ywkang
13f5cbc317
Concurrency fix (completes the cube_base change): the decode kernel's
output store o_base still used the global cube_id while every load used
the user-local cube_local. For a single user (cube_base=0) they coincide,
so it was masked; a second batched user (cube_base>0) overshot its o shard
into an unmapped VA, mis-decoded to a phantom sip0.cube0.pe8 and raised a
RoutingError. Switch o_base to cube_local (one line); single-user results
are byte-identical (decode smoke/correctness pass).
Batch harness (un-skipped): create all users' tensors first, then submit
all launches deferred and drain together, so deploys don't drive an
already-submitted launch and serialize the batch. Concurrent users on
disjoint CUBE groups now overlap to within 3-5% of the single-user
latency. Swept B in {1,2,capacity} at 8K (high-B dense runs are the
expensive ones; throughput is near-linear in B).
Measured result: aggregate throughput at a full SIP rises from 0.86
(1-kv-per-cube, 2 users) to 1.41 requests/us (8-kv-per-cube, 16 users) —
the dense mapping wins throughput, the spread mapping wins latency.
S5.2 batch paragraph updated projected -> measured; add batch_scaling
figure and plot_batch_scaling.py.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
"""Batch-scaling figure for concurrent decode users on one SIP.
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Reads ``docs/sweeps/decode_batch_sweep.csv`` (from
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``tests/attention/test_gqa_decode_batch_sweep.py``) and plots aggregate
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throughput (requests / us) versus batch size B for the four KV mappings,
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one panel per context length. Each mapping's curve runs up to its SIP
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capacity 16/C (A1=2 ... B=16 users).
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"""
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from pathlib import Path
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import csv
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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parents[3]
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CSV = ROOT / "docs" / "sweeps" / "decode_batch_sweep.csv"
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OUT = (ROOT / "docs" / "report" / "1H-codesign-paper"
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/ "figures" / "gqa_short_context" / "batch_scaling.png")
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MODE_LABEL = {"A1": "1-kv-per-cube", "A2": "2-kv-per-cube",
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"A4": "4-kv-per-cube", "B": "8-kv-per-cube"}
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MODE_COLOR = {"A1": "#1f77b4", "A2": "#2ca02c",
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"A4": "#ffd000", "B": "#d62728"}
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MODES = ["A1", "A2", "A4", "B"]
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def main():
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rows = list(csv.DictReader(CSV.open()))
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contexts = sorted({int(r["S_kv"]) for r in rows})
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fig, axes = plt.subplots(1, len(contexts), figsize=(6.5 * len(contexts), 4.5),
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squeeze=False)
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for col, S in enumerate(contexts):
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ax = axes[0][col]
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for m in MODES:
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pts = sorted(
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((int(r["B"]), float(r["throughput_users_per_us"]))
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for r in rows if r["mode"] == m and int(r["S_kv"]) == S),
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key=lambda t: t[0],
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)
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if not pts:
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continue
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xs, ys = zip(*pts)
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ax.plot(xs, ys, marker="o", color=MODE_COLOR[m],
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label=MODE_LABEL[m])
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ax.annotate(f"{ys[-1]:.2f}", (xs[-1], ys[-1]),
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textcoords="offset points", xytext=(4, 4), fontsize=8)
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ax.set_title(f"S_kv = {S // 1024}K")
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ax.set_xlabel("batch size B (concurrent users)")
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ax.set_ylabel("throughput (requests / µs)")
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=9)
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fig.suptitle("Batch scaling: aggregate throughput vs concurrent users "
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"(one SIP)", fontsize=12, fontweight="bold")
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fig.tight_layout()
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OUT.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(OUT, dpi=140, bbox_inches="tight")
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print(f" ✓ {OUT}")
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if __name__ == "__main__":
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main()
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