woolcoxm
bd3efb1c97
The core-count fix was necessary but not sufficient: @liangstein confirmed
physical_cpu_count() now returns 64 on his box, yet --auto-tier STILL pinned
decode to one core. A complete env diff between the plain (working) and
--auto-tier (broken) paths showed exactly three keys the plan adds:
OMP_NUM_THREADS = 64 (correct, verified)
OMP_PROC_BIND = spread
OMP_PLACES = cores
Since OMP_NUM_THREADS was already correct, the culprit is the affinity pair.
The mechanism: environment_for_plan() sets OMP_PROC_BIND=spread + OMP_PLACES=cores
in the launcher's env. The engine's hot-thread tuning (glm.c main, the
COLI_OMP_TUNED self-exec) then tries setenv("OMP_PROC_BIND","close", overwrite=0)
-- but overwrite=0 cannot replace an already-set var, so the plan's "spread"
wins. On the reporter's libgomp + multi-socket topology, spread + places=cores
collapsed the team to a single CPU even with 64 threads configured.
Fix: don't set affinity from the plan at all. The engine deliberately chose
"close" for cache locality (the tiny back-to-back per-expert matmuls want
adjacent cores), and the plain path already leaves affinity to the engine.
Removing the plan's spread/places makes --auto-tier match the working plain
path; a user wanting a specific policy can still set OMP_PROC_BIND/OMP_PLACES
in their own environment (environment_for_plan only setdefaults OMP_NUM_THREADS).
Verified via env diff: after the fix, --auto-tier adds ONLY OMP_NUM_THREADS
beyond the plain env -- the engine's own close/bind tuning now wins on both
paths identically.
Note: this could not be reproduced on Windows (MinGW libgomp prints "Affinity
not supported on this configuration" and ignores the vars entirely); it is
Linux-libgomp-specific, matching the reporter's Rocky 9 box.
Tests: rewrite test_applies_plan_without_overriding_explicit_settings to assert
the plan sets NO affinity vars on any platform (the old test encoded the buggy
platform-dependent spread/cores contract). Add
test_plan_does_not_set_omp_affinity_vars as a focused regression. 78/78 pass.
386 lines
19 KiB
Python
386 lines
19 KiB
Python
import json
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import struct
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import subprocess
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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from unittest import mock
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from resource_plan import (
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GB,
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analyze_model,
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build_plan,
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cpu_socket_count,
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environment_for_plan,
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format_plan,
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memory_available,
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physical_cpu_count,
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)
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def write_shard(path, tensors):
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offset = 0
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header = {}
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payload = b""
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for name, size in tensors:
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header[name] = {"dtype": "U8", "shape": [size], "data_offsets": [offset, offset + size]}
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payload += b"\0" * size
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offset += size
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raw = json.dumps(header).encode()
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path.write_bytes(struct.pack("<Q", len(raw)) + raw + payload)
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class ResourcePlanTest(unittest.TestCase):
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def setUp(self):
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self.tmp = tempfile.TemporaryDirectory()
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self.model = Path(self.tmp.name)
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(self.model / "config.json").write_text(json.dumps({
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"num_hidden_layers": 2,
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"n_routed_experts": 2,
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"kv_lora_rank": 4,
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"qk_rope_head_dim": 2,
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"qk_nope_head_dim": 3,
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"v_head_dim": 5,
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"num_attention_heads": 2,
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}))
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write_shard(self.model / "model.safetensors", [
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("model.embed_tokens.weight", 100),
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("model.layers.0.self_attn.q_a_proj.weight", 200),
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("model.layers.1.mlp.experts.0.gate_proj.weight", 30),
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("model.layers.1.mlp.experts.0.up_proj.weight", 30),
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("model.layers.1.mlp.experts.1.gate_proj.weight", 30),
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("model.layers.1.mlp.experts.1.up_proj.weight", 30),
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])
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def tearDown(self):
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self.tmp.cleanup()
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def test_analyzes_dense_and_expert_storage(self):
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info = analyze_model(self.model)
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self.assertEqual(info["dense_bytes"], 300)
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self.assertEqual(info["expert_bytes"], 120)
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self.assertEqual(info["expert_count"], 2)
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self.assertEqual(info["per_cap_bytes"], 60)
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def test_memory_available_is_positive(self):
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# Regression: on native Windows CPython, /proc/meminfo does not exist,
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# so the Linux-only path returned 0 and the expert cache was sized to
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# 0 slots/layer. The value must be a sane positive number of bytes.
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self.assertGreater(memory_available(), 0)
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def test_cpu_socket_count_is_positive(self):
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self.assertGreaterEqual(cpu_socket_count(), 1)
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def test_builds_bounded_three_tier_plan(self):
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gpus = [{"index": 0, "name": "test-gpu", "total_bytes": 12 * GB,
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"free_bytes": 10 * GB}]
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plan = build_plan(self.model, ram_gb=16, context=32, vram_gb=20,
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available_memory=32 * GB, available_disk=100 * GB, gpus=gpus,
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physical_cpus=24, cpu_sockets=2)
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self.assertEqual(plan["version"], 2)
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self.assertEqual(plan["policy"]["name"], "quality")
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self.assertEqual(plan["cpu"]["physical_cores"], 24)
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self.assertEqual(plan["cpu"]["sockets"], 2)
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self.assertTrue(plan["policy"]["preserve_quantization"])
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self.assertFalse(plan["tiers"]["vram"]["requires_host_backing"])
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self.assertEqual(plan["tiers"]["ram"]["budget_bytes"], 16 * GB)
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self.assertLessEqual(plan["tiers"]["vram"]["budget_bytes"], 8 * GB)
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self.assertIn("clamped", plan["warnings"][0])
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self.assertIn("0:test-gpu", format_plan(plan))
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def test_auto_tier_thread_count_uses_physical_cores(self):
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# End-to-end for #325: build_plan + environment_for_plan must export the
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# physical (not logical SMT) core count as OMP_NUM_THREADS. The original
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# suite passed physical_cpus=24 explicitly, so it never exercised the
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# real physical_cpu_count() probe whose single-core failure pinned decode.
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def lscpu(stdout):
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return subprocess.CompletedProcess(args=[], returncode=0,
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stdout=stdout, stderr="")
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# 1 socket, 12 cores, 2 SMT siblings -> 24 threads, 12 physical cores.
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# lscpu prepends the CPU column, so output is CPU,Core,Socket.
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rows = [f"{cpu},{core},0" for core in range(12) for cpu in range(2)]
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blob = "# CPU,Core,Socket\n" + "\n".join(rows)
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with mock.patch("resource_plan.subprocess.run", return_value=lscpu(blob)), \
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mock.patch.object(sys, "platform", "linux"):
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plan = build_plan(self.model, available_memory=16 * GB,
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available_disk=1, gpus=[])
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env = environment_for_plan(plan)
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self.assertEqual(plan["cpu"]["physical_cores"], 12)
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self.assertEqual(env["OMP_NUM_THREADS"], "12")
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def test_plan_does_not_set_omp_affinity_vars(self):
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# The real #325 regression: --auto-tier set OMP_PROC_BIND=spread +
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# OMP_PLACES=cores, which ran before the engine's overwrite=0 setenv and
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# so won, collapsing the OpenMP team to one CPU on the reporter's 64-core
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# Linux box even though OMP_NUM_THREADS was correct. The plan must leave
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# affinity to the engine's own hot-thread tuning (which prefers 'close').
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plan = build_plan(self.model, available_memory=16 * GB,
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available_disk=1, gpus=[], physical_cpus=64)
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env = environment_for_plan(plan)
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self.assertEqual(env["OMP_NUM_THREADS"], "64")
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self.assertNotIn("OMP_PROC_BIND", env)
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self.assertNotIn("OMP_PLACES", env)
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def test_plan_conserves_budget_and_experts_above_256gb(self):
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# Regression for #325's reporter: a 512 GB machine loading the whole
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# model into RAM. Verify the budget math stays exact at large RAM sizes
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# (no integer truncation, no over-allocation, no experts lost between
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# tiers). Checked at 256/512/800 GB to bracket the reporter's box.
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for ram_gb in (256, 512, 800):
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plan = build_plan(self.model, ram_gb=ram_gb, available_disk=1,
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gpus=[], physical_cpus=64)
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ram = plan["tiers"]["ram"]
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# RAM budget never over-allocated: dense + runtime + cache <= budget.
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allocated = (ram["dense_bytes"] + ram["runtime_bytes"]
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+ ram["expert_cache_bytes"])
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self.assertLessEqual(allocated, ram["budget_bytes"],
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f"over-allocated RAM at {ram_gb} GB")
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# Every expert byte is accounted for exactly once across the tiers.
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tiers = plan["tiers"]
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tiered = (tiers["vram"]["hot_expert_bytes"]
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+ ram["warm_expert_bytes"]
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+ tiers["disk"]["cold_expert_bytes"])
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self.assertEqual(tiered, plan["model"]["expert_bytes"],
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f"expert bytes lost/duplicated at {ram_gb} GB")
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# A positive RAM budget yields a non-negative cache and a sensible cap.
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self.assertGreaterEqual(ram["expert_cache_bytes"], 0)
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self.assertGreaterEqual(ram["cache_slots_per_layer"], 0)
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def test_filters_requested_devices(self):
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gpus = [{"index": 0, "name": "a", "total_bytes": 8 * GB, "free_bytes": 8 * GB}]
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plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=gpus, gpu_indices=[1])
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self.assertEqual(plan["tiers"]["vram"]["devices"], [])
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self.assertIn("not detected", plan["warnings"][0])
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def test_cli_emits_versioned_json(self):
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cli = Path(__file__).parents[1] / "coli"
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run = subprocess.run([
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sys.executable, str(cli), "plan", "--model", str(self.model),
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"--gpu", "none", "--json",
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], text=True, capture_output=True, check=True)
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plan = json.loads(run.stdout)
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self.assertEqual(plan["version"], 2)
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self.assertEqual(plan["model"]["expert_count"], 2)
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def test_applies_plan_without_overriding_explicit_settings(self):
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gpus = [
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{"index": 0, "name": "a", "total_bytes": 12 * GB, "free_bytes": 10 * GB},
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{"index": 1, "name": "b", "total_bytes": 12 * GB, "free_bytes": 10 * GB},
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]
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plan = build_plan(self.model, ram_gb=16, available_memory=32 * GB,
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available_disk=1, gpus=gpus, cpu_sockets=2)
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env = environment_for_plan(plan, {"RAM_GB": "12", "PIN": "stats.txt",
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"COLI_GPUS": "1"})
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self.assertEqual(env["RAM_GB"], "12")
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self.assertEqual(env["COLI_CUDA"], "1")
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self.assertEqual(env["COLI_GPUS"], "1")
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self.assertEqual(env["OMP_NUM_THREADS"], str(plan["cpu"]["physical_cores"]))
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# The plan must NOT set OMP_PROC_BIND / OMP_PLACES on any platform:
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# the engine's own hot-thread tuning owns affinity (it prefers
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# OMP_PROC_BIND=close for the back-to-back per-expert matmuls). Setting
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# spread + cores here ran before the engine's overwrite=0 setenv and so
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# won, collapsing the team to one CPU on some libgomp topologies (#325).
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self.assertNotIn("OMP_PROC_BIND", env)
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self.assertNotIn("OMP_PLACES", env)
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self.assertEqual(env["PIN_GB"], env["CUDA_EXPERT_GB"])
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explicit_threads = environment_for_plan(plan, {"OMP_NUM_THREADS": "7",
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"OMP_PROC_BIND": "close"})
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self.assertEqual(explicit_threads["OMP_NUM_THREADS"], "7")
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self.assertEqual(explicit_threads["OMP_PROC_BIND"], "close")
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if sys.platform.startswith("linux"):
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self.assertEqual(env["COLI_NUMA"], "1")
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explicit_numa = environment_for_plan(plan, {"COLI_NUMA": "0"})
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self.assertEqual(explicit_numa["COLI_NUMA"], "0")
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def test_single_socket_plan_does_not_enable_numa(self):
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plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=[], physical_cpus=8, cpu_sockets=1)
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self.assertNotIn("COLI_NUMA", environment_for_plan(plan))
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def test_auto_tune_mtp_off_when_compute_bound(self):
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# Tiny model with 64 GB RAM and no GPU: all experts fit in RAM with no
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# warm tier, so the plan classifies as compute-bound.
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plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
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available_disk=100 * GB, gpus=[], physical_cpus=24,
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cpu_sockets=2)
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# With such a small model fully in RAM and no GPU, bottleneck is compute
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self.assertEqual(plan["bottleneck_class"], "compute")
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self.assertIn("DRAFT", plan["tune"])
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self.assertEqual(plan["tune"]["DRAFT"]["value"], "0")
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env = environment_for_plan(plan)
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self.assertEqual(env["DRAFT"], "0")
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explicit = environment_for_plan(plan, {"DRAFT": "3"})
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self.assertEqual(explicit["DRAFT"], "3")
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def test_auto_tune_mtp_off_when_disk_low_hit(self):
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# Use a model large enough that 8 GB RAM can't hold all experts.
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big = tempfile.TemporaryDirectory()
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bigmodel = Path(big.name)
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(bigmodel / "config.json").write_text(json.dumps({
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"num_hidden_layers": 2, "n_routed_experts": 4,
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"kv_lora_rank": 4, "qk_rope_head_dim": 2,
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"qk_nope_head_dim": 3, "v_head_dim": 5, "num_attention_heads": 2,
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}))
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expert_size = 3 * GB # each expert 3 GB → 12 GB total, won't fit in 8 GB budget
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write_shard(bigmodel / "out-00000.safetensors", [
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("model.embed_tokens.weight", 100),
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("model.layers.0.self_attn.q_a_proj.weight", 200),
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])
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for i in range(4):
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write_shard(bigmodel / f"out-{i+1:05d}.safetensors", [
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(f"model.layers.1.mlp.experts.{i}.gate_proj.weight", expert_size),
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])
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plan = build_plan(bigmodel, ram_gb=0, available_memory=4 * GB,
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available_disk=100 * GB, gpus=[], physical_cpus=8,
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cpu_sockets=1)
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big.cleanup()
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self.assertEqual(plan["bottleneck_class"], "disk")
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self.assertLess(plan["projected_hit_rate"], 0.90)
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self.assertEqual(plan["tune"]["DRAFT"]["value"], "0")
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def test_auto_tune_pipe_multi_gpu(self):
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gpus = [
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{"index": 0, "name": "a", "total_bytes": 32 * GB, "free_bytes": 30 * GB},
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{"index": 1, "name": "b", "total_bytes": 32 * GB, "free_bytes": 30 * GB},
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]
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plan = build_plan(self.model, ram_gb=16, available_memory=32 * GB,
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available_disk=1, gpus=gpus, cpu_sockets=2)
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self.assertEqual(plan["tune"]["COLI_CUDA_PIPE"]["value"], "2")
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env = environment_for_plan(plan)
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self.assertEqual(env["COLI_CUDA_PIPE"], "2")
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def test_auto_tune_pipe_single_gpu(self):
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gpus = [{"index": 0, "name": "a", "total_bytes": 12 * GB, "free_bytes": 10 * GB}]
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plan = build_plan(self.model, ram_gb=16, available_memory=32 * GB,
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available_disk=1, gpus=gpus, cpu_sockets=1)
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self.assertEqual(plan["tune"]["COLI_CUDA_PIPE"]["value"], "1")
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def test_auto_tune_numa_hint_for_cpu_only(self):
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plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
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available_disk=1, gpus=[], physical_cpus=64, cpu_sockets=2)
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self.assertIn("_numa_hint", plan["tune"])
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self.assertIn("numactl", plan["tune"]["_numa_hint"])
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self.assertIn("auto-tune", format_plan(plan))
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def test_format_plan_shows_tune_and_hit_rate(self):
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plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
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available_disk=100 * GB, gpus=[], physical_cpus=24,
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cpu_sockets=1)
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text = format_plan(plan)
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self.assertIn("hit", text)
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self.assertIn("auto-tune", text)
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self.assertIn("DRAFT", text)
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def test_cpu_binary_does_not_apply_gpu_tier(self):
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plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=[{"index": 0, "name": "a", "total_bytes": 8 * GB,
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"free_bytes": 8 * GB}])
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env = environment_for_plan(plan, cuda_enabled=False)
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self.assertIn("RAM_GB", env)
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self.assertNotIn("COLI_CUDA", env)
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disabled = environment_for_plan(plan, {"COLI_CUDA": "0"}, cuda_enabled=True)
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self.assertNotIn("COLI_GPU", disabled)
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self.assertNotIn("CUDA_EXPERT_GB", disabled)
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def test_rejects_unknown_policy_and_marks_experimental_policy(self):
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with self.assertRaisesRegex(ValueError, "unknown policy"):
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build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=[], policy="fast-ish")
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plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=[], policy="experimental-fast")
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self.assertFalse(plan["policy"]["quality_preserving"])
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self.assertFalse(plan["policy"]["preserve_router"])
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def test_balanced_policy_enables_lossless_live_repin(self):
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plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
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gpus=[], policy="balanced")
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env = environment_for_plan(plan)
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self.assertEqual(env["COLI_POLICY"], "balanced")
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self.assertEqual(env["REPIN"], "64")
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explicit = environment_for_plan(plan, {"REPIN": "0"})
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self.assertEqual(explicit["REPIN"], "0")
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def test_plan_explains_hot_warm_and_cold_placement(self):
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plan = build_plan(self.model, ram_gb=4, vram_gb=0,
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available_memory=4 * GB, available_disk=1, gpus=[])
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self.assertEqual([item["target"] for item in plan["decisions"]],
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["VRAM", "RAM", "Disk"])
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self.assertIn("quality-preserving yes", format_plan(plan))
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self.assertIn("expected_bottleneck", plan)
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class PhysicalCpuCountTest(unittest.TestCase):
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"""Regression for #325: --auto-tier pinned decode to one core because
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physical_cpu_count() silently returned 1.
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Two root causes this locks down:
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1. lscpu -p prepends a CPU column, so `-p=core,socket` emits
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CPU,Core,Socket; counting rows counted logical SMT siblings.
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2. any probe failure fell through to ``os.cpu_count() or 1`` and the
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``or 1`` could pin a constrained/cgroup'd box to a single core.
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"""
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def _lscpu(self, stdout):
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return subprocess.CompletedProcess(args=[], returncode=0,
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stdout=stdout, stderr="")
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def _lscpu_topology(self, sockets, cores_per_socket, threads_per_core):
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# Real lscpu shape: socket-local core IDs repeat across sockets; the
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# CPU column (always prepended) is a unique logical-CPU index.
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rows, cpu = [], 0
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for sock in range(sockets):
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for core in range(cores_per_socket):
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for _ in range(threads_per_core):
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rows.append(f"{cpu},{core},{sock}")
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cpu += 1
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return "# CPU,Core,Socket\n" + "\n".join(rows)
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def test_counts_physical_cores_not_smt_threads(self):
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blob = self._lscpu_topology(sockets=2, cores_per_socket=16, threads_per_core=2)
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with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
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mock.patch.object(sys, "platform", "linux"):
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self.assertEqual(physical_cpu_count(), 32)
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def test_single_socket_no_smt(self):
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blob = self._lscpu_topology(sockets=1, cores_per_socket=8, threads_per_core=1)
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|
with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
|
|
mock.patch.object(sys, "platform", "linux"):
|
|
self.assertEqual(physical_cpu_count(), 8)
|
|
|
|
def test_skips_offline_core_socket_fields(self):
|
|
# VMs / large NUMA boxes emit "-" for offline core or socket IDs; that
|
|
# used to raise ValueError, discard the whole parse, and fall through
|
|
# to the single-core fallback.
|
|
blob = "# CPU,Core,Socket\n0,0,0\n1,-,0\n2,1,0\n3,1,0\n"
|
|
with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
|
|
mock.patch.object(sys, "platform", "linux"):
|
|
self.assertEqual(physical_cpu_count(), 2)
|
|
|
|
def test_lscpu_missing_falls_back_to_logical_not_silent_one(self):
|
|
# The bug: lscpu absent -> os.cpu_count() or 1. On a constrained box
|
|
# os.cpu_count() can be 1. We still must never silently pick 1 without
|
|
# a warning, and when logical cores exist they must be used.
|
|
import os
|
|
with mock.patch("resource_plan.subprocess.run", side_effect=FileNotFoundError), \
|
|
mock.patch.object(sys, "platform", "linux"), \
|
|
mock.patch("resource_plan.os.cpu_count", return_value=16), \
|
|
mock.patch("sys.stderr"):
|
|
self.assertEqual(physical_cpu_count(), 16)
|
|
|
|
def test_zero_logical_cores_warns_and_returns_one(self):
|
|
# The genuine degenerate case: no probe works and os.cpu_count() is
|
|
# None/1. Must return 1 (engine needs a positive team size) but warn.
|
|
with mock.patch("resource_plan.subprocess.run", side_effect=FileNotFoundError), \
|
|
mock.patch.object(sys, "platform", "linux"), \
|
|
mock.patch("resource_plan.os.cpu_count", return_value=None), \
|
|
mock.patch("sys.stderr"):
|
|
self.assertEqual(physical_cpu_count(), 1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|