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magpie-kernel-evaluator

价格未确认★ 332 GitHub Stars目录更新于 · 2026年9月13日agent-skill

概览

Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

Magpie

Use Magpie for three connected jobs:

Benchmark an inference workload and collect throughput, latency, and traces.
Analyze or compare GPU kernels for correctness and performance.
Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.

Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.

Choose the workflow

User goal	Workflow
Evaluate one implementation	`analyze`
Rank two or more implementations	`compare`
Measure model-serving performance	`benchmark`
Find expensive kernels in existing traces	standalone gap analysis
Explain a profiled inference workload	benchmark → TraceLens post-processing → stage/roofline review
Optimize an end-to-end workload	benchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark

Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.

Preflight

Locate the Magpie repository or installed package.

Check the local interface before constructing commands:

magpie --help
magpie analyze --help
magpie compare --help
magpie benchmark --help
magpie --gpu-info

Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.
Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.
Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.

Run from the Magpie repository root, install with pip install -e ., or use python -m Magpie when the magpie entry point is unavailable.

Analyze a kernel

Prefer a config when correctness or profiler settings matter:

magpie analyze --kernel-config path/to/kernel.yaml

For a quick single-kernel run:

magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"

Supported public kernel types are hip, cuda, pytorch, and triton. Use --no-perf only when the user wants correctness or execution validation without profiling.

Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.

Compare kernel variants

Compare at least two implementations and identify the baseline explicitly:

magpie compare --kernel-config path/to/compare.yaml

Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.

For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.

Benchmark inference

Prefer a checked-in benchmark config:

magpie benchmark --benchmark-config path/to/benchmark.yaml

The stable public CLI supports vllm, sglang, and atom. It supports direct docker and local run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.

Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.

Post-process traces with TraceLens

Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:

benchmark:
  profiler:
    torch_profiler:
      enabled: true
    tracelens:
      enabled: true
      analysis_mode: inference
      analysis_stages: all
      export_format: csv

Use analysis_mode: inference for vLLM/SGLang. It splits the rank-0 trace into prefilldecode, decode, and prefill stages when available, runs TraceLens post-processing, and writes full stage reports plus compact *_kernel_roofline_simple.csv files under the benchmark workspace's tracelens/ directory. For direct PyTorch trace reporting, use analysis_mode: pytorch.

Open the compact roofline CSVs first. Rank rows by kernel_time_ms_sum or time_pct; then use roofline_bound, arithmetic intensity, achieved TFLOP/s or TB/s, and pct_roofline_mean to form an optimization hypothesis. Confirm benchmark_report.json.tracelens_analysis has outputs and no error before treating post-processing as successful. Use analysis_mode: pytorch when the task specifically needs the legacy direct single-rank or multi-rank collective reports.

Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written analysis.md. If the user requests a prioritized agentic report, pass the captured trace to the separate tracelens-analysis-orchestrator skill when installed; keep that result distinct from Magpie's benchmark report.

Analyze existing traces and find source

Run standalone gap analysis with --trace-dir directly on benchmark:

magpie benchmark \
  --trace-dir path/to/torch_trace \
  --top-k 20 \
  --find-kernel-sources \
  --kernel-source-repos path/to/repository

Do not insert a gap-analysis positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.

Drive the optimization loop

Run an unprofiled baseline benchmark and save its config and report.
Repeat with torch profiling and TraceLens inference post-processing enabled.
Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
Run gap analysis over the representative steady-state window to rank concrete kernels.
Select bottlenecks by total contribution, not only single-dispatch duration.
Map the selected kernel to source and an executable testcase.
Generate isolated candidate implementations; preserve the baseline.
Use analyze for iteration, then compare with correctness gates to rank candidates.
Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.

Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.

Use MCP tools when available

Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.

Do not pass a CLI analyze_report.json wrapper directly to an MCP tool that expects one result object's performance_state and performance_result. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.

Additional resources

Full CLI reference: reference.md
Copy-paste command examples: examples.md

查看来源 ↗

文件元数据

name: magpie-kernel-evaluator
description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.

查看原始文本

---
name: magpie-kernel-evaluator
description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
---

# Magpie

Use Magpie for three connected jobs:

1. Benchmark an inference workload and collect throughput, latency, and traces.
2. Analyze or compare GPU kernels for correctness and performance.
3. Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.

Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.

## Choose the workflow

| User goal | Workflow |
|---|---|
| Evaluate one implementation | `analyze` |
| Rank two or more implementations | `compare` |
| Measure model-serving performance | `benchmark` |
| Find expensive kernels in existing traces | standalone gap analysis |
| Explain a profiled inference workload | benchmark → TraceLens post-processing → stage/roofline review |
| Optimize an end-to-end workload | benchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark |

Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.

## Preflight

1. Locate the Magpie repository or installed package.
2. Check the local interface before constructing commands:

   ```bash
   magpie --help
   magpie analyze --help
   magpie compare --help
   magpie benchmark --help
   magpie --gpu-info
   ```

3. Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.
4. Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.
5. Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.

Run from the Magpie repository root, install with `pip install -e .`, or use `python -m Magpie` when the `magpie` entry point is unavailable.

## Analyze a kernel

Prefer a config when correctness or profiler settings matter:

```bash
magpie analyze --kernel-config path/to/kernel.yaml
```

For a quick single-kernel run:

```bash
magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"
```

Supported public kernel types are `hip`, `cuda`, `pytorch`, and `triton`. Use `--no-perf` only when the user wants correctness or execution validation without profiling.

Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.

## Compare kernel variants

Compare at least two implementations and identify the baseline explicitly:

```bash
magpie compare --kernel-config path/to/compare.yaml
```

Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.

For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.

## Benchmark inference

Prefer a checked-in benchmark config:

```bash
magpie benchmark --benchmark-config path/to/benchmark.yaml
```

The stable public CLI supports `vllm`, `sglang`, and `atom`. It supports direct `docker` and `local` run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.

Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.

## Post-process traces with TraceLens

Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:

```yaml
benchmark:
  profiler:
    torch_profiler:
      enabled: true
    tracelens:
      enabled: true
      analysis_mode: inference
      analysis_stages: all
      export_format: csv
```

Use `analysis_mode: inference` for vLLM/SGLang. It splits the rank-0 trace into `prefilldecode`, `decode`, and `prefill` stages when available, runs TraceLens post-processing, and writes full stage reports plus compact `*_kernel_roofline_simple.csv` files under the benchmark workspace's `tracelens/` directory. For direct PyTorch trace reporting, use `analysis_mode: pytorch`.

Open the compact roofline CSVs first. Rank rows by `kernel_time_ms_sum` or `time_pct`; then use `roofline_bound`, arithmetic intensity, achieved TFLOP/s or TB/s, and `pct_roofline_mean` to form an optimization hypothesis. Confirm `benchmark_report.json.tracelens_analysis` has outputs and no error before treating post-processing as successful. Use `analysis_mode: pytorch` when the task specifically needs the legacy direct single-rank or multi-rank collective reports.

Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written `analysis.md`. If the user requests a prioritized agentic report, pass the captured trace to the separate `tracelens-analysis-orchestrator` skill when installed; keep that result distinct from Magpie's benchmark report.

## Analyze existing traces and find source

Run standalone gap analysis with `--trace-dir` directly on `benchmark`:

```bash
magpie benchmark \
  --trace-dir path/to/torch_trace \
  --top-k 20 \
  --find-kernel-sources \
  --kernel-source-repos path/to/repository
```

Do not insert a `gap-analysis` positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.

## Drive the optimization loop

1. Run an unprofiled baseline benchmark and save its config and report.
2. Repeat with torch profiling and TraceLens inference post-processing enabled.
3. Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
4. Run gap analysis over the representative steady-state window to rank concrete kernels.
5. Select bottlenecks by total contribution, not only single-dispatch duration.
6. Map the selected kernel to source and an executable testcase.
7. Generate isolated candidate implementations; preserve the baseline.
8. Use `analyze` for iteration, then `compare` with correctness gates to rank candidates.
9. Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.

Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.

## Use MCP tools when available

Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.

Do not pass a CLI `analyze_report.json` wrapper directly to an MCP tool that expects one result object's `performance_state` and `performance_result`. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.

## Additional resources

- Full CLI reference: [reference.md](reference.md)
- Copy-paste command examples: [examples.md](examples.md)

查看并核实来源

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许可证: MIT
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许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 332 stars, 30 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: amd/skills
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月5日
目录更新于: 2026年9月13日
技能指令路径: skills/magpie-kernel-evaluator/SKILL.md @ e867fa4ae451

版本来自目录元数据，使用前请核实来源发布记录。

质量

69/100

有潜力

信任

64/100

仅限沙盒

审计

76/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 332 stars, 30 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

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      "url": "https://www.openagentskill.com/skills/hermes-labs-ai-lintlang",
      "stars": 137,
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  "do_not_use_when": [
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    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
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    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
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      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
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      "install_command": "",
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      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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    "expected_outcomes": [
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      "not_relevant",
      "blocked_by_risk",
      "setup_required"
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      "task": "Use magpie-kernel-evaluator in an agent workflow",
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      "output_quality": 4,
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  "endpoints": {
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    "api": "https://www.openagentskill.com/api/agent/skills/amd-magpie-kernel-evaluator",
    "audit": "https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=amd-magpie-kernel-evaluator&task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/amd-magpie-kernel-evaluator/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/amd-magpie-kernel-evaluator"
  }
}

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