lucifer1004/veloq/pytorch-profile-analysis
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Analyze single-file PyTorch/Kineto Chrome trace `.json(.gz)` files using the VeloQ CLI. Use for CPU/CUDA/kernel correlation, ProfilerStep/annotation slicing, memory/shape grouping, and single-trace NCCL evidence.
Install
npx vskill@latest install lucifer1004/veloq/pytorch-profile-analysisSKILL.md
PyTorch Profile Analysis
Use veloq pytorch for PyTorch/Kineto Chrome traces:
This skill requires the VeloQ CLI on PATH. If veloq is missing, install it before analysis.
Tool Boundary
Use veloq pytorch verbs as the analysis interface. Do not query <input>.veloq/pytorch/ sidecars, generated Parquet files, or raw Kineto trace tables directly with DuckDB, PyArrow, pandas, or ad hoc SQL unless the user explicitly asks for raw-trace exploration or you are developing VeloQ itself.
veloq pytorch prep T only builds/checks sidecars. After prep, continue with summary, search, inspect, stats, correlate, timeline, slices, or collectives.
Inputs
- Explicit
veloq pytorchcommands accept one Chrome trace named.json
or .json.gz.
- Automatic source detection only claims
.pt.trace.jsonand
.pt.trace.json.gz; explicitly select pytorch for other JSON filenames.
- Directory inputs are not supported in PyTorch v0. Ask the user to
choose one trace file if they point at a directory.
Row IDs
PyTorch row ids use <kind>:<stable_index>, where the stable index is derived from the original traceEvents order after non-event flow markers are skipped. Do not use Kineto Ev Idx as a stable key. Use veloq pytorch schema <target> for the authoritative response field inventory; do not infer the public contract from raw Kineto fields.
Common prefixes:
| Type | Row id prefix | |
|---|---|---|
| CPU op | cpu_op:N | |
| Annotation | annotation:N | |
| Step | step:N | |
| Runtime | runtime:N | |
| Driver | driver:N | |
| Kernel | kernel:N | |
| Memcpy | memcpy:N | |
| Memset | memset:N | |
| Memory | memory:N | |
| Python | python:N | |
| Comm | comm:N |
Workflow
- Inventory first:
``bash veloq pytorch summary T ``
Read data.auxiliary.capabilities before choosing a path.
- Find events:
``bash veloq pytorch search T --type cpu-op --name '*aten::*' --limit 20 veloq pytorch search T --type kernel --is-comm --limit 20 ``
- Drill into one event:
``bash veloq pytorch inspect T ROW_ID ``
Inspect returns raw args, typed args, parent/children, enclosing step, and link metadata.
- Answer launch-cause questions:
``bash veloq pytorch correlate T kernel:91 ``
Read data.rows[0].events[] for the CPU op, annotation/step, runtime/driver, and GPU activity chain.
- Attribute CPU overhead to Python context when captured:
Traces exported from torch.profiler.profile(..., with_stack=True) include python_function events. inspect returns python_context / python_stack, and stats can group CPU work by python-context or python-path:
``bash veloq pytorch stats T --type cpu-op --group-by python-path,name --limit 20 veloq pytorch inspect T cpu_op:42 ``
- Slice ProfilerStep/user annotation ranges:
slices --from/--to selects ranges that overlap the time window and clips attributed GPU/comm time to that window. Slice row start_ns and duration_ns remain the original trace range so the row id still points to the inspectable event.
- For communication questions, stay within one trace file:
``bash veloq pytorch stats T --type comm --group-by comm-kind,rank veloq pytorch search T --type kernel --is-comm --limit 20 ``
collectives groups single-trace communication evidence and reports linked CPU/NCCL row ids. If a trace file contains multiple rank values, rank-scoped commands (search, stats, timeline, slices, and collectives) require --rank <n> or --all-ranks. inspect and correlate operate on explicit row ids and are not rank-scope gated. Device ids are rank-local and stream ids are device-local: filter a stream with --rank <n> --device <id> --stream <id>, or compare lanes with --group-by rank,device,stream. VeloQ does not compute cross-rank skew in PyTorch v0:
``bash veloq pytorch collectives T ``
Event Types
--type accepts cpu-op, annotation, step, runtime, driver, kernel, memcpy, memset, memory, python, comm, or all.
comm is a communication-related set. Use --type kernel --is-comm to focus on NCCL kernels.
Limits
PyTorch support is experimental (source.version = "v0"). Classification is based on Kineto category/name/arg conventions and may need extension for profiler variants not yet represented by tests. Treat documented fields, schema targets, row ids/keys, command ids, and output modes as the versioned source contract even while the source remains v0.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugins/veloq/skills/pytorch-profile-analysis/SKILL.md
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