ZacharyZcR
8a9a0fca4d
Extend resource_plan to classify the hardware into bottleneck regimes (disk / memory / mixed / compute) and derive tuning knobs automatically: MTP: off when compute-bound (42% loss at full residency, #389) or disk-bound with <90% hit (union growth adds reads) PIPE: COLI_CUDA_PIPE=1 single-GPU, =2 multi-GPU, PIPE=1 CPU disk NUMA: selective interleave for GPU hosts, blanket hint for CPU-only PIN: PIN_GB=all when fully resident + no GPU OMP: COLI_NO_OMP_TUNE=1 for Metal (spin steals GPU power) `coli plan` now shows an auto-tune section with each knob and its reason. `environment_for_plan()` applies them via setdefault so explicit user settings always win. plan version stays at 2 (additive fields: bottleneck_class, projected_hit_rate, tune). 7 new tests covering all regimes.
258 lines
12 KiB
Python
258 lines
12 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 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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)
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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_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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if sys.platform == "win32":
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# MinGW libgomp: niente affinity su Windows, le chiavi non vanno emesse
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self.assertNotIn("OMP_PROC_BIND", env)
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self.assertNotIn("OMP_PLACES", env)
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else:
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self.assertEqual(env["OMP_PROC_BIND"], "spread")
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self.assertEqual(env["OMP_PLACES"], "cores")
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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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if __name__ == "__main__":
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unittest.main()
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