def _prune_model(self, session):
pruning_hparams = pruning.get_pruning_hparams().parse(
self.pruning_spec)
p = pruning.Pruning(pruning_hparams, sparsity=self.sparsity)
self.mask_update_op = p.conditional_mask_update_op()
tf.global_variables_initializer().run()
for _ in range(20):
session.run(self.mask_update_op)
session.run(self.increment_global_step)
def ApplyPruning(cls, pruning_hparams_dict, lstmobj, weight_name, wm_pc, # pylint:disable=invalid-name
dtype, scope):
if not cls._pruning_obj:
cls.Setup(pruning_hparams_dict, global_step=py_utils.GetGlobalStep())
compression_op_spec = pruning.get_pruning_hparams().override_from_dict(
pruning_hparams_dict)
return apply_customized_lstm_matrix_compression(cls._pruning_obj,
py_utils.WeightParams,
py_utils.WeightInit,
lstmobj, weight_name,
wm_pc.shape, dtype, scope,
compression_op_spec)
def _GetMaskUpdateOp(self):
"""Returns op to update masks and threshold variables for model pruning."""
p = self.params
tp = p.train
mask_update_op = tf.no_op()
if tp.pruning_hparams_dict:
assert isinstance(tp.pruning_hparams_dict, dict)
pruning_hparams = pruning.get_pruning_hparams().override_from_dict(
tp.pruning_hparams_dict)
pruning_obj = pruning.Pruning(
pruning_hparams, global_step=self.global_step)
pruning_obj.add_pruning_summaries()
mask_update_op = pruning_obj.conditional_mask_update_op()
return mask_update_op
def _blockMasking(self, hparams, weights, expected_mask):
threshold = tf.Variable(0.0, name="threshold")
sparsity = tf.Variable(0.5, name="sparsity")
test_spec = ",".join(hparams)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
# Set up pruning
p = pruning.Pruning(pruning_hparams, sparsity=sparsity)
with self.cached_session():
tf.global_variables_initializer().run()
_, new_mask = p._maybe_update_block_mask(weights, threshold)
# Check if the mask is the same size as the weights
self.assertAllEqual(new_mask.get_shape(), weights.get_shape())
mask_val = new_mask.eval()
self.assertAllEqual(mask_val, expected_mask)
def testGroupSpecificBlockSparsity(self):
param_list = [
"begin_pruning_step=1",
"pruning_frequency=1",
"end_pruning_step=100",
"target_sparsity=0.5",
"group_sparsity_map=[group1:0.6,group2:0.75]",
"group_block_dims_map=[group1:2x2,group2:2x4]",
"threshold_decay=0.0",
"group_pruning=True",
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
stacked_tensor_1 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 100.0, 100), [1, 100]), [2, 2])
stacked_tensor_2 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 100.0, 100), [1, 100]), [2, 4])
stacked_tensor_3 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 200.0, 100), [1, 100]), [2, 4])
with tf.variable_scope("layer1"):
w1 = tf.Variable(stacked_tensor_1, name="weights")
_ = pruning.apply_mask_with_group(w1, group_name="group1")
with tf.variable_scope("layer2"):
w2 = tf.Variable(stacked_tensor_2, name="weights")
_ = pruning.apply_mask_with_group(w2, group_name="group2")
with tf.variable_scope("layer3"):
w3 = tf.Variable(stacked_tensor_2, name="kernel")
_ = pruning.apply_mask_with_group(w3, group_name="group2")
with tf.variable_scope("layer4"):
w4 = tf.Variable(stacked_tensor_3, name="kernel")
_ = pruning.apply_mask_with_group(w4, group_name="group2")
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, 0.9, 0.9, 0.45])
def Setup(cls, pruning_hparams_dict, global_step): # pylint:disable=invalid-name
"""Set up the pruning op with pruning hyperparameters and global step.
Args:
pruning_hparams_dict: a dict containing pruning hyperparameters;
global_step: global step in TensorFlow.
"""
if cls._pruning_obj is not None:
pass
assert pruning_hparams_dict is not None
assert isinstance(pruning_hparams_dict, dict)
cls._pruning_hparams_dict = pruning_hparams_dict
cls._global_step = global_step
cls._pruning_hparams = pruning.get_pruning_hparams(
).override_from_dict(pruning_hparams_dict)
cls._pruning_obj = pruning.Pruning(spec=cls._pruning_hparams,
global_step=global_step)
def testFirstOrderGradientBlockMasking(self):
param_list = [
"prune_option=first_order_gradient",
"gradient_decay_rate=0.5",
"block_height=2",
"block_width=2",
"threshold_decay=0",
"block_pooling_function=AVG",
]
threshold = tf.Variable(0.0, name="threshold")
sparsity = tf.Variable(0.5, name="sparsity")
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
weights_avg = tf.constant([[0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2],
[0.3, 0.3, 0.4, 0.4], [0.3, 0.3, 0.4, 0.4]])
expected_mask = [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[1., 1., 1., 1.], [1., 1., 1., 1.]]
w = tf.Variable(weights_avg, name="weights")
_ = pruning.apply_mask(w, prune_option="first_order_gradient")
p = pruning.Pruning(pruning_hparams, sparsity=sparsity)
old_weight_update_op = p.old_weight_update_op()
gradient_update_op = p.gradient_update_op()
with self.cached_session() as session:
tf.global_variables_initializer().run()
session.run(gradient_update_op)
session.run(old_weight_update_op)
weights = pruning.get_weights()
_ = pruning.get_old_weights()
gradients = pruning.get_gradients()
weight = weights[0]
gradient = gradients[0]
_, new_mask = p._maybe_update_block_mask(weight, threshold,
gradient)
self.assertAllEqual(new_mask.get_shape(), weight.get_shape())
mask_val = new_mask.eval()
self.assertAllEqual(mask_val, expected_mask)
def _sparsity_m_by_n_masking(self, weight, block_size=4, sparsity=0.5):
block_sparse_param = "block_width=" + str(block_size)
param_list = [
"target_sparsity=0.5",
"intra_block_sparsity=True",
block_sparse_param,
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
sparsity = tf.Variable(sparsity, name="sparsity")
p = pruning.Pruning(pruning_hparams, sparsity=sparsity)
mask_update_op = p.conditional_mask_update_op()
with self.cached_session() as session:
tf.global_variables_initializer().run()
session.run(mask_update_op)
_, new_mask = p._maybe_update_block_mask(weight, block_size)
return new_mask
def setUp(self):
super(PruningSpeechUtilsTest, self).setUp()
# Add global step variable to the graph
self.global_step = tf.train.get_or_create_global_step()
# Add sparsity
self.sparsity = tf.Variable(0.5, name="sparsity")
# Parse hparams
self.pruning_hparams = pruning.get_pruning_hparams().parse(
self.TEST_HPARAMS)
self.pruning_obj = pruning.Pruning(
self.pruning_hparams, global_step=self.global_step)
def MockWeightParamsFn(shape, init=None, dtype=None):
if init is None:
init = MockWeightInit.Constant(0.0)
if dtype is None:
dtype = tf.float32
return {"dtype": dtype, "shape": shape, "init": init}
self.mock_weight_params_fn = MockWeightParamsFn
self.mock_lstmobj = MockLSTMCell()
self.wm_pc = np.zeros((2, 2))
def testSecondOrderGradientCalculation(self):
param_list = [
"prune_option=second_order_gradient",
"gradient_decay_rate=0.5",
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
tf.logging.info(pruning_hparams)
w = tf.Variable(tf.linspace(1.0, 10.0, 10), name="weights")
_ = pruning.apply_mask(w, prune_option="second_order_gradient")
p = pruning.Pruning(pruning_hparams)
old_weight_update_op = p.old_weight_update_op()
old_old_weight_update_op = p.old_old_weight_update_op()
gradient_update_op = p.gradient_update_op()
with self.cached_session() as session:
tf.global_variables_initializer().run()
session.run(old_weight_update_op)
session.run(old_old_weight_update_op)
session.run(tf.assign(w, tf.math.scalar_mul(2.0, w)))
session.run(gradient_update_op)
old_weights = pruning.get_old_weights()
old_old_weights = pruning.get_old_old_weights()
gradients = pruning.get_gradients()
old_weight = old_weights[0]
old_old_weight = old_old_weights[0]
gradient = gradients[0]
self.assertAllEqual(
gradient.eval(),
tf.math.scalar_mul(0.5,
tf.nn.l2_normalize(tf.linspace(1.0, 10.0,
10))).eval())
self.assertAllEqual(old_weight.eval(), old_old_weight.eval())
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = contrib_framework.get_or_create_global_step()
# Get images and labels for CIFAR-10.
images, labels = cifar10.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar10.inference(images)
# Calculate loss.
loss = cifar10.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = cifar10.train(loss, global_step)
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(
FLAGS.pruning_hparams)
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
# Use the pruning_obj to add ops to the training graph to update the masks
# The conditional_mask_update_op will update the masks only when the
# training step is in [begin_pruning_step, end_pruning_step] specified in
# the pruning spec proto
mask_update_op = pruning_obj.conditional_mask_update_op()
# Use the pruning_obj to add summaries to the graph to track the sparsity
# of each of the layers
pruning_obj.add_pruning_summaries()
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
def before_run(self, run_context):
self._step += 1
self._start_time = time.time()
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
duration = time.time() - self._start_time
loss_value = run_values.results
if self._step % 10 == 0:
num_examples_per_step = 128
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str %
(datetime.datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
# Update the masks
mon_sess.run(mask_update_op)