def get_apply_compression(compression_op_spec, global_step):
"""Returns apply_compression operation matching compression_option input."""
compressor_spec = comp_op.LowRankDecompMatrixCompressor.get_default_hparams(
)
compressor_spec.set_hparam('rank', compression_op_spec.rank)
compressor_spec.set_hparam('block_size', compression_op_spec.block_size)
logging.info('Compressor spec %s', compressor_spec.to_json())
logging.info('Compression operator spec %s', compression_op_spec.to_json())
if compression_op_spec.compression_option not in _COMPRESSION_OPTIONS:
logging.info(
'Compression_option %s not in expected options: %s. '
'Will use low_rank decomp by default.',
str(compression_op_spec.compression_option),
','.join([str(opt) for opt in _COMPRESSION_OPTIONS]))
compression_op_spec.compression_option = 1
apply_compression = None
if compression_op_spec.compression_option == 1:
compressor = comp_op.LowRankDecompMatrixCompressor(
spec=compressor_spec)
apply_compression = comp_op.ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == 2:
compressor_spec.set_hparam('is_b_matrix_trainable', False)
compressor = simhash_comp_op.SimhashMatrixCompressor(
spec=compressor_spec)
apply_compression = simhash_comp_op.SimhashApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == 4:
compressor_spec.set_hparam('is_b_matrix_trainable', False)
compressor = simhash_comp_op.KmeansMatrixCompressor(
spec=compressor_spec)
apply_compression = simhash_comp_op.SimhashApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == 8:
compressor_spec.set_hparam('is_b_matrix_trainable', False)
compressor = simhash_comp_op.KmeansMatrixCompressor(
spec=compressor_spec)
apply_compression = simhash_comp_op.SimhashApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
return apply_compression
def get_apply_compression(compression_op_spec, global_step):
"""Returns apply_compression operation matching compression_option input."""
compressor_spec = comp_op.LowRankDecompMatrixCompressor.get_default_hparams()
if compression_op_spec.__contains__('rank'):
compressor_spec.set_hparam('rank', compression_op_spec.rank)
if compression_op_spec.__contains__('block_size'):
compressor_spec.set_hparam('block_size', compression_op_spec.block_size)
logging.info('Compressor spec %s', compressor_spec.to_json())
logging.info('Compression operator spec %s', compression_op_spec.to_json())
if compression_op_spec.compression_option not in list(CompressionOptions):
# if unknown compression_option is given, default to low rank compression.
logging.info(
'Compression_option %s not in expected options: %s. '
'Will use low_rank decomp by default.',
str(compression_op_spec.compression_option),
','.join([str(opt) for opt in CompressionOptions]))
compression_op_spec.compression_option = CompressionOptions.LOWRANK_MATRIX_COMPRESSION
apply_compression = None
if compression_op_spec.compression_option == CompressionOptions.LOWRANK_MATRIX_COMPRESSION:
compressor = comp_op.LowRankDecompMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.SIMHASH_MATRIX_COMPRESSION:
compressor_spec.set_hparam('is_b_matrix_trainable', False)
compressor = simhash_comp_op.SimhashMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.KMEANS_MATRIX_COMPRESSION:
compressor_spec.set_hparam('is_b_matrix_trainable', True)
compressor = simhash_comp_op.KmeansMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.KMEANS_AND_PRUNING_MATRIX_COMPRESSION:
compressor_spec.set_hparam('is_b_matrix_trainable', True)
compressor = simhash_comp_op.KmeansMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.INPUTOUTPUT_COMPRESSION:
compressor_spec.set_hparam('is_b_matrix_trainable', True)
compressor_spec.set_hparam('is_c_matrix_trainable', True)
compressor_spec.set_hparam('is_d_matrix_trainable', True)
compressor = comp_op.LowRankDecompMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.BLOCK_COMPRESSION:
compressor_spec.set_hparam('is_c_matrix_trainable', True)
compressor = comp_op.LowRankDecompMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.MIXED_BLOCK_COMPRESSION:
compressor_spec.set_hparam('is_c_matrix_trainable', True)
compressor = comp_op.LowRankDecompMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
elif compression_op_spec.compression_option == CompressionOptions.DL_MATRIX_COMPRESSION:
compressor = dl_comp_op.DLMatrixCompressor(spec=compressor_spec)
apply_compression = ApplyCompression(
scope='default_scope',
compression_spec=compression_op_spec,
compressor=compressor,
global_step=global_step)
return apply_compression
def testSimhashApplyCompression(self):
with self.cached_session():
hparams = ("name=cifar10_compression,"
"begin_compression_step=1000,"
"end_compression_step=2001,"
"compression_frequency=100,"
"compression_option=2")
spec = simhash.SimhashCompressionOp.get_default_hparams().parse(
hparams)
matrix_compressor = simhash.SimhashMatrixCompressor(
spec=compression_op.LowRankDecompMatrixCompressor.
get_default_hparams().parse("num_rows=5,num_cols=5,rank=200"))
global_step = tf.compat.v1.get_variable("global_step",
initializer=30)
apply_comp = simhash.SimhashApplyCompression(
scope="default_scope",
compression_spec=spec,
compressor=matrix_compressor,
global_step=global_step)
# Need to add initial value for a_matrix so that we would know what to
# expect back.
a_matrix_init = np.outer(np.array([1., 2., 3.]),
np.array([4., 5., 6.]))
jitter = np.tile([0, 1e-1, 2e-2], (3, 1))
a_matrix_init += jitter
a_matrix = tf.compat.v1.get_variable(
"a_matrix",
initializer=a_matrix_init.astype(np.float32),
dtype=tf.float32)
a_matrix_compressed = apply_comp.apply_compression(
a_matrix, scope="first_compressor")
c = apply_comp._compression_ops[0]
a_matrix2 = tf.compat.v1.get_variable(
"a_matrix2",
initializer=a_matrix_init.astype(np.float32),
dtype=tf.float32)
_ = apply_comp.apply_compression(a_matrix2,
scope="second_compressor")
c2 = apply_comp._compression_ops[1]
_ = apply_comp.all_update_op()
tf.compat.v1.global_variables_initializer().run()
_ = a_matrix_compressed.eval()
# Compression won't start until step 1000 + some random_shift amount.
# Here we make sure output is as expected at step 30.
self.assertEqual(c._global_step.eval(), 30)
self.assertEqual(c.alpha.eval(), 1.0)
self.assertEqual(c2.alpha.eval(), 1.0)
self.assertEqual(c._last_alpha_update_step.eval(), -1)
self.assertAllEqual(
np.array([
np.linalg.norm(c.a_matrix_tfvar.eval()),
np.linalg.norm(c.b_matrix_tfvar.eval()),
]) > 0, [True, False])
self.assertAllEqual(
np.array([
np.linalg.norm(c.a_matrix_tfvar.eval()),
np.linalg.norm(c.b_matrix_tfvar.eval()),
]) < 0.00001, [False, True])
# At this point compression should have already started being applied;
# verify at step 2000 all is as expected.
tf.compat.v1.assign(global_step, 2000).eval()
apply_comp._all_update_op.run()
_ = a_matrix_compressed.eval()
self.assertEqual(c._global_step.eval(), 2000)
self.assertAlmostEqual(c.alpha.eval(), 0.99)
self.assertEqual(c._last_alpha_update_step.eval(), 2000)
self.assertAllEqual(
np.array([
np.linalg.norm(c.a_matrix_tfvar.eval()),
np.linalg.norm(c.b_matrix_tfvar.eval()),
]) > 0, [True, True])
self.assertFalse(
np.all(
np.abs(np.linalg.norm(c.b_matrix_tfvar.eval())) < 0.00001))
# The static_matrix_compressor was configured with a rank spec of 200 --
# meaning compression by half, i.e. new_rank = orig_rank / 2.
self.assertEqual(
np.linalg.matrix_rank(c.b_matrix_tfvar.eval()),
np.linalg.matrix_rank(c.a_matrix_tfvar.eval()) / 2)
b_matrix = matrix_compressor.static_matrix_compressor(
a_matrix_init)
self.assertAllEqual(
np.linalg.norm(
np.abs(b_matrix) - np.abs(c.b_matrix_tfvar.eval())) <
0.00001, True)
