39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from __future__ import annotations
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[2]
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PYTHON_DIR = ROOT / "python"
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if str(PYTHON_DIR) not in sys.path:
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sys.path.insert(0, str(PYTHON_DIR))
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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import torch
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from operator_runtime_testing import cuda_time_ms, PerformanceResult
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from tests.cases import reduce_sum as reduce_sum_cases
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def bench_reduce_sum(backend: str) -> list[PerformanceResult]:
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rows: list[PerformanceResult] = []
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for case in reduce_sum_cases.benchmark_cases():
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src = torch.randn(case["shape"], dtype=case["dtype"], device="cuda")
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from operator_runtime import prepare_reduce_sum
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out = torch.empty(src.shape[0], dtype=src.dtype, device="cuda")
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with prepare_reduce_sum(out, src, dim=1, backend=backend) as prepared:
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runtime = cuda_time_ms(prepared.run_inputs, args=(src,))
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torch_ms = cuda_time_ms(lambda src: torch.sum(src, dim=1, out=out), args=(src,))
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rows.append(
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PerformanceResult(
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"reduce_sum",
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backend,
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str(tuple(src.shape)),
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str(src.dtype),
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runtime,
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torch_ms,
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)
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)
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return rows
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