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a42d9ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | from concurrent.futures import ThreadPoolExecutor
import io
import itertools
import json
from pathlib import Path
import tempfile
from threading import Event
import time
import unittest
from unittest.mock import patch
import numpy as np
from tensor_pruning import prune_manifest, prune_queries
from tensor_pruning.service import create_app
from tensor_pruning.core import Projection, _kernel
from fastapi.testclient import TestClient
def reference(arrays, keep, moments):
gains = []
offset = 0
for w, v in zip(arrays, moments):
energies = w.astype(np.longdouble)**2 * v
for head in energies:
intermediate = []
for channel in head:
mass = 0
for u, i in sorted((u, offset+i) for i, u in enumerate(channel)):
intermediate.append((u * (2*mass+u), i))
mass += u
offset += len(channel)
mass = 0
for d, i in sorted(intermediate):
gains.append((d * (2*mass+d), i))
mass += d
keep_ids = {i for _, i in sorted(gains, reverse=True)[:keep]}
offset = 0
masks = []
for w in arrays:
masks.append(np.array([i in keep_ids for i in range(offset, offset+w.size)]).reshape(w.shape))
offset += w.size
return masks
def objective(arrays, masks, moments):
return sum(np.sum(np.sum(np.sum(w.astype(np.longdouble)**2 * v * ~mask, axis=-1)**2,
axis=-1)**2)
for w, mask, v in zip(arrays, masks, moments))
class SelectorTest(unittest.TestCase):
def test_mixed_shapes_and_ties_against_reference(self):
rng = np.random.default_rng(17)
for i in range(100):
shapes = [(rng.integers(1, 6), rng.integers(1, 7), rng.integers(1, 14)) for _ in range(3)]
weights = [rng.integers(-4, 5, size=shape).astype(dtype)
for shape, dtype in zip(shapes, (np.float16, np.float32, np.float64))]
moments = [rng.integers(0, 6, size=w.shape[-1]) for w in weights]
total = sum(w.size for w in weights)
keep = int(rng.integers(0, total+1))
actual = prune_queries(weights, keep, input_second_moments=moments, threads=2, seed=i)
self.assertEqual(sum(np.count_nonzero(mask) for mask in actual), keep)
for mask, expected in zip(actual, reference(weights, keep, moments)):
np.testing.assert_array_equal(mask, expected)
def test_exhaustive_global_optimum(self):
rng = np.random.default_rng(18)
for _ in range(18):
weights = [rng.integers(-3, 4, size=shape).astype(np.float32)
for shape in ((1, 2, 3), (2, 1, 2))]
moments = [np.arange(1, w.shape[-1]+1) for w in weights]
keep = int(rng.integers(0, 11))
got = prune_queries(weights, keep, input_second_moments=moments)
best = float("inf")
for ids in itertools.combinations(range(10), keep):
flat = np.zeros(10, dtype=bool)
flat[list(ids)] = True
trial = [flat[:6].reshape(weights[0].shape), flat[6:].reshape(weights[1].shape)]
best = min(best, objective(weights, trial, moments))
self.assertEqual(objective(weights, got, moments), best)
def test_concurrent_calls_and_disk_workspace(self):
rng = np.random.default_rng(19)
weights = [rng.normal(size=shape).astype(dtype)
for shape, dtype in [((12, 8, 65), np.float32), ((6, 5, 73), np.float64)]]
moments = [rng.uniform(0.2, 2, size=w.shape[-1]) for w in weights]
with tempfile.TemporaryDirectory() as scratch, ThreadPoolExecutor(3) as pool:
def run(keep):
return prune_queries(weights, keep, input_second_moments=moments,
threads=2, scratch_dir=scratch)
for keep, result in zip((2145, 3941, 6158), pool.map(run, (2145, 3941, 6158))):
for mask, expected in zip(result, reference(weights, keep, moments)):
np.testing.assert_array_equal(mask, expected)
def test_native_bfloat16_bits(self):
x = np.linspace(-6, 5, 72, dtype=np.float32).reshape(3, 4, 6)
bits = (x.view(np.uint32) >> 16).astype(np.uint16)
float_equivalent = (bits.astype(np.uint32) << 16).view(np.float32)
moments = np.linspace(0.25, 1.25, x.shape[-1], dtype=np.float64)
workspace = np.empty(x.size, dtype=np.uint32)
mask = np.empty(x.shape, dtype=bool)
desc = Projection(bits.ctypes.data, moments.ctypes.data, mask.ctypes.data, *x.shape, 3)
self.assertEqual(_kernel()(desc, 1, workspace.ctypes.data, 29, 1, 0), 0)
expected = prune_queries([float_equivalent], 29, input_second_moments=[moments])[0]
np.testing.assert_array_equal(mask, expected)
def test_zero_and_full_budget(self):
w = np.ones((1, 2, 3), dtype=np.float16)
self.assertEqual(prune_queries([w], 0)[0].sum(), 0)
self.assertEqual(prune_queries([w], 6)[0].sum(), 6)
with self.assertRaises(ValueError):
prune_queries([w], 7)
with self.assertRaises(ValueError):
prune_queries([np.full_like(w, np.inf)], 0)
with self.assertRaises(ValueError):
prune_queries([w[..., ::-1]], 3)
with self.assertRaises(ValueError):
prune_queries([w], 3, input_second_moments=[[-1, 2, 3]])
with self.assertRaises(TypeError):
prune_queries([np.ones((1, 2, 3), dtype=np.int16)], 3)
class FileAndServiceTest(unittest.TestCase):
def setUp(self):
self.temp = tempfile.TemporaryDirectory()
self.addCleanup(self.temp.cleanup)
self.root = Path(self.temp.name)
self.inputs = self.root / "data"
self.inputs.mkdir()
self.weights = np.arange(24, dtype=np.float32).reshape(2, 3, 4)
np.save(self.inputs / "a.npy", self.weights)
np.save(self.inputs / "v.npy", np.array([0.5, 1., 2., 3.]))
self.spec = dict(keep=7, projections=[dict(name="q", weights="a.npy",
input_second_moment="v.npy")])
(self.inputs / "job.json").write_text(json.dumps(self.spec))
def test_manifest_publishes_exact_masks(self):
output = self.root / "results"
result = prune_manifest(self.inputs, "job.json", output, threads=2)
self.assertEqual(result["kept_slots"], 7)
mask = np.load(output / "mask-0000.npy", allow_pickle=False)
expected = prune_queries([self.weights], 7, input_second_moments=[[.5, 1., 2., 3.]])[0]
np.testing.assert_array_equal(mask, expected)
self.assertEqual(json.loads((output / "result.json").read_text()), result)
with self.assertRaises(FileExistsError):
prune_manifest(self.inputs, "job.json", output)
with self.assertRaises(ValueError):
prune_manifest(self.inputs, "job.json", self.root / "large", max_weights=12)
def test_rejects_symlink_and_failed_native_run_leaves_no_output(self):
external = self.root / "outside.npy"
np.save(external, self.weights)
(self.inputs / "escaped.npy").symlink_to(external)
self.spec["projections"][0]["weights"] = "escaped.npy"
(self.inputs / "escape.json").write_text(json.dumps(self.spec))
with self.assertRaises(ValueError):
prune_manifest(self.inputs, "escape.json", self.root / "out")
np.save(self.inputs / "a.npy", np.full_like(self.weights, np.nan))
with self.assertRaises(ValueError):
prune_manifest(self.inputs, "job.json", self.root / "out")
self.assertFalse((self.root / "out").exists())
def test_http_job_mask_and_auth(self):
app = create_app(self.inputs, self.root / "jobs", token="test-token", threads_per_job=2)
headers = {"Authorization": "Bearer test-token"}
with TestClient(app) as client:
self.assertEqual(client.get("/health").status_code, 200)
self.assertEqual(client.post("/v1/jobs", json={"manifest": "job.json"}).status_code, 401)
escape = client.post("/v1/jobs", json={"manifest": "../out.json"}, headers=headers)
self.assertEqual(escape.status_code, 400)
response = client.post("/v1/jobs", json={"manifest": "job.json"}, headers=headers)
self.assertEqual(response.status_code, 202)
endpoint = f"/v1/jobs/{response.json()['id']}"
for _ in range(150):
status = client.get(endpoint, headers=headers).json()
if status["state"] in {"completed", "failed"}:
break
time.sleep(0.01)
self.assertEqual(status["state"], "completed", status)
binary = client.get(endpoint + "/masks/0", headers=headers)
self.assertEqual(binary.status_code, 200)
self.assertEqual(np.load(io.BytesIO(binary.content)).sum(), 7)
self.assertEqual(client.get(endpoint + "/masks/1", headers=headers).status_code, 404)
def test_queue_backpressure(self):
started, proceed = Event(), Event()
def slow(*args, **kwargs):
started.set()
if not proceed.wait(5):
raise RuntimeError("test timeout")
return prune_manifest(*args, **kwargs)
app = create_app(self.inputs, self.root / "jobs", workers=1, queue_size=0)
with patch("tensor_pruning.service.prune_manifest", slow), TestClient(app) as client:
try:
self.assertEqual(client.post("/v1/jobs", json={"manifest": "job.json"}).status_code, 202)
self.assertTrue(started.wait(2))
response = client.post("/v1/jobs", json={"manifest": "job.json"})
self.assertEqual(response.status_code, 429)
self.assertEqual(response.headers["Retry-After"], "1")
finally:
proceed.set()
if __name__ == "__main__":
unittest.main()
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