def test_get_filter(self):
"""Test for various filtering operations on ts ops."""
# TODO(fkp): parameterise
self.assertEqual(len(ge.filter_ops(self.graph, True)), 8)
self.assertEqual(
len(ge.filter_ops(self.graph,
lambda op: op.node_def.op == "Const")), 3)
self.assertEqual(
len(ge.filter_ops(self.graph, lambda op: op.node_def.op == "Add")),
5)
self.assertEqual(
len(ge.filter_ops_from_regex(self.graph, r"^.*\b[abc]$")), 3)
self.assertEqual(len(ge.filter_ts(self.graph, True)), 8)
self.assertEqual(
len(ge.filter_ts_from_regex(self.graph, r"^.*/[fgh]:\d$")), 3)
self.assertEqual(len(ge.get_name_scope_ops(self.graph, "foo/")), 7)
self.assertEqual(len(ge.get_name_scope_ops(self.graph, "foo/bar")), 4)
def gradients(ys, xs, grad_ys=None, checkpoints="collection", **kwargs):
"""Recompute gradients.
Authors: Tim Salimans & Yaroslav Bulatov
Modified by: Nikolay Zakirov
memory efficient gradient implementation inspired by
"Training Deep Nets with Sublinear Memory Cost"
by Chen et al. 2016 (https://arxiv.org/abs/1604.06174)
ys,xs,grad_ys,kwargs are the arguments to standard tensorflow tf.gradients
(https://www.tensorflow.org/versions/r0.12/api_docs/python/train.html#gradients)
'checkpoints' can either be
- a list consisting of tensors from the forward pass of the neural net
that we should re-use when calculating the gradients in the
backward pass
all other tensors that do not appear in this list will be re-computed
- a string or list specifying how this list should be determined.
currently we support
- 'speed': checkpoint all outputs of convolutions and matmuls.
these ops are usually the most expensive,
so checkpointing them maximizes the running speed
(this is a good option if nonlinearities, concats,
batchnorms, etc are taking up a lot of memory)
- 'memory': try to minimize the memory usage
(currently using a very simple strategy that
identifies a number of bottleneck tensors in the
graph to checkpoint)
- 'collection': look for a tensorflow collection named
'checkpoints', which holds the tensors to checkpoint
- a list: a list of strings to be matched in the names of
the tensors
"""
# print("Calling memsaving gradients with", checkpoints)
if not isinstance(ys, list):
ys = [ys]
if not isinstance(xs, list):
xs = [xs]
bwd_ops = ge.get_backward_walk_ops([y.op for y in ys], inclusive=True)
logging.debug("bwd_ops: %s", len(bwd_ops))
# forward ops are all ops that are candidates for recomputation
fwd_ops = ge.get_forward_walk_ops([x.op for x in xs],
inclusive=True,
within_ops=bwd_ops)
logging.debug("fwd_ops: %s", len(fwd_ops))
# exclude ops with no inputs
fwd_ops = [op for op in fwd_ops if op.inputs]
logging.debug("fwd_ops: %s", len(fwd_ops))
# don't recompute xs, remove variables
xs_ops = _to_ops(xs)
fwd_ops = [op for op in fwd_ops if op not in xs_ops]
fwd_ops = [op for op in fwd_ops if "/assign" not in op.name]
fwd_ops = [op for op in fwd_ops if "/Assign" not in op.name]
fwd_ops = [op for op in fwd_ops if "/read" not in op.name]
logging.debug("fwd_ops: %s", len(fwd_ops))
ts_all = ge.filter_ts(fwd_ops, True) # get the tensors
logging.debug("ts_all: %s", len(ts_all))
ts_all = [t for t in ts_all if "/read" not in t.name]
ts_all = set(ts_all) - set(xs) - set(ys)
logging.debug("ts_all: %s", len(ts_all))
# construct list of tensors to checkpoint during forward pass, if not
# given as input
if not isinstance(checkpoints, list):
if checkpoints == "collection":
checkpoints = tf.get_collection("checkpoints")
elif checkpoints == "speed":
# checkpoint all expensive ops to maximize running speed
checkpoints = ge.filter_ts_from_regex(fwd_ops, "conv2d|Conv|MatMul")
elif checkpoints == "memory":
# remove very small tensors and some weird ops
def fixdims(
t
): # tf.Dimension values are not compatible with int, convert manually
try:
return [int(e if e is not None else 64) for e in t.as_list()]
except ValueError as e:
logging.exception("%s", e)
logging.exception("unknown shape %s", t)
return [0] # unknown shape
ts_all = [
t for t in ts_all
if np.prod(fixdims(t.shape)) > MIN_CHECKPOINT_NODE_SIZE
# if (tf.size(t) > MIN_CHECKPOINT_NODE_SIZE)
]
logging.debug("ts_all: %s", len(ts_all))
ts_all = [t for t in ts_all if "L2Loss" not in t.name]
ts_all = [t for t in ts_all if "entropy" not in t.name]
ts_all = [t for t in ts_all if "FusedBatchNorm" not in t.name]
ts_all = [t for t in ts_all if "Switch" not in t.name]
ts_all = [t for t in ts_all if "dropout" not in t.name]
# DV: FP16_FIX - need to add 'Cast' layer here to make it work for FP16
ts_all = [t for t in ts_all if "Cast" not in t.name]
logging.debug("ts_all: %s", len(ts_all))
# filter out all tensors that are inputs of the backward graph
with capture_ops() as bwd_ops:
tf_gradients(ys, xs, grad_ys, **kwargs)
bwd_inputs = [t for op in bwd_ops for t in op.inputs]
# list of tensors in forward graph that is in input to bwd graph
ts_filtered = list(set(bwd_inputs).intersection(ts_all))
debug_print("Using tensors %s", ts_filtered)
# try two slightly different ways of getting bottlenecks tensors
# to checkpoint
logging.debug("len(ts_filtered): %s", len(ts_filtered))
logging.debug("len(ts_all) %s", len(ts_all))
for ts in [ts_filtered, ts_all]:
# get all bottlenecks in the graph
bottleneck_ts = []
for t in ts:
b = set(
ge.get_backward_walk_ops(
t.op, inclusive=True, within_ops=fwd_ops))
f = set(
ge.get_forward_walk_ops(
t.op, inclusive=False, within_ops=fwd_ops))
# check that there are no shortcuts
b_inp = {inp for op in b for inp in op.inputs}.intersection(ts_all)
f_inp = {inp for op in f for inp in op.inputs}.intersection(ts_all)
if not set(b_inp).intersection(
f_inp) and len(b_inp) + len(f_inp) >= len(ts_all):
bottleneck_ts.append(t) # we have a bottleneck!
else:
logging.debug("Rejected bottleneck candidate and ops %s %d", [t],
len(b_inp) + len(f_inp) - len(ts_all))
# success? or try again without filtering?
if len(bottleneck_ts) >= np.sqrt(
len(ts_filtered)): # yes, enough bottlenecks found!
break
# bottleneck_ts = [t for t in ts_all if 'Add' in t.name]
# logging.debug("Add only ts_all: %s", len(bottleneck_ts))
if not bottleneck_ts:
raise Exception(
"unable to find bottleneck tensors! please provide checkpoint "
'nodes manually, or use checkpoints="speed" or a list of strings.')
logging.debug("len(bottleneck_ts): %s", len(bottleneck_ts))
# sort the bottlenecks
bottlenecks_sorted_lists = tf_toposort(bottleneck_ts, within_ops=fwd_ops)
sorted_bottlenecks = [t for ts in bottlenecks_sorted_lists for t in ts]
# save an approximately optimal number ~ sqrt(N)
n_filtered = len(ts_filtered)
if len(bottleneck_ts) <= np.ceil(np.sqrt(n_filtered)):
checkpoints = sorted_bottlenecks
else:
step = int(np.ceil(len(bottleneck_ts) / np.sqrt(n_filtered)))
checkpoints = sorted_bottlenecks[step::step]
else:
raise Exception('%s is unsupported input for "checkpoints"' %
(checkpoints,))
else:
# exclude some layers as was done in the original bottleneck searching
# algorithm
for excl_layer in [
"L2Loss", "entropy", "FusedBatchNorm", "Switch", "dropout", "Cast"
]:
ts_all = [t for t in ts_all if excl_layer not in t.name]
logging.info("Excluding %s from ts_all: %d", excl_layer, len(ts_all))
# leave only layers that match strings in checkpoints list
ts_all = [
t for t in ts_all
if any(partial_match in t.name for partial_match in checkpoints)
]
logging.info("Leaving only %s in ts_all: %d", checkpoints, len(ts_all))
checkpoints = ts_all.copy()
checkpoints = list(set(checkpoints).intersection(ts_all))
# at this point selection happened and checkpoints is list of nodes
# assert isinstance(checkpoints, list)
# TODO(nikzak): implement multithreading in graph recomputation
logging.info("Checkpoint nodes used: %s", len(checkpoints))
# better error handling of special cases
# xs are already handled as checkpoint nodes, so no need to include them
xs_intersect_checkpoints = set(xs).intersection(set(checkpoints))
if xs_intersect_checkpoints:
debug_print("Warning, some input nodes are also checkpoint nodes: %s",
xs_intersect_checkpoints)
ys_intersect_checkpoints = set(ys).intersection(set(checkpoints))
debug_print("ys: %s, checkpoints: %s, intersect: %s", ys, checkpoints,
ys_intersect_checkpoints)
# saving an output node (ys) gives no benefit in memory while creating
# new edge cases, exclude them
if ys_intersect_checkpoints:
debug_print("Warning, some output nodes are also checkpoints nodes: %s",
format_ops(ys_intersect_checkpoints))
# remove initial and terminal nodes from checkpoints list if present
checkpoints = list(set(checkpoints) - set(ys) - set(xs))
logging.info("Pruned initial and terminal nodes. Leaving %d",
len(checkpoints))
# check that we have some nodes to checkpoint
if not checkpoints:
raise Exception("no checkpoints nodes found or given as input! ")
# disconnect dependencies between checkpointed tensors
checkpoints_disconnected = {}
for x in checkpoints:
if x.op and x.op.name is not None:
grad_node = tf.stop_gradient(x, name=x.op.name + "_sg")
else:
grad_node = tf.stop_gradient(x)
grad_node.op._set_device(x.op.node_def.device)
checkpoints_disconnected[x] = grad_node
# partial derivatives to the checkpointed tensors and xs
ops_to_copy = fast_backward_ops(
seed_ops=[y.op for y in ys], stop_at_ts=checkpoints, within_ops=fwd_ops)
debug_print("Found %s ops to copy within fwd_ops %s, seed %s, stop_at %s",
len(ops_to_copy), fwd_ops, [r.op for r in ys], checkpoints)
debug_print("ops_to_copy = %s", ops_to_copy)
debug_print("Processing list %s", ys)
_, info = ge.copy_with_input_replacements(ge.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items():
op._set_device(origin_op.node_def.device)
copied_ops = info._transformed_ops.values()
debug_print("Copied %s to %s", ops_to_copy, copied_ops)
ge.reroute_ts(
checkpoints_disconnected.values(),
checkpoints_disconnected.keys(),
can_modify=copied_ops)
debug_print("Rewired %s in place of %s restricted to %s",
checkpoints_disconnected.values(),
checkpoints_disconnected.keys(), copied_ops)
# get gradients with respect to current boundary + original x's
copied_ys = [info._transformed_ops[y.op]._outputs[0] for y in ys]
boundary = list(checkpoints_disconnected.values())
dv = tf_gradients(ys=copied_ys, xs=boundary + xs, grad_ys=grad_ys, **kwargs)
debug_print("Got gradients %s", dv)
debug_print("for %s", copied_ys)
debug_print("with respect to %s", boundary + xs)
inputs_to_do_before = [y.op for y in ys]
if grad_ys is not None:
inputs_to_do_before += grad_ys
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# partial derivatives to the checkpointed nodes
# dictionary of "node: backprop" for nodes in the boundary
d_checkpoints = dict(
zip(checkpoints_disconnected.keys(), dv[:len(checkpoints_disconnected)]))
# partial derivatives to xs (usually the params of the neural net)
d_xs = dv[len(checkpoints_disconnected):]
# incorporate derivatives flowing through the checkpointed nodes
logging.info("Sorting nodes topologically")
checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops)
logging.info("Rebuilding graph with %d checkpoints",
len(checkpoints_sorted_lists))
for index, ts in enumerate(checkpoints_sorted_lists[::-1]):
if index % 50 == 0:
logging.info("Processed %d nodes", index)
debug_print("Processing list %s", ts)
checkpoints_other = [r for r in checkpoints if r not in ts]
checkpoints_disconnected_other = [
checkpoints_disconnected[r] for r in checkpoints_other
]
# copy part of the graph below current checkpoint node, stopping at
# other checkpoints nodes
ops_to_copy = fast_backward_ops(
within_ops=fwd_ops,
seed_ops=[r.op for r in ts],
stop_at_ts=checkpoints_other)
debug_print("Found %s ops to copy within %s, seed %s, stop_at %s",
len(ops_to_copy), fwd_ops, [r.op for r in ts],
checkpoints_other)
debug_print("ops_to_copy = %s", ops_to_copy)
if not ops_to_copy: # we're done!
break
_, info = ge.copy_with_input_replacements(ge.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items():
op._set_device(origin_op.node_def.device)
copied_ops = info._transformed_ops.values()
debug_print("Copied %s to %s", ops_to_copy, copied_ops)
ge.reroute_ts(
checkpoints_disconnected_other,
checkpoints_other,
can_modify=copied_ops)
debug_print("Rewired %s in place of %s restricted to %s",
checkpoints_disconnected_other, checkpoints_other, copied_ops)
# gradient flowing through the checkpointed node
boundary = [info._transformed_ops[r.op]._outputs[0] for r in ts]
substitute_backprops = [d_checkpoints[r] for r in ts]
dv = tf_gradients(
boundary,
checkpoints_disconnected_other + xs,
grad_ys=substitute_backprops,
**kwargs)
debug_print("Got gradients %s", dv)
debug_print("for %s", boundary)
debug_print("with respect to %s", checkpoints_disconnected_other + xs)
debug_print("with boundary backprop substitutions %s", substitute_backprops)
inputs_to_do_before = [d_checkpoints[r].op for r in ts]
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# partial derivatives to the checkpointed nodes
for r, dr in zip(checkpoints_other, dv[:len(checkpoints_other)]):
if dr is not None:
if d_checkpoints[r] is None:
d_checkpoints[r] = dr
else:
d_checkpoints[r] += dr
def _unsparsify(x):
if not isinstance(x, tf.IndexedSlices):
return x
if x.dense_shape is None:
raise ValueError(
"memory_saving_gradients has sparse gradients of unknown shape.")
indices = x.indices
while indices.shape.ndims < x.values.shape.ndims:
indices = tf.expand_dims(indices, -1)
return tf.scatter_nd(indices, x.values, x.dense_shape)
# partial derivatives to xs (usually the params of the neural net)
d_xs_new = dv[len(checkpoints_other):]
for j in range(len(xs)):
if d_xs_new[j] is not None:
if d_xs[j] is None:
d_xs[j] = _unsparsify(d_xs_new[j])
else:
d_xs[j] += _unsparsify(d_xs_new[j])
return d_xs
