ywkang
08812eda58
Implement VA/MMU layer (ADR-0011 Phase 1) enabling Triton kernels to use contiguous virtual addresses on sharded tensors. Key changes: - PE_MMU component: hybrid inbox (MmuMapMsg) + sync translate() for PE_DMA - VirtualAllocator + PEMemAllocator: free-list with coalescing - MmuMapMsg/MmuUnmapMsg fabric path with SIP-level routing - DPPolicy-based mapping: replicate=local, sharded=broadcast - Tensor lifecycle: del + weakref cleanup, context manager - Rename: TensorHandle.pa→addr, DmaReadCmd.src_pa→src_addr, ctx→torch Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
from __future__ import annotations
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import importlib
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from collections.abc import Callable
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from typing import Any
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from kernbench.runtime_api.context import RuntimeContext
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BenchFn = Callable[[RuntimeContext], Any]
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def resolve_bench(bench_id: str) -> BenchFn:
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"""
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Resolve a bench id into a callable bench function.
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Expected layout (repo root):
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benches/<bench_id>.py
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def run(torch: RuntimeContext) -> Any
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"""
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bench_id = bench_id.strip()
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if not bench_id:
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raise ValueError("Bench id is empty.")
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module_path = f"benches.{bench_id}"
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try:
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mod = importlib.import_module(module_path)
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except ModuleNotFoundError as e:
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raise ValueError(f"Unknown bench '{bench_id}'. Expected module {module_path}.py") from e
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run_fn = getattr(mod, "run", None)
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if run_fn is None:
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raise ValueError(f"Bench module {module_path} must define a 'run(torch)' function.")
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if not callable(run_fn):
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raise ValueError(f"'run' in {module_path} is not callable.")
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return run_fn
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