fr-analysis

name	fr-analysis
description	Analyze PyTorch NCCL flight-recorder (FR) dumps to identify collective operation hangs and isolate the responsible ranks using CollectiveAnalyzer. Use when a distributed training job hangs due to an NCCL collective timeout and FR dump files are available. Detects the wavefront process group where collectives diverge and returns the root-cause suspect ranks.
compatibility	Requires PyTorch NCCL FR dumps (TORCH_NCCL_TRACE_BUFFER_SIZE > 0 must be set during training). LLM_API_KEY and langchain-openai are required only when using --llm-analyze.
metadata	{"entry-point":"CollectiveAnalyzer","script":"scripts/fr_attribution.py"}

Skill: fr_analysis

Analyze PyTorch NCCL flight-recorder (FR) dumps to identify the collective operation hang and isolate the ranks responsible, using CollectiveAnalyzer.

Script: scripts/fr_attribution.py → attribution/trace_analyzer/fr_attribution.py

---

What it does

1. Loads all FR dump files matching a glob pattern under --fr-path.
2. Parses each dump into Collective records (op type, ranks, process group, timing, state).
3. Groups collectives by process group and sequence ID across ranks to detect mismatches.
4. Identifies the wavefront — the process group boundary where collectives diverge — and returns the missing ranks at that boundary as the root-cause suspects.
5. Optionally runs an LLM pass (--llm-analyze) over the structured findings for a human-readable summary.

---

CLI

python scripts/fr_attribution.py \
    --fr-path /path/to/fr_dumps/ \
    [-p "_dump_*"] \
    [--verbose] \
    [--health-check] \
    [--llm-analyze] \
    [--model MODEL] \
    [--debug]

Flag	Default	Description
--fr-path	required	Path to a directory (or single file) containing FR dump files
--pattern, -p	_dump_*	Glob pattern for dump files within --fr-path
--verbose, -v	off	Print detailed per-rank collective tables
--health-check, -c	off	Include node health check results in output
--llm-analyze, -l	off	Pass structured findings to the LLM for a narrative summary
--model, -m	nvidia/nemotron-3-super-120b-a12b	LLM model (only used with --llm-analyze)
--debug	off	Convert binary trace files to JSON for inspection

---

Programmatic API

from nvidia_resiliency_ext.attribution.trace_analyzer.fr_attribution import CollectiveAnalyzer

analyzer = CollectiveAnalyzer({
    "fr_path": "/path/to/fr_dumps/",
    "pattern": "_dump_*",
    "verbose": False,
    "health_check": False,
    "llm_analyze": False,
    "model": "nvidia/nemotron-3-super-120b-a12b",
})
results = analyzer.run_sync({
    "fr_path": "/path/to/fr_dumps/",
})
# results: tuple[FRAnalysisResult | str, AttributionState]

---

Output

Returns (result, AttributionState) where result is the FR analysis table and describes:

• The selected wavefront/front process group
• Missing ranks at that process group (root-cause suspects)
• Per-rank collective status tables (when --verbose)
• Node health summary (when --health-check)
• LLM narrative (when --llm-analyze)

AttributionState.STOP indicates the hang is unrecoverable; CONTINUE indicates the job may be restartable after isolating the identified ranks.

---

Dump file formats

Format	Notes
_dump_* files	PyTorch FR dump prefix pattern used by the feedback loop
Binary pickle / JSON payloads	Detected automatically; use --debug to convert binary traces to JSON

FR dumps are typically written to the directory specified by TORCH_NCCL_DEBUG_INFO_TEMP_FILE or triggered automatically on NCCL timeout.

---

Prerequisites

• FR dump files produced by PyTorch NCCL (set TORCH_NCCL_TRACE_BUFFER_SIZE > 0)
• LLM_API_KEY required only when using --llm-analyze
• langchain-openai required only when using --llm-analyze
• FR_DEBUG=1 env var enables verbose debug logging in the script