def test_dual_chain():
"""Runs regular chain gradient, makes sure memory usage makes sense."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
nodes1 = make_chain_tanh_constant(n, "a")
nodes2 = make_chain_tanh_constant(n, "b")
a0, b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad = tf.gradients([a + b], [a0, b0])
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun([grad[0].op, grad[1].op])
peak_memory = cpu_peak()
expected_peak = (2 * n + 1) * 10**6
util.report_memory(peak_memory, expected_peak)
# 1 unit of memory slack since parallel computation chains adds
# scheduling variablity
if not REMOVE_ASSERTS:
assert (peak_memory -
expected_peak) < 1.1 * 10**9, "Difference too large."
def test_dual_chain_rewrite():
"""Runs regular chain gradient, makes sure memory usage makes sense."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
nodes1 = make_chain_tanh_constant(n, "a")
nodes2 = make_chain_tanh_constant(n, "b")
a0, b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad = memory_saving_gradients.gradients(
[a + b], [a0, b0], checkpoints=[nodes1[2], nodes2[2]])
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun([grad[0].op, grad[1].op])
peak_memory = cpu_peak()
# normal usage comes from 2*n nodes + default ygrad node + 2 gradient nodes
# here we save two 2 units of memory by dropping 2 activations (a1/b1) temporarily
# also, this moves "peak memory" scenario lower down the chain
# where the final addition node activations are no longer needed (another -1)
expected_peak = (2 * (n - 1) + 1) * 10**6
util.report_memory(peak_memory, expected_peak)
# since two independent chains, some variability in node scheduling
# allow 1MB slack
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 4.1e6, "Difference too large."
def test_dual_chain():
"""Runs regular chain gradient, makes sure memory usage makes sense."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
nodes1 = make_chain_tanh_constant(n, "a")
nodes2 = make_chain_tanh_constant(n, "b")
a0,b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad = tf.gradients([a+b], [a0, b0])
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun([grad[0].op, grad[1].op])
peak_memory = cpu_peak()
expected_peak = (2*n+1)*10**6
util.report_memory(peak_memory, expected_peak)
# 1 unit of memory slack since parallel computation chains adds
# scheduling variablity
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1*10**9, "Difference too large."
def test_dual_chain_rewrite():
"""Runs regular chain gradient, makes sure memory usage makes sense."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
nodes1 = make_chain_tanh_constant(n, "a")
nodes2 = make_chain_tanh_constant(n, "b")
a0,b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad = memory_saving_gradients.gradients([a+b], [a0, b0],
checkpoints=[nodes1[2], nodes2[2]])
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun([grad[0].op, grad[1].op])
peak_memory = cpu_peak()
# normal usage comes from 2*n nodes + default ygrad node + 2 gradient nodes
# here we save two 2 units of memory by dropping 2 activations (a1/b1) temporarily
# also, this moves "peak memory" scenario lower down the chain
# where the final addition node activations are no longer needed (another -1)
expected_peak = (2*(n-1)+1)*10**6
util.report_memory(peak_memory, expected_peak)
# since two independent chains, some variability in node scheduling
# allow 1MB slack
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 4.1e6, "Difference too large."
def test_long_chain_tarjan(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="tarjan"
strategy."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 100
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
grad = memory_saving_gradients.gradients_tarjan([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
added = linearize_lib.linearize()
peak_memory = cpu_peak()
# points picked
# a09:0,19:0,a29:0,a39:0,a49:0,a58:0,a68:0,a78:0,a88:0,a97:0
expected_peak = 18e6
util.report_memory(peak_memory, expected_peak)
# todo: remove "REMOVE_ASSERTS"
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
def test_long_chain_memory(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="memory"
strategy."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 100
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
tf.add_to_collection("checkpoints", nodes[10])
tf.add_to_collection("checkpoints", nodes[20])
#grad = memory_saving_gradients.gradients_collection([a], [a0])[0]
grad = memory_saving_gradients.gradients_memory([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
added = linearize_lib.linearize()
peak_memory = cpu_peak()
# 20 mem used with following tensors picked automatically as bottlenecks
# ['a10:0', 'a19:0', 'a28:0', 'a37:0', 'a46:0', 'a55:0', 'a64:0', 'a73:0',
# 'a82:0', 'a91:0']
expected_peak = 20 * 10**6
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
def test_chain_memory(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="memory" strat."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 6 # for n=5, only choice of a2 saves memory, and alg picks a3
# hence use n>5 to avoid this edge condition
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
grad = memory_saving_gradients.gradients_memory([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
linearize_lib.linearize()
peak_memory = cpu_peak()
expected_peak = (n + 1 -
1) * 10**6 # 1 for each node + 1 for generated - 1 saved
# "loss" tensor
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 10000, "Difference too large."
def test_long_chain_tarjan(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="tarjan"
strategy."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 100
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
grad = memory_saving_gradients.gradients_tarjan([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
added = linearize_lib.linearize()
peak_memory = cpu_peak()
# points picked
# a09:0,19:0,a29:0,a39:0,a49:0,a58:0,a68:0,a78:0,a88:0,a97:0
expected_peak = 18e6
util.report_memory(peak_memory, expected_peak)
# todo: remove "REMOVE_ASSERTS"
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
def test_long_chain_memory(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="memory"
strategy."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 100
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
tf.add_to_collection("checkpoints", nodes[10])
tf.add_to_collection("checkpoints", nodes[20])
#grad = memory_saving_gradients.gradients_collection([a], [a0])[0]
grad = memory_saving_gradients.gradients_memory([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
added = linearize_lib.linearize()
peak_memory = cpu_peak()
# 20 mem used with following tensors picked automatically as bottlenecks
# ['a10:0', 'a19:0', 'a28:0', 'a37:0', 'a46:0', 'a55:0', 'a64:0', 'a73:0',
# 'a82:0', 'a91:0']
expected_peak = 20 * 10**6
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
def test_chain_memory(linearize=False):
"""Like test_chain, but use automatic rewriting with checkpoints="memory" strat."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 6 # for n=5, only choice of a2 saves memory, and alg picks a3
# hence use n>5 to avoid this edge condition
nodes = make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
grad = memory_saving_gradients.gradients_memory([a], [a0])[0]
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
if linearize:
linearize_lib.linearize()
peak_memory = cpu_peak()
expected_peak = (n+1-1)*10**6 # 1 for each node + 1 for generated - 1 saved
# "loss" tensor
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 10000, "Difference too large."
def test_targets():
tf.reset_default_graph()
n = 5
g = tf.get_default_graph()
nodes1 = util.make_chain_tanh_constant(n, "a")
nodes2 = util.make_chain_tanh_constant(n, "b")
a0, b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad1 = tf.gradients([a], [a0, b0])
grad2 = tf.gradients([b], [a0, b0])
assert linearize_lib.linearize(grad1) == 3
old_version = g._version
assert linearize_lib.linearize(grad1) == 0
assert g._version == old_version
assert linearize_lib.linearize(grad2) == 3
assert linearize_lib.linearize(grad2) == 0
def test_targets():
tf.reset_default_graph()
n = 5
g = tf.get_default_graph()
nodes1 = util.make_chain_tanh_constant(n, "a")
nodes2 = util.make_chain_tanh_constant(n, "b")
a0,b0 = nodes1[0], nodes2[0]
a, b = nodes1[-1], nodes2[-1]
grad1 = tf.gradients([a], [a0, b0])
grad2 = tf.gradients([b], [a0, b0])
assert linearize_lib.linearize(grad1) == 3
old_version = g._version
assert linearize_lib.linearize(grad1) == 0
assert g._version == old_version
assert linearize_lib.linearize(grad2) == 3
assert linearize_lib.linearize(grad2) == 0
def test_chain_linearize():
tf.reset_default_graph()
n = 5
nodes = util.make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
order1 = linearize_lib.obtain_linear_order()
observed_order1 = [n.name for n in order1]
num_new_deps = linearize_lib.linearize()
assert num_new_deps == 0
def test_articulation_points():
tf.reset_default_graph()
n = 5
nodes = util.make_chain_tanh_constant(n)
a0 = nodes[0]
a = nodes[-1]
points = linearize_lib.sorted_articulation_points(None)
# original list is ['a00', 'a01', 'a02', 'a03', 'a04']
# end-points are not considered separators, so result should be
assert util.format_ops(points) == ['a01', 'a02', 'a03']
tf.reset_default_graph()
n = 5
nodes = _make_simple_caterpillar_graph(n)
a0 = nodes[0]
a = nodes[-1]
points = linearize_lib.sorted_articulation_points(None)
assert util.format_ops(points) == [
'merge0', 'merge1', 'merge2', 'merge3', 'merge4', 'merge5'
]
def test_chain_rewrite_save_one_before_last():
"""Take chain of length 5, save first node."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
a0, a1, a2, a3, a4 = make_chain_tanh_constant(n)
grad = memory_saving_gradients.gradients([a4], [a0], checkpoints=[a2])[0]
expected_peak = (n + 1 - 2) * 10**6
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
peak_memory = cpu_peak()
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
def test_chain_rewrite_save_first():
"""Take chain of length 5, save first node."""
tf.reset_default_graph()
tf_dev = tf.device('/cpu:0')
tf_dev.__enter__()
n = 5
a0, a1, a2, a3, a4 = make_chain_tanh_constant(n)
grad = memory_saving_gradients.gradients([a4], [a0], checkpoints=[a1, a3])[0]
expected_peak = (n+1-2)*10**6
sess = create_session()
sessrun(tf.global_variables_initializer())
sessrun(grad.op)
peak_memory = cpu_peak()
util.report_memory(peak_memory, expected_peak)
if not REMOVE_ASSERTS:
assert (peak_memory - expected_peak) < 1.1e6, "Difference too large."
