-
Notifications
You must be signed in to change notification settings - Fork 21.5k
/
_memory_viz.py
632 lines (546 loc) · 24.3 KB
/
_memory_viz.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
# mypy: allow-untyped-defs
import pickle
import sys
import os
import io
import subprocess
import json
from functools import lru_cache
from typing import Any
from itertools import groupby
import base64
import warnings
import operator
cache = lru_cache(None)
__all__ = ["format_flamegraph", "segments", "memory", "compare"]
def _frame_fmt(f, full_filename=False):
i = f['line']
fname = f['filename']
if not full_filename:
fname = fname.split('/')[-1]
func = f['name']
return f'{fname}:{i}:{func}'
@cache
def _frame_filter(name, filename):
omit_functions = [
"unwind::unwind",
"CapturedTraceback::gather",
"gather_with_cpp",
"_start",
"__libc_start_main",
"PyEval_",
"PyObject_",
"PyFunction_",
]
omit_filenames = [
"core/boxing",
"/Register",
"/Redispatch",
"pythonrun.c",
"Modules/main.c",
"Objects/call.c",
"Objects/methodobject.c",
"pycore_ceval.h",
"ceval.c",
"cpython/abstract.h",
]
for of in omit_functions:
if of in name:
return False
for of in omit_filenames:
if of in filename:
return False
return True
def _frames_fmt(frames, full_filename=False, reverse=False):
if reverse:
frames = reversed(frames)
return [_frame_fmt(f, full_filename) for f in frames if _frame_filter(f['name'], f['filename'])]
def _block_extra_legacy(b):
if 'history' in b:
frames = b['history'][0].get('frames', [])
real_size = b['history'][0]['real_size']
else:
real_size = b.get('requested_size', b['size'])
frames = []
return frames, real_size
def _block_extra(b):
if 'frames' not in b:
# old snapshot format made it more complicated to get frames/allocated size
return _block_extra_legacy(b)
return b['frames'], b['requested_size']
def format_flamegraph(flamegraph_lines, flamegraph_script=None):
if flamegraph_script is None:
flamegraph_script = f'/tmp/{os.getuid()}_flamegraph.pl'
if not os.path.exists(flamegraph_script):
import urllib.request
print(f"Downloading flamegraph.pl to: {flamegraph_script}")
urllib.request.urlretrieve(
'https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl', flamegraph_script)
subprocess.check_call(['chmod', '+x', flamegraph_script])
args = [flamegraph_script, '--countname', 'bytes']
p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8')
assert p.stdin is not None
assert p.stdout is not None
p.stdin.write(flamegraph_lines)
p.stdin.close()
result = p.stdout.read()
p.stdout.close()
p.wait()
assert p.wait() == 0
return result
def _write_blocks(f, prefix, blocks):
def frames_fragment(frames):
if not frames:
return "<non-python>"
return ';'.join(_frames_fmt(frames, reverse=True))
for b in blocks:
if 'history' not in b:
frames, accounted_for_size = _block_extra(b)
f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {accounted_for_size}\n')
else:
accounted_for_size = 0
for h in b['history']:
sz = h['real_size']
accounted_for_size += sz
if 'frames' in h:
frames = h['frames']
f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {sz}\n')
else:
f.write(f'{prefix};{b["state"]};<no-context> {sz}\n')
gaps = b['size'] - accounted_for_size
if gaps:
f.write(f'{prefix};{b["state"]};<gaps> {gaps}\n')
def segments(snapshot, format_flamegraph=format_flamegraph):
f = io.StringIO()
for seg in snapshot['segments']:
prefix = f'stream_{seg["stream"]};seg_{seg["address"]}'
_write_blocks(f, prefix, seg['blocks'])
return format_flamegraph(f.getvalue())
def memory(snapshot, format_flamegraph=format_flamegraph):
f = io.StringIO()
for seg in snapshot['segments']:
prefix = f'stream_{seg["stream"]}'
_write_blocks(f, prefix, seg['blocks'])
return format_flamegraph(f.getvalue())
def compare(before, after, format_flamegraph=format_flamegraph):
def _seg_key(seg):
return (seg['address'], seg['total_size'])
def _seg_info(seg):
return f'stream_{seg["stream"]};seg_{seg["address"]}'
f = io.StringIO()
before_segs = {_seg_key(seg) for seg in before}
after_segs = {_seg_key(seg) for seg in after}
print(f'only_before = {[a for a, _ in (before_segs - after_segs)]}')
print(f'only_after = {[a for a, _ in (after_segs - before_segs)]}')
for seg in before:
if _seg_key(seg) not in after_segs:
_write_blocks(f, f'only_before;{_seg_info(seg)}', seg['blocks'])
for seg in after:
if _seg_key(seg) not in before_segs:
_write_blocks(f, f'only_after;{_seg_info(seg)}', seg['blocks'])
return format_flamegraph(f.getvalue())
def _format_size(num):
# https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size
for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
if abs(num) < 1024.0:
return f"{num:3.1f}{unit}B"
num /= 1024.0
return f"{num:.1f}YiB"
class Bytes:
def __init__(self, value):
self.value = value
def __add__(self, rhs):
return Bytes(self.value + rhs)
def __repr__(self):
return _format_size(self.value)
def calc_active(seg):
return sum(b['size'] for b in seg['blocks'] if b['state'] == 'active_allocated')
def _report_free(free_external, free_internal):
total = free_external + free_internal
suffix = ''
if total != 0:
pct = (free_internal / total) * 100
suffix = f' ({pct:.1f}% internal)'
return f'{Bytes(total)}{suffix}'
PAGE_SIZE = 1024 * 1024 * 20
legend = f"""\
Legend:
[a ] - a segment in the allocator
^-- a page {Bytes(PAGE_SIZE)} of memory in the segment
a-z: pages filled with a single block's content
' ': page is completely free
*: page if completely full with multiple blocks
0-9: page is partially full with tensors of multiple blocks (9 == 90% full)
(X% internal) - of the free memory, X% is free because we rounded the size of the allocation.
"""
def segsum(data):
r"""Visually reports how the allocator has filled its segments.
This printout can help debug fragmentation issues since free fragments
will appear as gaps in this printout. The amount of free space is reported
for each segment.
We distinguish between internal free memory which occurs because the
allocator rounds the allocation size, and external free memory, which are
the gaps between allocations in a segment.
Args:
data: snapshot dictionary created from _snapshot()
"""
segments = []
out = io.StringIO()
out.write(f"Summary of segments >= {Bytes(PAGE_SIZE)} in size\n")
total_reserved = 0
total_allocated = 0
free_external = 0
free_internal = 0
for seg in sorted(data['segments'], key=lambda x: (x['total_size'], calc_active(x))):
total_reserved += seg['total_size']
seg_free_external = 0
seg_free_internal = 0
seg_allocated = 0
all_ranges = []
boffset = 0
for b in seg['blocks']:
active = b['state'] == 'active_allocated'
if active:
_, allocated_size = _block_extra(b)
all_ranges.append((boffset, allocated_size, True))
seg_allocated += allocated_size
seg_free_internal += b['size'] - allocated_size
else:
seg_free_external += b['size']
boffset += b['size']
total_allocated += seg_allocated
free_external += seg_free_external
free_internal += seg_free_internal
nseg = (seg['total_size'] - 1) // PAGE_SIZE + 1
occupied = [' ' for _ in range(nseg)]
frac = [0.0 for _ in range(nseg)]
active_size = 0
for i, (start_, size, active) in enumerate(all_ranges):
active_size += size
finish_ = (start_ + size)
start = start_ // PAGE_SIZE
finish = (finish_ - 1) // PAGE_SIZE + 1
m = chr(ord('a' if active else 'A') + (i % 26))
for j in range(start, finish):
s = max(start_, j * PAGE_SIZE)
e = min(finish_, (j + 1) * PAGE_SIZE)
frac[j] += (e - s) / PAGE_SIZE
if occupied[j] != ' ':
occupied[j] = '0123456789*'[int(frac[j] * 10)]
else:
occupied[j] = m
stream = '' if seg['stream'] == 0 else f', stream_{seg["stream"]}'
body = ''.join(occupied)
assert seg_free_external + seg_free_internal + seg_allocated == seg['total_size']
stream = f' stream_{seg["stream"]}' if seg['stream'] != 0 else ''
if seg['total_size'] >= PAGE_SIZE:
out.write(f'[{body}] {Bytes(seg["total_size"])} allocated, '
f'{_report_free(seg_free_external, seg_free_internal)} free{stream}\n')
out.write(f'segments: {len(data["segments"])}\n')
out.write(f'total_reserved: {Bytes(total_reserved)}\n')
out.write(f'total_allocated: {Bytes(total_allocated)}\n')
internal_external = f' ({Bytes(free_internal)} internal + {Bytes(free_external)} external)' if free_internal else ''
out.write(f'total_free: {_report_free(free_external, free_internal)}\n')
out.write(legend)
assert free_internal + free_external + total_allocated == total_reserved
return out.getvalue()
def trace(data):
out = io.StringIO()
def format(entries):
segment_intervals : list = []
segment_addr_to_name = {}
allocation_addr_to_name = {}
free_names : list = []
next_name = 0
def _name():
nonlocal next_name
if free_names:
return free_names.pop()
r, m = next_name // 26, next_name % 26
next_name += 1
return f'{chr(ord("a") + m)}{"" if r == 0 else r}'
def find_segment(addr):
for name, saddr, size in segment_intervals:
if addr >= saddr and addr < saddr + size:
return name, saddr
for i, seg in enumerate(data['segments']):
saddr = seg['address']
size = seg['allocated_size']
if addr >= saddr and addr < saddr + size:
return f'seg_{i}', saddr
return None, None
count = 0
out.write(f'{len(entries)} entries\n')
total_reserved = 0
for seg in data['segments']:
total_reserved += seg['total_size']
for count, e in enumerate(entries):
if e['action'] == 'alloc':
addr, size = e['addr'], e['size']
n = _name()
seg_name, seg_addr = find_segment(addr)
if seg_name is None:
seg_name = "MEM"
offset = addr
else:
offset = addr - seg_addr
out.write(f'{n} = {seg_name}[{offset}:{Bytes(size)}]\n')
allocation_addr_to_name[addr] = (n, size, count)
count += size
elif e['action'] == 'free_requested':
addr, size = e['addr'], e['size']
name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
out.write(f'del {name} # {Bytes(size)}\n')
elif e['action'] == 'free_completed':
addr, size = e['addr'], e['size']
count -= size
name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
out.write(f'# free completed for {name} {Bytes(size)}\n')
if name in allocation_addr_to_name:
free_names.append(name)
del allocation_addr_to_name[name]
elif e['action'] == 'segment_alloc':
addr, size = e['addr'], e['size']
name = _name()
out.write(f'{name} = cudaMalloc({addr}, {Bytes(size)})\n')
segment_intervals.append((name, addr, size))
segment_addr_to_name[addr] = name
elif e['action'] == 'segment_free':
addr, size = e['addr'], e['size']
name = segment_addr_to_name.get(addr, addr)
out.write(f'cudaFree({name}) # {Bytes(size)}\n')
if name in segment_addr_to_name:
free_names.append(name)
del segment_addr_to_name[name]
elif e['action'] == 'oom':
size = e['size']
free = e['device_free']
out.write(f'raise OutOfMemoryError # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n')
else:
out.write(f'{e}\n')
out.write(f"TOTAL MEM: {Bytes(count)}")
for i, d in enumerate(data['device_traces']):
if d:
out.write(f'Device {i} ----------------\n')
format(d)
return out.getvalue()
_memory_viz_template = r"""
<!DOCTYPE html>
<html>
<head>
</head>
<body>
<script type="module">
import {add_local_files} from "https://cdn.jsdelivr.net/gh/pytorch/pytorch@main/torch/utils/viz/MemoryViz.js"
const local_files = $SNAPSHOT
add_local_files(local_files, $VIZ_KIND)
</script>
</body>
"""
def _format_viz(data, viz_kind, device):
if device is not None:
warnings.warn(
'device argument is deprecated, plots now contain all device',
FutureWarning,
stacklevel=3,
)
buffer = pickle.dumps(data)
buffer += b'\x00' * (3 - len(buffer) % 3)
# Encode the buffer with base64
encoded_buffer = base64.b64encode(buffer).decode('utf-8')
json_format = json.dumps([{"name": 'snapshot.pickle', "base64": encoded_buffer}])
return _memory_viz_template.replace('$VIZ_KIND', repr(viz_kind)) \
.replace('$SNAPSHOT', json_format)
def trace_plot(data, device=None, plot_segments=False):
"""Generate a visualization over time of the memory usage recorded by the trace as an html file.
Args:
data: Memory snapshot as generated from torch.cuda.memory._snapshot()
device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
plot_segments (bool, optional): Plots memory returned from cudaMalloc, rather than individual allocations.
Defaults to False.
Returns:
str: HTML of visualization
"""
return _format_viz(data, 'Active Memory Timeline' if not plot_segments else 'Active Cached Memory Timeline', device)
def _profile_to_snapshot(profile):
import torch
from torch.profiler._memory_profiler import Action, TensorKey
from torch._C._profiler import _EventType
memory_profile = profile._memory_profile()
allocation_stacks = {}
for event in memory_profile._op_tree.sorted_nodes:
if event.tag == _EventType.Allocation:
parent = event.parent
python_parents = []
while parent:
if parent.tag in (_EventType.PyCall, _EventType.PyCCall):
python_parents.append(parent)
parent = parent.parent
key = TensorKey.from_allocation(event.extra_fields)
# Corner case: If allocation doesn't have an ID (can't prove it was used as a Tensor)
# key will be None. I should add some way to identify these, I just haven't yet.
if key and event.extra_fields.alloc_size > 0:
allocation_stacks[key] = python_parents
device_count = torch.cuda.device_count()
snapshot = {
'device_traces': [[] for _ in range(device_count + 1)],
'segments': [{'device': device,
'address': None,
'total_size': 0,
'stream': 0,
'blocks': []} for device in range(device_count + 1)]
}
def to_device(device):
if device.type == 'cuda':
return device.index
else:
return device_count
def allocate(size, tensor_key, version, during_trace=True):
device = to_device(tensor_key.device)
addr = tensor_key.storage.ptr
seg = snapshot['segments'][device] # type: ignore[index]
if seg['address'] is None or seg['address'] > addr:
seg['address'] = addr
seg['total_size'] = max(seg['total_size'], addr + size) # record max addr for now, we will make it the size later
category = memory_profile._categories.get(tensor_key, version)
category = category.name.lower() if category is not None else "unknown"
stack = allocation_stacks.get(tensor_key, ())
stack = [{'filename': 'none', 'line': 0, 'name': p.name} for p in stack]
r = {'action': 'alloc', 'addr': addr, 'size': size, 'stream': 0, 'frames': stack, 'category': category}
if during_trace:
snapshot['device_traces'][device].append(r) # type: ignore[index]
return r
def free(alloc, device):
for e in ('free_requested', 'free_completed'):
snapshot['device_traces'][device].append({'action': e, # type: ignore[index]
'addr': alloc['addr'],
'size': alloc['size'],
'stream': 0,
'frames': alloc['frames']})
kv_to_elem = {}
# create the device trace
for time, action, (tensor_key, version), size in memory_profile.timeline:
if not isinstance(tensor_key, TensorKey):
continue
if action == Action.CREATE:
kv_to_elem[(tensor_key, version)] = allocate(size, tensor_key, version)
elif action == Action.DESTROY:
free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
elif action == Action.INCREMENT_VERSION:
free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
kv_to_elem[(tensor_key, version + 1)] = allocate(size, tensor_key, version + 1)
elif action == Action.PREEXISTING:
kv_to_elem[(tensor_key, version)] = allocate(size, tensor_key, version, during_trace=False)
# create the final snapshot state
blocks_at_end = [(to_device(tensor_key.device), event['addr'], event['size'], event['frames'])
for (tensor_key, version), event in kv_to_elem.items()]
for device, blocks in groupby(sorted(blocks_at_end), key=operator.itemgetter(0)):
seg = snapshot['segments'][device] # type: ignore[index]
last_addr = seg['address']
for _, addr, size, frames in blocks:
if last_addr < addr:
seg['blocks'].append({'size': addr - last_addr, 'state': 'inactive'})
seg['blocks'].append({'size': size, 'state': 'active_allocated', 'requested_size': size, 'frames': frames})
last_addr = addr + size
if last_addr < seg['total_size']:
seg['blocks'].append({'size': seg['total_size'] - last_addr, 'state': 'inactive'})
snapshot['segments'] = [seg for seg in snapshot['segments'] if seg['blocks']] # type: ignore[attr-defined]
for seg in snapshot['segments']: # type: ignore[attr-defined, name-defined, no-redef]
seg['total_size'] -= seg['address']
if not seg['blocks']:
seg['blocks'].append({'size': seg['total_size'], 'state': 'inactive'})
return snapshot
def profile_plot(profile, device=None):
"""Generate a visualization over time of the memory usage recorded by kineto memory profiling as an html file.
Args:
profile: profile as generated by `torch.profiler.profile(profile_memory=True)`
device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
Returns:
str: HTML of visualization
"""
snapshot = _profile_to_snapshot(profile)
return _format_viz(snapshot, 'Active Memory Timeline', device)
def segment_plot(data: Any, device=None):
return _format_viz(data, 'Allocator State History', device)
if __name__ == "__main__":
import os.path
thedir = os.path.realpath(os.path.dirname(__file__))
if thedir in sys.path:
# otherwise we find cuda/random.py as random...
sys.path.remove(thedir)
import argparse
fn_name = 'torch.cuda.memory._snapshot()'
pickled = f'pickled memory statistics from {fn_name}'
parser = argparse.ArgumentParser(description=f'Visualize memory dumps produced by {fn_name}')
subparsers = parser.add_subparsers(dest='action')
def _output(p):
p.add_argument('-o', '--output', default='output.svg', help='flamegraph svg (default: output.svg)')
description = 'Prints overall allocation statistics and a visualization of how the allocators segments are currently filled.'
stats_a = subparsers.add_parser('stats', description=description)
stats_a.add_argument('input', help=pickled)
description = 'Prints buffer of the most recent allocation events embedded in the snapshot in a Pythonic style.'
trace_a = subparsers.add_parser('trace', description=description)
trace_a.add_argument('input', help=pickled)
description = 'Generate a flamegraph that visualizes what memory is stored in each allocator segment (aka block)'
segments_a = subparsers.add_parser('segments', description=description)
segments_a.add_argument('input', help=pickled)
_output(segments_a)
description = "Generate a flamegraph the program locations contributing to CUDA memory usage."
memory_a = subparsers.add_parser('memory', description=description)
memory_a.add_argument('input', help=pickled)
_output(memory_a)
description = 'Generate a flamegraph that shows segments (aka blocks) that have been added ' \
'or removed between two different memorys snapshots.'
compare_a = subparsers.add_parser('compare', description=description)
compare_a.add_argument('before', help=pickled)
compare_a.add_argument('after', help=pickled)
_output(compare_a)
plots = (
("trace_plot", "Generate a visualization over time of the memory usage recorded by the trace as an html file."),
("segment_plot", "Visualize how allocations are packed into allocator segments at each point in a trace as an html file.")
)
for cmd, description in plots:
trace_plot_a = subparsers.add_parser(cmd, description=description)
trace_plot_a.add_argument('input', help=pickled)
help = 'visualize trace from this device (default: chooses the only device with trace info or errors)'
trace_plot_a.add_argument('-d', '--device', type=int, default=None, help=help)
help = 'path to save the visualization(default: output.html)'
trace_plot_a.add_argument('-o', '--output', default='output.html', help=help)
if cmd == "trace_plot":
help = 'visualize change to segments rather than individual allocations'
trace_plot_a.add_argument('-s', '--segments', action='store_true', help=help)
args = parser.parse_args()
def _read(name):
if name == '-':
f = sys.stdin.buffer
else:
f = open(name, 'rb')
data = pickle.load(f)
if isinstance(data, list): # segments only...
data = {'segments': data, 'traces': []}
return data
def _write(name, data):
with open(name, 'w') as f:
f.write(data)
if args.action == 'segments':
data = _read(args.input)
_write(args.output, segments(data))
elif args.action == 'memory':
data = _read(args.input)
_write(args.output, memory(data))
elif args.action == 'stats':
data = _read(args.input)
print(segsum(data))
elif args.action == 'trace':
data = _read(args.input)
print(trace(data))
elif args.action == 'compare':
before = _read(args.before)
after = _read(args.after)
_write(args.output, compare(before, after))
elif args.action == 'trace_plot':
data = _read(args.input)
_write(args.output, trace_plot(data, device=args.device, plot_segments=args.segments))
elif args.action == 'segment_plot':
data = _read(args.input)
_write(args.output, segment_plot(data, device=args.device))