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https://github.com/NVIDIA/nvbench.git
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4a22923721
Move NVBench JSON result parsing into cuda.bench.results with explicit BenchmarkResult, BenchmarkResultDevice, BenchmarkResultSummary, SubBenchmarkResult, and SubBenchmarkState types. Remove the result reader from the top-level cuda.bench namespace and require construction through BenchmarkResult.from_json() or BenchmarkResult.empty(). Preserve bulk sample/frequency parsing and estimator helpers while making summaries rich objects that retain tag/name/hint/hide/description metadata. Add nvbench-json-summary to render NVBench JSON output as an NVBench-style markdown summary table, including axis formatting, device sections, hidden summary filtering, and summary hint formatting. Update packaging, type stubs, and tests for the new namespace, renamed classes, Python 3.10-compatible annotations, and summary-table generation.
222 lines
8.8 KiB
Python
222 lines
8.8 KiB
Python
# Copyright 2026 NVIDIA Corporation
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#
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# Licensed under the Apache License, Version 2.0 with the LLVM exception
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# (the "License"); you may not use this file except in compliance with
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# the License.
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#
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# You may obtain a copy of the License at
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#
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# http://llvm.org/foundation/relicensing/LICENSE.txt
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib.util
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import json
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from pathlib import Path
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def load_nvbench_json_summary():
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module_path = (
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Path(__file__).resolve().parents[1] / "scripts" / "nvbench_json_summary.py"
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)
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spec = importlib.util.spec_from_file_location("nvbench_json_summary", module_path)
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assert spec is not None
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assert spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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nvbench_json_summary = load_nvbench_json_summary()
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def write_result_json(path):
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path.write_text(
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json.dumps(
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{
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"devices": [
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{
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"id": 0,
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"name": "Test GPU",
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}
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],
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"benchmarks": [
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{
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"name": "copy",
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"devices": [0],
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"axes": [
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{
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"name": "BlockSize",
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"type": "int64",
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"flags": "pow2",
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"values": [
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{
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"input_string": "8",
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"description": "2^8 = 256",
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"value": 256,
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}
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],
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}
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],
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"states": [
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{
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"name": "Device=0 BlockSize=2^8",
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"device": 0,
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"type_config_index": 0,
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"axis_values": [
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{
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"name": "BlockSize",
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"type": "int64",
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"value": "256",
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}
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],
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"summaries": [
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{
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"tag": "nv/cold/time/gpu/sample_size",
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"name": "Samples",
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"hint": "sample_size",
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"data": [
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{
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"name": "value",
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"type": "int64",
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"value": "12",
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}
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],
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},
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{
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"tag": "nv/cold/time/gpu/mean",
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"name": "GPU Time",
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"hint": "duration",
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"data": [
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{
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"name": "value",
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"type": "float64",
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"value": "1.25e-6",
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}
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],
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},
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{
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"tag": "nv/cold/time/gpu/stdev/relative",
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"name": "Noise",
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"hint": "percentage",
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"data": [
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{
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"name": "value",
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"type": "float64",
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"value": "0.015",
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}
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],
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},
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{
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"tag": "nv/cold/bw/global/bytes_per_second",
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"name": "GlobalMem BW",
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"hint": "byte_rate",
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"data": [
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{
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"name": "value",
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"type": "float64",
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"value": "2.5e9",
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}
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],
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},
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{
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"tag": "nv/cold/bw/global/utilization",
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"name": "BWUtil",
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"hint": "percentage",
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"data": [
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{
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"name": "value",
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"type": "float64",
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"value": "0.625",
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}
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],
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},
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{
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"tag": "nv/cold/time/gpu/min",
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"name": "Min GPU Time",
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"hint": "duration",
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"hide": "Hidden by default.",
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"data": [
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{
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"name": "value",
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"type": "float64",
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"value": "1.0e-6",
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}
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],
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},
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],
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"is_skipped": False,
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}
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],
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}
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],
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}
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),
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encoding="utf-8",
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)
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def test_json_summary_formats_nvbench_style_markdown(tmp_path):
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json_path = tmp_path / "result.json"
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write_result_json(json_path)
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result = nvbench_json_summary.BenchmarkResult.from_json(json_path)
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report = nvbench_json_summary.format_result(result)
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assert "# Benchmark Results" in report
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assert "## copy" in report
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assert "### [0] Test GPU" in report
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assert (
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"| BlockSize | Samples | GPU Time | Noise | GlobalMem BW | BWUtil |" in report
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)
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assert (
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"| 2^8 = 256 | 12x | 1.250 us | 1.50% | 2.500 GB/s | 62.50% |" in report
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)
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assert "Min GPU Time" not in report
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def test_json_summary_formats_axis_values_like_markdown_printer():
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axes_by_name = {
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"BlockSize": {
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"name": "BlockSize",
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"type": "int64",
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"flags": "pow2",
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},
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"NumBlocks": {
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"name": "NumBlocks",
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"type": "int64",
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"flags": "",
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},
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"Duration": {
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"name": "Duration",
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"type": "float64",
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"flags": "",
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},
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}
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assert nvbench_json_summary.format_axis_value(
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{"name": "BlockSize", "type": "int64", "value": "256"}, axes_by_name
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) == ("BlockSize", "2^8 = 256")
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assert nvbench_json_summary.format_axis_value(
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{"name": "NumBlocks", "type": "int64", "value": "64"}, axes_by_name
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) == ("NumBlocks", "64")
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assert nvbench_json_summary.format_axis_value(
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{"name": "Duration", "type": "float64", "value": "0.123456789"},
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axes_by_name,
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) == ("Duration", "0.12346")
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def test_json_summary_cli_writes_output_file(tmp_path):
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json_path = tmp_path / "result.json"
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output_path = tmp_path / "summary.md"
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write_result_json(json_path)
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rc = nvbench_json_summary.main([str(json_path), "--output", str(output_path)])
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assert rc == 0
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assert "GlobalMem BW" in output_path.read_text(encoding="utf-8")
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