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chore: import upstream snapshot with attribution
2026-07-13 12:32:31 +08:00

89 lines
2.8 KiB
Python

# Copyright (c) 2026 LightSeek Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import annotations
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
__all__ = [
"BenchmarkResult",
"export_results",
"import_results",
]
@dataclass
class BenchmarkResult:
"""Result of benchmarking a single kernel on a single shape."""
kernel_name: str
op_family: str
op_mode: str
solution: str
dtype: str
platform_arch: str
shape_params: dict[str, Any]
median_latency_us: float
p90_latency_us: float
p99_latency_us: float
min_latency_us: float
max_latency_us: float
tflops: float | None = None
bandwidth_gb_s: float | None = None
numerics_passed: bool | None = None
max_abs_diff: float | None = None
max_rel_diff: float | None = None
timestamp: str = ""
num_iters: int = 0
def to_dict(self) -> dict[str, Any]:
return asdict(self)
@classmethod
def from_dict(cls, data: dict[str, Any]) -> "BenchmarkResult":
return cls(**data)
def export_results(
results: list[BenchmarkResult],
path: str | Path,
) -> None:
"""Export benchmark results as JSON."""
payload = [result.to_dict() for result in results]
output_path = Path(path)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(payload, indent=2, sort_keys=True))
def import_results(path: str | Path) -> list[BenchmarkResult]:
"""Import benchmark results from JSON."""
input_path = Path(path)
raw = json.loads(input_path.read_text())
if not isinstance(raw, list):
raise ValueError(f"Expected a list of benchmark results in {input_path}")
return [BenchmarkResult.from_dict(item) for item in raw]