ops.items():
assert op is not None, op_name
return train_ops
def _BPropForVariables(self, vmap):
"""Constructs the backward graph."""
bprop_variable_filters = self.input_generator.GetBpropVariableFilters()
# Only compute the mask if the variable filters are not empty.
if bprop_variable_filters != [''] * len(bprop_variable_filters):
self._ComputeGradientMask(bprop_variable_filters)
train_ops = {} # mapping from op name to op.
gradient_mask = None
if self._per_input_gradient_mask:
# TODO(neerajgaur): Change this to use source_selected from input_batch.
onehot = self.input_generator.GetInputSourceOneHot()
gradient_mask = {
k: tf.tensordot(v, onehot, 1)
for k, v in six.iteritems(self._per_input_gradient_mask)
}
all_losses = []
for optimization in self.learners:
loss_name = optimization.params.name
metric = self._metrics.get(loss_name, None)
if metric is None:
raise ValueError('Loss %s not found in metrics %s' %
(loss_name, list(self._metrics.keys())))
loss = metric[0]
all_losses.append(loss)
train_ops['train/%s' % loss_name], eval_metrics = optimization.Apply(
loss,
vmap,
gradient_mask=gradient_mask,
gradient_adjuster=self.AdjustGradients)
for key, (value, weight) in six.iteritems(eval_metrics):
self.AddEvalMetric(key + '/' + loss_name, value, weight)
relevant_bn_updates, _ = py_utils.FindRelevantBatchNormUpdates(
all_losses, tf.get_collection(py_utils.BATCH_NORM_UPDATES))
train_ops['bn_updates'] = relevant_bn_updates
# Get the op to update the weight masks and thresholds
train_ops['mask_updates'] = self._GetMaskUpdateOp()
# Post training step update.
train_ops['post_step'] = self.PostTrainingStepUpdate(self.global_step)
with tf.control_dependencies(tf.nest.flatten(train_ops)):
true_global_step = py_utils.GetOrCreateGlobalStepVar()
with tf.colocate_with(true_global_step):
increment_global_steps = tf.assign_add(true_global_step, 1)
if self._global_step_var != true_global_step:
with tf.colocate_with(self._global_step_var):
increment_global_steps = tf.group(
increment_global_steps, tf.assign_add(self._global_step_var, 1))
train_ops['global_step'] = increment_global_steps
# If we are using Tpu Embeddings, generate the monolithic send
# gradient op.
tpu_embedding_activations = tf.get_collection(
py_utils.TPU_EMBEDDING_ACTIVATIONS)
if tpu_embedding_activations:
tpu_embedding_activations_dict = tpu_embedding_activations[0]
tpu_embedding = tf.get_collection(py_utils.TPU_EMBEDDING)[0]
tpu_embedding_send_gradient_op = py_utils.ComputeTpuEmbeddingGradients(
self.loss, tpu_embedding_activations_dict, tpu_embedding)
train_ops['tpu_embedding'] = tpu_embedding_send_gradient_op
for op_name, op in six.iteritems(train_ops):
assert op is not None, op_name
# TODO(rpang): try to structure _train_op as:
# tf.cond(skip_step, <only update skip stats>, <all updates>)
# so that we skip all other updates when a step is skipped.
self._train_op = tf.group(*tf.nest.flatten(train_ops), name='bprop')
