mirror of
https://github.com/boykopovar/AnyPS5.git
synced 2026-10-22 08:33:12 -05:00
316 lines
17 KiB
Python
316 lines
17 KiB
Python
import argparse
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import bisect
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import collections
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import gzip
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import json
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import sys
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from pathlib import Path
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DESCRIPTION = """Digest of an APS5_TRACE_FRAMES / APS5_TRACE_SECONDS capture (aps5-trace-*.json.gz) for an issue or a
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pull request: the summary line, the slowest frames with their largest slices, the busy and wait time of each
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thread, the largest slices per frame, the longest waits, the largest GPU batches with the CPU slices that
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recorded them (when the trace carries batch ids) and, with --stacks, the hottest functions of an
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APS5_SAMPLE_WORKER collapsed-stack file. The summary line is read from aps5-summary.jsonl next to the trace
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unless --summary names another file.
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A summary file written by APS5_TRACE_SUMMARY_EVERY (aps5-summary.jsonl, or any .jsonl) is digested as a table with
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one row per interval and a text sparkline of the frame p99 and of the GPU busy time per present; the lines of
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window captures in the same file are skipped."""
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RAMP = "._:-=+*#%@"
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def is_wait(name):
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return "wait" in name.rsplit(".", 1)[-1].lower()
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class Trace:
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def __init__(self, document):
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self.threads = {}
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self.slices = collections.defaultdict(list)
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self.presents = []
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self.folded = collections.defaultdict(float)
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self.batched = []
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for event in document["traceEvents"]:
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phase = event.get("ph")
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if phase == "M" and event.get("name") == "thread_name":
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self.threads[event["tid"]] = event["args"]["name"]
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elif phase == "X":
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self.slices[event["tid"]].append([float(event["ts"]), float(event["dur"]), event["name"], 0, False])
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batch = event.get("args", {}).get("batch")
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if batch:
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self.batched.append((event["tid"], float(event["ts"]), float(event["dur"]), event["name"], batch))
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elif phase == "i" and event.get("name") == "present":
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self.presents.append((float(event["ts"]), event.get("args", {}).get("present")))
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elif phase == "C":
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self.folded[event["tid"]] += sum(float(value) for value in event.get("args", {}).values())
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self.presents.sort()
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for slices in self.slices.values():
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slices.sort(key=lambda entry: (entry[0], -entry[1]))
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stack = []
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for entry in slices:
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start = round(entry[0] * 1000)
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while stack and start >= round(stack[-1][0] * 1000) + round(stack[-1][1] * 1000):
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stack.pop()
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entry[3] = len(stack)
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entry[4] = any(is_wait(parent[2]) for parent in stack)
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stack.append(entry)
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def name(self, tid):
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return self.threads.get(tid, f"tid {tid}")
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def frames(self, end):
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starts = [ts for ts, _ in self.presents]
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if end is None:
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end = starts[-1] if starts else 0.0
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bounds = starts + ([end] if not starts or end > starts[-1] else [])
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return [(index, bounds[index], bounds[index + 1], self.presents[index][1]) for index in range(len(bounds) - 1)]
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def load_trace(path):
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opener = gzip.open if str(path).endswith(".gz") else open
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with opener(path, "rt", encoding="utf-8") as handle:
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return Trace(json.load(handle))
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def load_summary(path, trace_path):
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candidate = Path(path) if path else Path(trace_path).with_name("aps5-summary.jsonl")
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if not candidate.exists():
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return None
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lines = [json.loads(line) for line in candidate.read_text(encoding="utf-8").splitlines() if line.strip()]
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for line in reversed(lines):
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if line.get("trace") == Path(trace_path).name:
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return line
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return None
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def load_stacks(path):
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stacks = []
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for line in Path(path).read_text(encoding="utf-8").splitlines():
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if line.startswith("#") or not line.strip():
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continue
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frames, _, count = line.rpartition(" ")
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stacks.append((frames.split(";"), int(count)))
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return stacks
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def digest(trace, summary=None, stacks=None, worst=5, top=10):
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window_ms = summary["window"]["ms"] if summary else None
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frames = trace.frames(window_ms * 1000.0 if window_ms is not None else None)
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span_us = frames[-1][2] - frames[0][1] if frames else 0.0
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count = max(len(frames), 1)
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result = {"summary": summary, "frames": len(frames), "window_ms": span_us / 1000.0}
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slowest = []
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for index, begin, end, present in sorted(frames, key=lambda frame: frame[1] - frame[2])[:worst]:
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inside = collections.Counter()
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for tid, slices in trace.slices.items():
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for ts, dur, name, _, _ in slices:
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overlap = min(ts + dur, end) - max(ts, begin)
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if overlap > 0:
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inside[(name, trace.name(tid))] += overlap
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slowest.append({"present": present, "ms": (end - begin) / 1000.0, "at_ms": begin / 1000.0,
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"slices": [{"name": name, "thread": thread, "ms": us / 1000.0} for (name, thread), us in inside.most_common(4)]})
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result["slowest_frames"] = slowest
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threads = []
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totals = collections.Counter()
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waits = []
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for tid, slices in sorted(trace.slices.items()):
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traced = sum(dur for _, dur, _, depth, _ in slices if depth == 0)
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waited = sum(dur for _, dur, name, _, nested in slices if is_wait(name) and not nested)
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leaders = collections.Counter()
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for ts, dur, name, depth, nested in slices:
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totals[name] += dur
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if depth == 0 and not is_wait(name):
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leaders[name] += dur
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if is_wait(name) and not nested:
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waits.append((dur, name, trace.name(tid), ts))
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threads.append({"thread": trace.name(tid), "busy_ms_per_frame": (traced - waited) / 1000.0 / count,
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"wait_ms_per_frame": waited / 1000.0 / count,
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"untraced_ms_per_frame": max(span_us - traced, 0.0) / 1000.0 / count,
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"folded_ms_per_frame": trace.folded.get(tid, 0.0) / count,
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"busy_pct": 100.0 * (traced - waited) / span_us if span_us else 0.0,
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"wait_pct": 100.0 * waited / span_us if span_us else 0.0,
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"top": [{"name": name, "ms_per_frame": us / 1000.0 / count} for name, us in leaders.most_common(2)]})
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result["threads"] = threads
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result["top_slices_ms_per_frame"] = [{"name": name, "ms": us / 1000.0 / count} for name, us in totals.most_common(top)]
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starts = [ts for ts, _ in trace.presents]
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result["longest_waits"] = [{"name": name, "thread": thread, "ms": dur / 1000.0, "at_ms": ts / 1000.0,
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"present": trace.presents[bisect.bisect_right(starts, ts) - 1][1] if starts and ts >= starts[0] else None}
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for dur, name, thread, ts in sorted(waits, reverse=True)[:top]]
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gpu = {tid for tid, name in trace.threads.items() if name == "gpu"}
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batches = [entry for entry in trace.batched if entry[0] in gpu and entry[3] == "batch"]
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if batches:
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ranges = collections.defaultdict(collections.Counter)
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recorded = collections.defaultdict(collections.Counter)
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for tid, ts, dur, name, batch in trace.batched:
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if tid in gpu:
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if name != "batch":
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ranges[batch][name] += dur
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else:
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recorded[batch][(name, trace.name(tid))] += dur
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result["largest_gpu_batches"] = [{"batch": batch, "ms": dur / 1000.0, "at_ms": ts / 1000.0,
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"present": trace.presents[bisect.bisect_right(starts, ts) - 1][1] if starts and ts >= starts[0] else None,
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"gpu_ranges": [{"name": name, "ms": us / 1000.0} for name, us in ranges[batch].most_common(3)],
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"recorded_by": [{"name": name, "thread": thread, "ms": us / 1000.0} for (name, thread), us in recorded[batch].most_common(4)]}
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for _, ts, dur, _, batch in sorted(batches, key=lambda entry: -entry[2])[:worst]]
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if stacks is not None:
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samples = sum(count for _, count in stacks)
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own = collections.Counter()
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inclusive = collections.Counter()
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truncated = 0
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common = None
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for frames_of, number in stacks:
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if frames_of[0] == "[truncated]":
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truncated += number
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frames_of = frames_of[1:]
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elif frames_of:
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common = set(frames_of) if common is None else common & set(frames_of)
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if not frames_of:
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continue
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own[frames_of[-1]] += number
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for frame in set(frames_of):
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inclusive[frame] += number
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common = common or set()
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for frame in common:
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del inclusive[frame]
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share = lambda value: 100.0 * value / samples if samples else 0.0
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result["stacks"] = {"samples": samples, "truncated_pct": share(truncated), "common_frames": len(common),
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"self": [{"frame": frame, "pct": share(value)} for frame, value in own.most_common(top)],
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"total": [{"frame": frame, "pct": share(value)} for frame, value in inclusive.most_common(top)]}
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return result
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def markdown(result):
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lines = []
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summary = result["summary"]
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if summary:
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frame = summary["frame_ms"]
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lines.append(f"**{summary['window']['spec']}** from present {summary['window']['first_present']}: {summary['presents']} presents, "
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f"frame p50 {frame['p50']:.1f} / p90 {frame['p90']:.1f} / p99 {frame['p99']:.1f} / max {frame['max']:.1f} ms, "
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f"GPU busy {summary['gpu_busy_ms_per_present']:.1f} ms per present")
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lines.append(f"{summary['gpu']}, {summary['driver']}, {summary['os']}, {summary['resolution']}, commit {summary['commit']}"
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f"{' (dirty)' if summary.get('dirty') else ''}, dropped: {'yes' if summary['trace_dropped'] else 'no'}")
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lines.append("")
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lines.append("```json")
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lines.append(json.dumps(summary, separators=(",", ":")))
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lines.append("```")
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else:
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lines.append(f"{result['frames']} frames over {result['window_ms']:.1f} ms (no summary line found)")
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lines.append("")
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lines.append("**Slowest frames** (largest slices inside each, inclusive)")
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lines.append("")
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for frame in result["slowest_frames"]:
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parts = ", ".join(f"{entry['name']} ({entry['thread']}) {entry['ms']:.1f}" for entry in frame["slices"])
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lines.append(f"- present {frame['present']}: {frame['ms']:.1f} ms at {frame['at_ms']:.0f} ms: {parts}")
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lines.append("")
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lines.append("**Threads** (ms per frame)")
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lines.append("")
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lines.append("| thread | busy | wait | untraced | in slices < 20 us | largest |")
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lines.append("|---|---|---|---|---|---|")
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for thread in result["threads"]:
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largest = ", ".join(f"{entry['name']} {entry['ms_per_frame']:.1f}" for entry in thread["top"])
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lines.append(f"| {thread['thread']} | {thread['busy_ms_per_frame']:.1f} ({thread['busy_pct']:.0f} %) | {thread['wait_ms_per_frame']:.1f} ({thread['wait_pct']:.0f} %) "
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f"| {thread['untraced_ms_per_frame']:.1f} | {thread['folded_ms_per_frame']:.1f} | {largest} |")
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lines.append("")
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lines.append("**Largest slices** (ms per frame, inclusive, all threads): " + ", ".join(f"{entry['name']} {entry['ms']:.1f}" for entry in result["top_slices_ms_per_frame"]))
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lines.append("")
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lines.append("**Longest waits**")
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lines.append("")
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for wait in result["longest_waits"]:
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lines.append(f"- {wait['name']} ({wait['thread']}) {wait['ms']:.1f} ms at {wait['at_ms']:.0f} ms, present {wait['present']}")
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if "largest_gpu_batches" in result:
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lines.append("")
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lines.append("**Largest GPU batches** (GPU ranges inside, and the CPU slices that recorded each, inclusive)")
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lines.append("")
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for batch in result["largest_gpu_batches"]:
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ranges = ", ".join(f"{entry['name']} {entry['ms']:.1f}" for entry in batch["gpu_ranges"]) or "no ranges"
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recorded = ", ".join(f"{entry['name']} ({entry['thread']}) {entry['ms']:.1f}" for entry in batch["recorded_by"]) or "no CPU slices"
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lines.append(f"- batch {batch['batch']}: {batch['ms']:.1f} ms at {batch['at_ms']:.0f} ms, present {batch['present']}: GPU {ranges}; recorded by {recorded}")
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if "stacks" in result:
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stacks = result["stacks"]
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lines.append("")
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lines.append(f"**Worker stacks** ({stacks['samples']} samples, {stacks['truncated_pct']:.1f} % truncated; total leaves out the {stacks['common_frames']} frames every complete stack shares)")
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lines.append("")
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lines.append("- self: " + ", ".join(f"{entry['frame']} {entry['pct']:.1f} %" for entry in stacks["self"]))
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lines.append("- total: " + ", ".join(f"{entry['frame']} {entry['pct']:.1f} %" for entry in stacks["total"]))
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return "\n".join(lines) + "\n"
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def load_intervals(path):
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lines = [json.loads(line) for line in Path(path).read_text(encoding="utf-8").splitlines() if line.strip()]
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return [line for line in lines if "interval_start_s" in line]
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def sparkline(values):
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high = max(values, default=0.0)
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if high <= 0:
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return RAMP[0] * len(values)
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return "".join(RAMP[min(len(RAMP) - 1, int(value / high * (len(RAMP) - 1) + 0.5))] for value in values)
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def digest_intervals(lines):
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rows = []
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for line in lines:
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workers = line.get("worker_busy_pct", {})
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busiest = max(workers.items(), key=lambda entry: entry[1]) if workers else None
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phases = list(line.get("top_phases_ms_per_frame", {}).items())
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rows.append({"start_s": line["interval_start_s"], "end_s": line["interval_end_s"], "presents": line["presents"],
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"frame_ms": line["frame_ms"], "gpu_busy_ms_per_present": line["gpu_busy_ms_per_present"],
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"busiest_worker": {"thread": busiest[0], "pct": busiest[1]} if busiest else None,
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"top_phase": {"name": phases[0][0], "ms_per_frame": phases[0][1]} if phases else None,
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"trace_dropped": line.get("trace_dropped", False)})
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first = lines[0] if lines else {}
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keys = ("commit", "dirty", "env", "gpu", "driver", "os", "resolution")
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changed = [index for index, line in enumerate(lines) if any(line.get(key) != first.get(key) for key in keys)]
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return {"intervals": rows, "identity": {key: first.get(key) for key in keys}, "identity_changed_at": changed}
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def markdown_intervals(result):
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rows = result["intervals"]
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identity = result["identity"]
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if not rows:
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return "no interval lines (interval_start_s) found\n"
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lines = [f"**{len(rows)} intervals**, {rows[0]['start_s']:.1f} to {rows[-1]['end_s']:.1f} s, {sum(row['presents'] for row in rows)} presents",
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f"{identity['gpu']}, {identity['driver']}, {identity['os']}, {identity['resolution']}, commit {identity['commit']}"
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f"{' (dirty)' if identity.get('dirty') else ''}"]
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if result["identity_changed_at"]:
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lines.append("commit, environment, GPU, driver or OS changed at interval " + ", ".join(str(index + 1) for index in result["identity_changed_at"]))
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lines += ["", "| # | seconds | presents | p50 | p90 | p99 | max | GPU busy | busiest worker | largest phase |", "|---|---|---|---|---|---|---|---|---|---|"]
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for index, row in enumerate(rows, 1):
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frame = row["frame_ms"]
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worker = f"{row['busiest_worker']['pct']:.0f} %" if row["busiest_worker"] else "-"
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phase = f"{row['top_phase']['name']} {row['top_phase']['ms_per_frame']:.1f}" if row["top_phase"] else "-"
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lines.append(f"| {index} | {row['start_s']:.0f}-{row['end_s']:.0f}{' (dropped)' if row['trace_dropped'] else ''} | {row['presents']} | {frame['p50']:.1f} | {frame['p90']:.1f} "
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f"| {frame['p99']:.1f} | {frame['max']:.1f} | {row['gpu_busy_ms_per_present']:.1f} | {worker} | {phase} |")
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p99 = [row["frame_ms"]["p99"] for row in rows]
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gpu = [row["gpu_busy_ms_per_present"] for row in rows]
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lines += ["", "```", f"p99 ms {sparkline(p99)} 0-{max(p99):.1f}", f"gpu ms {sparkline(gpu)} 0-{max(gpu):.1f}", "```"]
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return "\n".join(lines) + "\n"
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def main(arguments=None):
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parser = argparse.ArgumentParser(description=DESCRIPTION)
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parser.add_argument("trace", help="aps5-trace-*.json.gz (or .json), or a .jsonl summary file with APS5_TRACE_SUMMARY_EVERY lines")
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parser.add_argument("--summary", help="file with the summary line (default: aps5-summary.jsonl next to the trace)")
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parser.add_argument("--stacks", help="collapsed-stack file of APS5_SAMPLE_WORKER (<file stem>.collapsed.txt)")
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parser.add_argument("--frames", type=int, default=5, help="slowest frames to list (default 5)")
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parser.add_argument("--top", type=int, default=10, help="entries per list (default 10)")
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parser.add_argument("--json", action="store_true", help="print the digest as JSON")
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options = parser.parse_args(arguments)
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if options.trace.endswith(".jsonl"):
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result = digest_intervals(load_intervals(options.trace))
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sys.stdout.write(json.dumps(result, indent=1) + "\n" if options.json else markdown_intervals(result))
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return 0
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result = digest(load_trace(options.trace), load_summary(options.summary, options.trace),
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load_stacks(options.stacks) if options.stacks else None, options.frames, options.top)
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sys.stdout.write(json.dumps(result, indent=1) + "\n" if options.json else markdown(result))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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