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c31fc4df52
[CK Tile Engine] Daily tier sampling for tile engine GEMM (#7311) Summary - Replace uniform random instance sampling (random.shuffle) with scrambled Sobol + Latin Hypercube + maximin space-filling sampling, per the Tile Engine Benchmark Sampling RFC - Add op-weighted budget allocation via new TILE_ENGINE_SAMPLING_TIER=daily CMake knob that auto-distributes 8,000 instances across ops proportional to registered weights in op_weights.json - Emit chosen_instances.json manifests for reproducibility tracking - Consolidate 5 copies of sampling logic into single _apply_sampling() method on the base class Jenkinsfile changes Replace per-op -D *_MAX_INSTANCES=250 with single -D TILE_ENGINE_SAMPLING_TIER=daily in gfx942/gfx950/gfx1201 stages. Budget auto-distributes (8000 total per GPU target). --------- Co-authored-by: Claude Sonnet 4 <noreply@anthropic.com>
83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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# SPDX-License-Identifier: MIT
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GEMM_AXES = [
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"tile_m",
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"tile_n",
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"tile_k",
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"warp_m",
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"warp_n",
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"warp_k",
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"warp_tile_m",
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"warp_tile_n",
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"warp_tile_k",
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"pipeline",
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"epilogue",
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"scheduler",
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"pad_m",
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"pad_n",
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"pad_k",
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"persistent",
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]
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CATEGORICAL_AXES = {
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"pipeline",
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"epilogue",
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"scheduler",
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"pad_m",
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"pad_n",
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"pad_k",
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"persistent",
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}
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def normalize_axis_values(feasible_set, axes=None):
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"""Compute normalization metadata for each axis.
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Returns dict mapping axis name to:
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- For numeric axes: {"type": "numeric", "min": v, "max": v, "range": v}
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- For categorical axes: {"type": "categorical", "values": sorted list, "map": value->index}
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"""
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if axes is None:
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axes = GEMM_AXES
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meta = {}
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for ax in axes:
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values = [item[ax] for item in feasible_set if ax in item]
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if not values:
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continue
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if ax in CATEGORICAL_AXES:
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unique = sorted(set(str(v) for v in values))
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meta[ax] = {
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"type": "categorical",
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"values": unique,
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"map": {v: i for i, v in enumerate(unique)},
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"count": len(unique),
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}
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else:
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num_values = [float(v) for v in values]
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mn, mx = min(num_values), max(num_values)
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meta[ax] = {
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"type": "numeric",
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"min": mn,
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"max": mx,
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"range": mx - mn if mx != mn else 1.0,
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}
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return meta
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def normalize_point(item, axes, meta):
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"""Normalize a single point to [0, 1] per axis."""
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coords = []
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for ax in axes:
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if ax not in meta or ax not in item:
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coords.append(0.0)
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continue
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m = meta[ax]
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if m["type"] == "numeric":
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coords.append((float(item[ax]) - m["min"]) / m["range"])
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else:
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coords.append(m["map"].get(str(item[ax]), 0) / max(m["count"] - 1, 1))
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return coords
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