mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
6f07c57416
This updates the JS format of MemoryViz.js to match internal format. It also moves the style sheet into the JS so it is easier package for both oss and internal use. Pull Request resolved: https://github.com/pytorch/pytorch/pull/106482 Approved by: https://github.com/aaronenyeshi ghstack dependencies: #106328
624 lines
24 KiB
Python
624 lines
24 KiB
Python
import pickle
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import sys
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import os
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import io
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import subprocess
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import json
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from functools import lru_cache
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from typing import Any
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from itertools import groupby
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import base64
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import warnings
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cache = lru_cache(None)
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__all__ = ["format_flamegraph", "segments", "memory", "compare"]
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def _frame_fmt(f, full_filename=False):
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i = f['line']
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fname = f['filename']
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if not full_filename:
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fname = fname.split('/')[-1]
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func = f['name']
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return f'{fname}:{i}:{func}'
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@cache
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def _frame_filter(name, filename):
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omit_functions = [
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"unwind::unwind",
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"CapturedTraceback::gather",
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"gather_with_cpp",
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"_start",
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"__libc_start_main",
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"PyEval_",
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"PyObject_",
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"PyFunction_",
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]
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omit_filenames = [
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"core/boxing",
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"/Register",
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"/Redispatch",
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"pythonrun.c",
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"Modules/main.c",
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"Objects/call.c",
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"Objects/methodobject.c",
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"pycore_ceval.h",
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"ceval.c",
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"cpython/abstract.h",
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]
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for of in omit_functions:
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if of in name:
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return False
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for of in omit_filenames:
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if of in filename:
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return False
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return True
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def _frames_fmt(frames, full_filename=False, reverse=False):
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if reverse:
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frames = reversed(frames)
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return [_frame_fmt(f, full_filename) for f in frames if _frame_filter(f['name'], f['filename'])]
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def _block_extra_legacy(b):
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if 'history' in b:
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frames = b['history'][0].get('frames', [])
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real_size = b['history'][0]['real_size']
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else:
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real_size = b.get('requested_size', b['size'])
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frames = []
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return frames, real_size
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def _block_extra(b):
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if 'frames' not in b:
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# old snapshot format made it more complicated to get frames/allocated size
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return _block_extra_legacy(b)
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return b['frames'], b['requested_size']
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def format_flamegraph(flamegraph_lines, flamegraph_script=None):
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if flamegraph_script is None:
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flamegraph_script = f'/tmp/{os.getuid()}_flamegraph.pl'
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if not os.path.exists(flamegraph_script):
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import urllib.request
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print(f"Downloading flamegraph.pl to: {flamegraph_script}")
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urllib.request.urlretrieve(
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'https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl', flamegraph_script)
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subprocess.run(['chmod', '+x', flamegraph_script])
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args = [flamegraph_script, '--countname', 'bytes']
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p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8')
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assert p.stdin is not None
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assert p.stdout is not None
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p.stdin.write(flamegraph_lines)
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p.stdin.close()
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result = p.stdout.read()
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p.stdout.close()
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p.wait()
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assert p.wait() == 0
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return result
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def _write_blocks(f, prefix, blocks):
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def frames_fragment(frames):
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if not frames:
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return "<non-python>"
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return ';'.join(_frames_fmt(frames, reverse=True))
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for b in blocks:
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if 'history' not in b:
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frames, accounted_for_size = _block_extra(b)
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f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {accounted_for_size}\n')
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else:
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accounted_for_size = 0
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for h in b['history']:
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sz = h['real_size']
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accounted_for_size += sz
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if 'frames' in h:
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frames = h['frames']
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f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {sz}\n')
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else:
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f.write(f'{prefix};{b["state"]};<no-context> {sz}\n')
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gaps = b['size'] - accounted_for_size
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if gaps:
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f.write(f'{prefix};{b["state"]};<gaps> {gaps}\n')
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def segments(snapshot, format_flamegraph=format_flamegraph):
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f = io.StringIO()
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for seg in snapshot['segments']:
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prefix = f'stream_{seg["stream"]};seg_{seg["address"]}'
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_write_blocks(f, prefix, seg['blocks'])
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return format_flamegraph(f.getvalue())
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def memory(snapshot, format_flamegraph=format_flamegraph):
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f = io.StringIO()
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for seg in snapshot['segments']:
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prefix = f'stream_{seg["stream"]}'
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_write_blocks(f, prefix, seg['blocks'])
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return format_flamegraph(f.getvalue())
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def compare(before, after, format_flamegraph=format_flamegraph):
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def _seg_key(seg):
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return (seg['address'], seg['total_size'])
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def _seg_info(seg):
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return f'stream_{seg["stream"]};seg_{seg["address"]}'
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f = io.StringIO()
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before_segs = {_seg_key(seg) for seg in before}
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after_segs = {_seg_key(seg) for seg in after}
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print(f'only_before = {[a for a,_ in (before_segs - after_segs)]}')
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print(f'only_after = {[a for a,_ in (after_segs - before_segs)]}')
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for seg in before:
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if _seg_key(seg) not in after_segs:
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_write_blocks(f, f'only_before;{_seg_info(seg)}', seg['blocks'])
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for seg in after:
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if _seg_key(seg) not in before_segs:
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_write_blocks(f, f'only_after;{_seg_info(seg)}', seg['blocks'])
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return format_flamegraph(f.getvalue())
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def _format_size(num):
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# https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size
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for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
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if abs(num) < 1024.0:
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return f"{num:3.1f}{unit}B"
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num /= 1024.0
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return f"{num:.1f}YiB"
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class Bytes:
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def __init__(self, value):
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self.value = value
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def __add__(self, rhs):
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return Bytes(self.value + rhs)
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def __repr__(self):
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return _format_size(self.value)
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def calc_active(seg):
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return sum(b['size'] for b in seg['blocks'] if b['state'] == 'active_allocated')
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def _report_free(free_external, free_internal):
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total = free_external + free_internal
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suffix = ''
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if total != 0:
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pct = (free_internal / total) * 100
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suffix = f' ({pct:.1f}% internal)'
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return f'{Bytes(total)}{suffix}'
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PAGE_SIZE = 1024 * 1024 * 20
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legend = f"""\
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Legend:
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[a ] - a segment in the allocator
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^-- a page {Bytes(PAGE_SIZE)} of memory in the segment
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a-z: pages filled with a single block's content
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' ': page is completely free
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*: page if completely full with multiple blocks
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0-9: page is partially full with tensors of multiple blocks (9 == 90% full)
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(X% internal) - of the free memory, X% is free because we rounded the size of the allocation.
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"""
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def segsum(data):
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"""" Visually reports how the allocator has filled its segments. This printout can help debug fragmentation issues
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since free fragments will appear as gaps in this printout. The amount of free space is reported for each segment.
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We distinguish between internal free memory which occurs because the allocator rounds the allocation size, and
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external free memory, which are the gaps between allocations in a segment.
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Args:
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data: snapshot dictionary created from _snapshot()
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"""
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segments = []
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out = io.StringIO()
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out.write(f"Summary of segments >= {Bytes(PAGE_SIZE)} in size\n")
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total_reserved = 0
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total_allocated = 0
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free_external = 0
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free_internal = 0
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for seg in sorted(data['segments'], key=lambda x: (x['total_size'], calc_active(x))):
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total_reserved += seg['total_size']
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seg_free_external = 0
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seg_free_internal = 0
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seg_allocated = 0
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all_ranges = []
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boffset = 0
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for b in seg['blocks']:
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active = b['state'] == 'active_allocated'
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if active:
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_, allocated_size = _block_extra(b)
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all_ranges.append((boffset, allocated_size, True))
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seg_allocated += allocated_size
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seg_free_internal += b['size'] - allocated_size
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else:
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seg_free_external += b['size']
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boffset += b['size']
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total_allocated += seg_allocated
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free_external += seg_free_external
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free_internal += seg_free_internal
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nseg = (seg['total_size'] - 1) // PAGE_SIZE + 1
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occupied = [' ' for _ in range(nseg)]
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frac = [0.0 for _ in range(nseg)]
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active_size = 0
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for i, (start_, size, active) in enumerate(all_ranges):
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active_size += size
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finish_ = (start_ + size)
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start = start_ // PAGE_SIZE
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finish = (finish_ - 1) // PAGE_SIZE + 1
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m = chr(ord('a' if active else 'A') + (i % 26))
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for j in range(start, finish):
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s = max(start_, j * PAGE_SIZE)
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e = min(finish_, (j + 1) * PAGE_SIZE)
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frac[j] += (e - s) / PAGE_SIZE
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if occupied[j] != ' ':
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occupied[j] = '0123456789*'[int(frac[j] * 10)]
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else:
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occupied[j] = m
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stream = '' if seg['stream'] == 0 else f', stream_{seg["stream"]}'
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body = ''.join(occupied)
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assert seg_free_external + seg_free_internal + seg_allocated == seg['total_size']
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stream = f' stream_{seg["stream"]}' if seg['stream'] != 0 else ''
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if seg['total_size'] >= PAGE_SIZE:
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out.write(f'[{body}] {Bytes(seg["total_size"])} allocated, '
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f'{_report_free(seg_free_external, seg_free_internal)} free{stream}\n')
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out.write(f'segments: {len(data["segments"])}\n')
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out.write(f'total_reserved: {Bytes(total_reserved)}\n')
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out.write(f'total_allocated: {Bytes(total_allocated)}\n')
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internal_external = f' ({Bytes(free_internal)} internal + {Bytes(free_external)} external)' if free_internal else ''
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out.write(f'total_free: {_report_free(free_external, free_internal)}\n')
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out.write(legend)
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assert free_internal + free_external + total_allocated == total_reserved
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return out.getvalue()
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def trace(data):
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out = io.StringIO()
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def format(entries):
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segment_intervals : list = []
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segment_addr_to_name = {}
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allocation_addr_to_name = {}
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free_names : list = []
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next_name = 0
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def _name():
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nonlocal next_name
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if free_names:
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return free_names.pop()
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r, m = next_name // 26, next_name % 26
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next_name += 1
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return f'{chr(ord("a") + m)}{"" if r == 0 else r}'
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def find_segment(addr):
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for name, saddr, size in segment_intervals:
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if addr >= saddr and addr < saddr + size:
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return name, saddr
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for i, seg in enumerate(data['segments']):
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saddr = seg['address']
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size = seg['allocated_size']
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if addr >= saddr and addr < saddr + size:
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return f'seg_{i}', saddr
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return None, None
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count = 0
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out.write(f'{len(entries)} entries\n')
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total_reserved = 0
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for seg in data['segments']:
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total_reserved += seg['total_size']
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for count, e in enumerate(entries):
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if e['action'] == 'alloc':
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addr, size = e['addr'], e['size']
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n = _name()
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seg_name, seg_addr = find_segment(addr)
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if seg_name is None:
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seg_name = "MEM"
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offset = addr
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else:
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offset = addr - seg_addr
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out.write(f'{n} = {seg_name}[{offset}:{Bytes(size)}]\n')
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allocation_addr_to_name[addr] = (n, size, count)
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count += size
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elif e['action'] == 'free_requested':
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addr, size = e['addr'], e['size']
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name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
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out.write(f'del {name} # {Bytes(size)}\n')
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elif e['action'] == 'free_completed':
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addr, size = e['addr'], e['size']
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count -= size
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name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
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out.write(f'# free completed for {name} {Bytes(size)}\n')
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if name in allocation_addr_to_name:
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free_names.append(name)
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del allocation_addr_to_name[name]
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elif e['action'] == 'segment_alloc':
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addr, size = e['addr'], e['size']
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name = _name()
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out.write(f'{name} = cudaMalloc({addr}, {Bytes(size)})\n')
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segment_intervals.append((name, addr, size))
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segment_addr_to_name[addr] = name
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elif e['action'] == 'segment_free':
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addr, size = e['addr'], e['size']
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name = segment_addr_to_name.get(addr, addr)
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out.write(f'cudaFree({name}) # {Bytes(size)}\n')
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if name in segment_addr_to_name:
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free_names.append(name)
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del segment_addr_to_name[name]
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elif e['action'] == 'oom':
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size = e['size']
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free = e['device_free']
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out.write(f'raise OutOfMemoryError() # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n')
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else:
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out.write(f'{e}\n')
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out.write(f"TOTAL MEM: {Bytes(count)}")
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for i, d in enumerate(data['device_traces']):
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if d:
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out.write(f'Device {i} ----------------\n')
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format(d)
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return out.getvalue()
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_memory_viz_template = r"""
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<!DOCTYPE html>
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<html>
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<head>
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</head>
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<body>
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<script type="module">
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import {add_local_files} from "https://cdn.jsdelivr.net/gh/pytorch/pytorch@main/torch/utils/viz/MemoryViz.js"
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const local_files = $SNAPSHOT
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add_local_files(local_files, $VIZ_KIND)
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</script>
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</body>
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"""
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def _format_viz(data, viz_kind, device):
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if device is not None:
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warnings.warn('device argument is deprecated, plots now contain all device')
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buffer = pickle.dumps(data)
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buffer += b'\x00' * (3 - len(buffer) % 3)
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# Encode the buffer with base64
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encoded_buffer = base64.b64encode(buffer).decode('utf-8')
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json_format = json.dumps([{"name": 'snapshot.pickle', "base64": encoded_buffer}])
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return _memory_viz_template.replace('$VIZ_KIND', repr(viz_kind)) \
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.replace('$SNAPSHOT', json_format)
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def trace_plot(data, device=None, plot_segments=False):
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"""Generate a visualization over time of the memory usage recorded by the trace as an html file.
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Args:
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data: Memory snapshot as generated from torch.cuda.memory._snapshot()
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device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
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plot_segments (bool, optional): Plots memory returned from cudaMalloc, rather than individual allocations.
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Defaults to False.
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Returns:
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str: HTML of visualization
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"""
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return _format_viz(data, 'Active Memory Timeline' if not plot_segments else 'Active Cached Memory Timeline', device)
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def _profile_to_snapshot(profile):
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import torch
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from torch.profiler._memory_profiler import Action, TensorKey
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from torch._C._profiler import _EventType
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memory_profile = profile._memory_profile()
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allocation_stacks = {}
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for event in memory_profile._op_tree.sorted_nodes:
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if event.tag == _EventType.Allocation:
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parent = event.parent
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python_parents = []
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while parent:
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if parent.tag in (_EventType.PyCall, _EventType.PyCCall):
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python_parents.append(parent)
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parent = parent.parent
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key = TensorKey.from_allocation(event.extra_fields)
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# Corner case: If allocation doesn't have an ID (can't prove it was used as a Tensor)
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# key will be None. I should add some way to identify these, I just haven't yet.
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if key and event.extra_fields.alloc_size > 0:
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allocation_stacks[key] = python_parents
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device_count = torch.cuda.device_count()
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snapshot = {
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'device_traces': [[] for _ in range(device_count + 1)],
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'segments': [{'device': device,
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'address': None,
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'total_size': 0,
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'stream': 0,
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'blocks': []} for device in range(device_count + 1)]
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}
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def to_device(device):
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if device.type == 'cuda':
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return device.index
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else:
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return device_count
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def allocate(size, tensor_key, version, during_trace=True):
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device = to_device(tensor_key.device)
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addr = tensor_key.storage.ptr
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seg = snapshot['segments'][device] # type: ignore[index]
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if seg['address'] is None or seg['address'] > addr:
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seg['address'] = addr
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seg['total_size'] = max(seg['total_size'], addr + size) # record max addr for now, we will make it the size later
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category = memory_profile._categories.get(tensor_key, version)
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category = category.name.lower() if category is not None else "unknown"
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stack = allocation_stacks.get(tensor_key, ())
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stack = [{'filename': 'none', 'line': 0, 'name': p.name} for p in stack]
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r = {'action': 'alloc', 'addr': addr, 'size': size, 'stream': 0, 'frames': stack, 'category': category}
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if during_trace:
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snapshot['device_traces'][device].append(r) # type: ignore[index]
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return r
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|
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=lambda x: x[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))
|