forked from ccf-ai-infra/Intro-ops
48 lines
1.4 KiB
Python
48 lines
1.4 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 import copy
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from operator_runtime_testing import cuda_time_ms, PerformanceResult
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from tests.cases import copy as copy_cases
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def _estimate_copy(tensor: torch.Tensor) -> tuple[int, int]:
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elem_bytes = tensor.element_size()
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return 2 * tensor.numel() * elem_bytes, 0
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def bench_copy(backend: str) -> list[PerformanceResult]:
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rows: list[PerformanceResult] = []
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for case in copy_cases.benchmark_cases():
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src = torch.randn(case["shape"], dtype=case["dtype"], device="cuda")
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out = torch.empty_like(src)
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from operator_runtime import prepare_copy
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with prepare_copy(out, src, backend=backend) as prepared:
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runtime = cuda_time_ms(prepared.run)
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torch_ms = cuda_time_ms(lambda: out.copy_(src))
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bytes_, flops = _estimate_copy(src)
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rows.append(
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PerformanceResult(
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"copy",
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backend,
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str(tuple(src.shape)),
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str(src.dtype),
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bytes_,
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flops,
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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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