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 test_get_filter(self): """Test for various filtering operations on ts ops.""" # TODO (fkp): parameterise id:836 # https://github.com/imdone/tensorflow/issues/837 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): ''' Authors: Tim Salimans & Yaroslav Bulatov 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 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 ''' # 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) debug_print("bwd_ops: %s", 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) debug_print("fwd_ops: %s", fwd_ops) # exclude ops with no inputs fwd_ops = [op for op in fwd_ops if op.inputs] # don't recompute xs, remove variables xs_ops = _to_ops(xs) fwd_ops = [op for op in fwd_ops if not op in xs_ops] fwd_ops = [op for op in fwd_ops if not '/assign' in op.name] fwd_ops = [op for op in fwd_ops if not '/Assign' in op.name] fwd_ops = [op for op in fwd_ops if not '/read' in op.name] ts_all = ge.filter_ts(fwd_ops, True) # get the tensors ts_all = [t for t in ts_all if '/read' not in t.name] ts_all = set(ts_all) - set(xs) - set(ys) # construct list of tensors to checkpoint during forward pass, if not # given as input if type(checkpoints) is not 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.value is not None else 64) for e in t] except: return [0] # unknown shape ts_all = [ t for t in ts_all if np.prod(fixdims(t.shape)) > MIN_CHECKPOINT_NODE_SIZE ] 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] # filter out all tensors that are inputs of the backward graph with util.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 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 not shortcuts b_inp = set([inp for op in b for inp in op.inputs]).intersection(ts_all) f_inp = set([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: debug_print( "Rejected bottleneck candidate and ops %s", [t] + list(set(ts_all) - set(b_inp) - set(f_inp))) # success? or try again without filtering? if len(bottleneck_ts) >= np.sqrt( len(ts_filtered)): # yes, enough bottlenecks found! break if not bottleneck_ts: raise Exception( 'unable to find bottleneck tensors! please provide checkpoint nodes manually, or use checkpoints="speed".' ) # 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 = len(ts_filtered) if len(bottleneck_ts) <= np.ceil(np.sqrt(N)): checkpoints = sorted_bottlenecks else: step = int(np.ceil(len(bottleneck_ts) / np.sqrt(N))) checkpoints = sorted_bottlenecks[step::step] else: raise Exception('%s is unsupported input for "checkpoints"' % (checkpoints, )) checkpoints = list(set(checkpoints).intersection(ts_all)) # at this point automatic selection happened and checkpoints is list of nodes assert isinstance(checkpoints, list) debug_print("Checkpoint nodes used: %s", 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)) # 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) 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) copied_sgv, 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 = { r: dr for r, dr in 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 checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops) for ts in checkpoints_sorted_lists[::-1]: 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 copied_sgv, 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 assert x.dense_shape is not None, "memory_saving_gradients encountered 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
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs): ''' Authors: Tim Salimans & Yaroslav Bulatov 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 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 ''' # 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) debug_print("bwd_ops: %s", 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) debug_print("fwd_ops: %s", fwd_ops) # exclude ops with no inputs fwd_ops = [op for op in fwd_ops if op.inputs] # don't recompute xs, remove variables xs_ops = _to_ops(xs) fwd_ops = [op for op in fwd_ops if not op in xs_ops] fwd_ops = [op for op in fwd_ops if not '/assign' in op.name] fwd_ops = [op for op in fwd_ops if not '/Assign' in op.name] fwd_ops = [op for op in fwd_ops if not '/read' in op.name] ts_all = ge.filter_ts(fwd_ops, True) # get the tensors ts_all = [t for t in ts_all if '/read' not in t.name] ts_all = set(ts_all) - set(xs) - set(ys) checkpoints = 'collection' # construct list of tensors to checkpoint during forward pass, if not # given as input stereo_checkpoints = ge.filter_ts_from_regex(fwd_ops, "add") motion_checkpoints = ge.filter_ts_from_regex(fwd_ops, "Conv2D") my_ckps = [] for x in motion_checkpoints: if ("motion" in x.name) and ("BatchNorm" not in x.name): my_ckps.append(x) for x in stereo_checkpoints: if ("stereo" in x.name) and ("BatchNorm" not in x.name): my_ckps.append(x) checkpoints = my_ckps checkpoints = list(set(checkpoints).intersection(ts_all)) # at this point automatic selection happened and checkpoints is list of nodes assert isinstance(checkpoints, list) debug_print("Checkpoint nodes used: %s", 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)) # 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) 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) copied_sgv, info = ge.copy_with_input_replacements(ge.sgv(ops_to_copy), {}) 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 = { r: dr for r, dr in 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 checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops) for ts in checkpoints_sorted_lists[::-1]: 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 copied_sgv, info = ge.copy_with_input_replacements( ge.sgv(ops_to_copy), {}) 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 # 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] = d_xs_new[j] else: d_xs[j] += d_xs_new[j] return d_xs
def gradients(ys, xs, # pylint: disable: too-many-statements, too-many-branches grad_ys=None, checkpoints='collection', **kwargs): ''' Authors: Tim Salimans & Yaroslav Bulatov 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 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 ''' # 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) debug_print("bwd_ops: {}".format(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) debug_print("fwd_ops: {}".format(fwd_ops)) # exclude ops with no inputs fwd_ops = [op for op in fwd_ops if op.inputs] # 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] ts_all = ge.filter_ts(fwd_ops, True) # get the tensors ts_all = [t for t in ts_all if '/read' not in t.name] ts_all = set(ts_all) - set(xs) - set(ys) # construct list of tensors to checkpoint during forward pass, if not # given as input if type(checkpoints) is not 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.value is not None else 64) for e in t] except: return [0] # unknown shape ts_all = [t for t in ts_all if np.prod(fixdims(t.shape)) > MIN_CHECKPOINT_NODE_SIZE] 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] # filter out all tensors that are inputs of the backward graph with util.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 {}".format(ts_filtered)) # try two slightly different ways of getting bottlenecks tensors # to checkpoint 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 not shortcuts b_inp = set([inp for op in b for inp in op.inputs]).intersection(ts_all) f_inp = set([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: debug_print("Rejected bottleneck candidate and ops {}".format( [t] + list(set(ts_all) - set(b_inp) - set(f_inp)))) # success? or try again without filtering? if len(bottleneck_ts) >= np.sqrt(len(ts_filtered)): # enough bottlenecks found! break if not bottleneck_ts: raise Exception('unable to find bottleneck tensors! please provide checkpoint ' 'nodes manually, or use checkpoints="speed".') # 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 = len(ts_filtered) if len(bottleneck_ts) <= np.ceil(np.sqrt(N)): checkpoints = sorted_bottlenecks else: step = int(np.ceil(len(bottleneck_ts) / np.sqrt(N))) checkpoints = sorted_bottlenecks[step::step] else: raise Exception('%s is unsupported input for "checkpoints"' % (checkpoints,)) checkpoints = list(set(checkpoints).intersection(ts_all)) # at this point automatic selection happened and checkpoints is list of nodes assert isinstance(checkpoints, list) debug_print("Checkpoint nodes used: {}".format(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: {}".format( xs_intersect_checkpoints)) ys_intersect_checkpoints = set(ys).intersection(set(checkpoints)) debug_print("ys: {}, checkpoints:{}, intersect: {}".format( 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: {}".format( format_ops(ys_intersect_checkpoints))) # remove initial and terminal nodes from checkpoints list if present checkpoints = list(set(checkpoints) - set(ys) - set(xs)) # 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) 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 {} ops to copy within fwd_ops {}, seed {}, stop_at {}".format( len(ops_to_copy), fwd_ops, [r.op for r in ys], checkpoints)) debug_print("ops_to_copy = {}".format(ops_to_copy)) debug_print("Processing list {}".format(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 {} to {}".format(ops_to_copy, copied_ops)) ge.reroute_ts(checkpoints_disconnected.values(), checkpoints_disconnected.keys(), can_modify=copied_ops) debug_print("Rewired {} in place of {} restricted to {}".format( 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 {}".format(dv)) debug_print("for %s", copied_ys) debug_print("with respect to {}".format(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 = {r: dr for r, dr in 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 checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops) for ts in checkpoints_sorted_lists[::-1]: debug_print("Processing list {}".format(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 {} ops to copy within {}, seed {}, stop_at {}".format( len(ops_to_copy), fwd_ops, [r.op for r in ts], checkpoints_other)) debug_print("ops_to_copy = {}".format(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 {} to {}".format(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 {}".format(dv)) debug_print("for {}".format(boundary)) debug_print("with respect to {}".format(checkpoints_disconnected_other+xs)) debug_print("with boundary backprop substitutions {}".format(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(var_x): if not isinstance(var_x, tf.IndexedSlices): return var_x assert var_x.dense_shape is not None, \ "memory_saving_gradients encountered sparse gradients of unknown shape" indices = var_x.indices while indices.shape.ndims < var_x.values.shape.ndims: indices = tf.expand_dims(indices, -1) return tf.scatter_nd(indices, var_x.values, var_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