magpie

name

magpie

description

Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.

Magpie

Magpie is a GPU kernel evaluation and LLM benchmarking framework. Use this skill when performing analyze, compare, benchmark, gap-analysis, or when creating kernel configs or discovering kernels without MCP.

When describing Magpie's capabilities: Describe only what is in this skill. Do not add project-specific, pipeline-specific, or other product/org names (e.g. do not mention any parent repo name).

Entry point

• CLI: magpie or python -m Magpie. Run from the Magpie repo root (or with PYTHONPATH including the Magpie package).
• Setup: From repo root, pip install -e . (or make install).

Analyze (single or multi-kernel)

Analyze kernel(s) for correctness and performance.

With kernel config (recommended):

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

Inline (single kernel):

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

• -k, --kernel-config: YAML with kernel or kernels (see template below).
• -t, --testcase: Command to run the test (required if not in config).
• --type: hip | cuda | pytorch (default: hip).
• --compile-cmd: Custom compile command.
• --no-perf: Skip performance profiling.
• -o, --output-dir: Output directory (default: ./results).

Config template (single kernel): Use kernel: with id, type, source_files, working_dir, testcase_command, optional compile_command, env. See Magpie/kernel_config.yaml.example and examples/ck_gemm_add.yaml.

Compare (multiple kernels)

Compare and rank multiple kernel implementations.

With config:

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

Inline:

magpie compare kernel1.hip kernel2.hip --testcase "./run_test.sh"

• -k, --kernel-config: YAML with kernels: list.
• --baseline: Index of baseline kernel (default: 0).
• --no-perf, -o: Same as analyze.

Example: examples/ck_grouped_gemm_compare.yaml.

Benchmark (vLLM / SGLang)

Run framework-level LLM inference benchmarks with optional profiling and gap analysis.

With config (recommended):

magpie benchmark --benchmark-config examples/benchmarks/benchmark_vllm_dsr1.yaml

CLI overrides: magpie benchmark [vllm|sglang] -m <model> --benchmark-config <yaml> with optional:

• -m, --model: Model name or path.
• -p, --precision: fp8 | fp16 | bf16 | fp4 (default: fp8).
• --tp: Tensor parallel size (default: 1).
• --concurrency, --input-len, --output-len: Request and sequence settings.
• --torch-profiler, --system-profiler: Enable profilers.
• --run-mode: docker (default) or local.
• --docker-image, --timeout, -o: Override image, timeout (seconds), output dir.

Example configs: examples/benchmarks/benchmark_vllm_dsr1.yaml, docs/benchmark.md.

Gap analysis (standalone)

Run gap analysis on existing torch trace directories.

magpie benchmark gap-analysis --trace-dir path/to/torch_trace

• --trace-dir: Path to torch_trace dir or benchmark workspace (required).
• --start-pct, --end-pct: Analysis window 0–100 (default: 0, 100).
• --top-k: Top bottleneck kernels (default: 20).
• --min-duration-us: Minimum event duration (µs).
• --categories, --ignore-categories: Include/exclude event categories.

GPU info

magpie --gpu-info

Shows vendor, architecture, compiler, profiler. No mode required.

Create kernel config (no CLI)

When the user needs a kernel config file:

1. Emit YAML matching the structure in Magpie/kernel_config.yaml.example: kernel: with id, type (hip|cuda|pytorch), source_files, working_dir, testcase_command, and optionally compile_command, env.
2. Write the file to the user's requested path (e.g. kernel_config.yaml).
3. Run: magpie analyze --kernel-config <that_file>.

For compare, use kernels: as a list of kernel entries (each with id, type, source_files, etc.).

Discover kernels (no CLI)

1. Scan the project for .hip, .cu, or PyTorch kernel files.
2. For each candidate, build a kernel config entry (id, type, source_files, working_dir, testcase_command if inferrable; otherwise prompt user).
3. Optionally write a combined config and run magpie analyze -k <file> or magpie compare -k <file>.

Suggest optimizations (no CLI)

1. Read analyze or compare JSON output (from -o results or last run).
2. Use performance_state, performance_result.summary, and per-kernel stats (dispatch count, duration, utilization).
3. Suggest improvements (e.g. memory bandwidth, occupancy, kernel fusion) based on the metrics.

List / get benchmark results (no CLI)

• List: Results live under the benchmark --output-dir (default: ./results); each run has a timestamped workspace (e.g. results/benchmark_vllm_<timestamp>/).
• Get result: Open benchmark_report.json or inferencex_result.json in that workspace.
• Compare runs: Diff two workspace reports or run two benchmarks and compare; for TraceLens comparison use TraceLens tooling if available.

Additional resources

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