File size: 11,463 Bytes
ae36cfb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | """CPU-only tests of batched decoding, timing, and infrastructure accounting."""
from contextlib import nullcontext
import json
from pathlib import Path
import sys
import tempfile
from types import SimpleNamespace
import unittest
from unittest.mock import patch
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
import generate
import evaluate
import benchmark_batch
class Scalar:
def __init__(self, value):
self.value = value
def item(self):
return self.value
class Row:
def __init__(self, values):
self.values = values
def sum(self):
return Scalar(sum(self.values))
class Matrix:
def __init__(self, rows):
self.rows = rows
self.shape = (len(rows), len(rows[0]))
def __getitem__(self, key):
if isinstance(key, tuple):
row, column = key
return Matrix([value[column] for value in self.rows[row]])
return Row(self.rows[key])
def tolist(self):
return self.rows
class Batch(dict):
def __init__(self, ids, masks):
super().__init__(input_ids=Matrix(ids), attention_mask=Matrix(masks))
self.input_ids = self["input_ids"]
self.attention_mask = self["attention_mask"]
def to(self, device):
assert device == "cuda"
return self
class Tokenizer:
eos_token_id = 99
padding_side = "right"
width = 4
def apply_chat_template(self, messages, **kwargs):
assert kwargs == {"tokenize": False, "add_generation_prompt": True}
return messages[-1]["content"]
def __call__(self, texts, **kwargs):
self.padding_observed = self.padding_side
self.kwargs_observed = kwargs
width = self.width
return Batch([[0] * (width - 2) + [10, 11] for _ in texts],
[[0] * (width - 2) + [1, 1] for _ in texts])
def decode(self, tokens, **kwargs):
return ",".join(str(token) for token in tokens if token not in (98, 99))
class Model:
def __init__(self, rows, eos):
self.rows = rows
self.generation_config = SimpleNamespace(eos_token_id=eos)
def generate(self, **kwargs):
self.kwargs = kwargs
prefix = kwargs["input_ids"].rows
return Matrix([head + tail for head, tail in zip(prefix, self.rows)])
def backend(rows, eos=99):
instance = generate.UnslothGenerator.__new__(generate.UnslothGenerator)
instance.tokenizer = Tokenizer()
instance.model = Model(rows, eos)
instance.torch = SimpleNamespace(cuda=SimpleNamespace(synchronize=lambda: None), inference_mode=nullcontext)
return instance
class BatchInferenceTests(unittest.TestCase):
def test_left_padding_mask_and_eos_trim(self):
generator = backend([[1, 2, 99, 99], [3, 99, 99, 99]])
rows = generator.generate_many(["short", "longer"], 4)
self.assertEqual(generator.tokenizer.padding_observed, "left")
self.assertEqual(generator.tokenizer.padding_side, "right")
self.assertFalse(generator.tokenizer.kwargs_observed["add_special_tokens"])
self.assertIn("attention_mask", generator.model.kwargs)
self.assertEqual([row["output"] for row in rows], ["1,2", "3"])
self.assertEqual([row["tokens"] for row in rows], [3, 2])
self.assertEqual([row["prompt_tokens"] for row in rows], [2, 2])
self.assertTrue(all(row["done_reason"] == "stop" for row in rows))
def test_eos_list_and_length_exhaustion(self):
generator = backend([[1, 98, 99, 99], [1, 2, 3, 4]], eos=[98, 99])
rows = generator.generate_many(["first", "second"], 4)
self.assertEqual(rows[0]["tokens"], 2)
self.assertEqual(rows[0]["output"], "1")
self.assertEqual(rows[1]["done_reason"], "length")
self.assertEqual(rows[1]["tokens"], 4)
def test_whole_batch_latency_never_divided(self):
generator = backend([[1, 99], [2, 99]])
with patch.object(generate.time, "monotonic", side_effect=[100.0, 101.0, 109.0, 111.0]):
rows = generator.generate_many(["first", "second"], 2)
self.assertEqual([row["generation_seconds"] for row in rows], [8.0, 8.0])
self.assertEqual([row["generation_call_seconds"] for row in rows], [11.0, 11.0])
self.assertEqual([row["generation_batch_size"] for row in rows], [2, 2])
def test_context_bound_rejects_without_silent_truncation(self):
generator = backend([[1, 99]])
generator.tokenizer.width = 4090
with self.assertRaisesRegex(ValueError, "4096-token"):
generator.generate_many(["long"], 16)
self.assertEqual(generator.tokenizer.padding_side, "right")
self.assertFalse(hasattr(generator.model, "kwargs"))
def test_batch_limit_enforced(self):
generator = backend([])
for prompts in [[], ["x"] * 33]:
with self.assertRaises(ValueError):
generator.generate_many(prompts, 2)
def test_sixteen_prompt_capacity_is_accepted(self):
generator = backend([[1, 99]] * 16)
rows = generator.generate_many(["training prompt"] * 16, 2)
self.assertEqual(len(rows), 16)
self.assertTrue(all(row["generation_batch_size"] == 16 for row in rows))
def test_thirty_two_prompt_capacity_is_accepted(self):
generator = backend([[1, 99]] * 32)
rows = generator.generate_many(["training prompt"] * 32, 2)
self.assertEqual(len(rows), 32)
self.assertTrue(all(row["generation_batch_size"] == 32 for row in rows))
def test_prompt_to_launch_uses_full_call_with_explicit_legacy_fallback(self):
check = {"compile_success": True, "clean_run_60s": True,
"validation_queue_seconds": 4.0, "preimport_seconds": 1.0,
"import": {"wall_seconds": 2.0}, "runtime": {"startup_seconds": 3.0}}
for call_seconds, expected, source in [(11.0, 21.0, "generation_call_seconds"),
(None, 18.0, "generation_seconds_legacy_fallback")]:
with self.subTest(call_seconds=call_seconds), tempfile.TemporaryDirectory() as folder:
root = Path(folder)
item = root / "items/0000"
item.mkdir(parents=True)
result = {"index": 0, "generation_seconds": 8.0,
"generation_call_seconds": call_seconds, "prompt_to_launch_seconds": None}
with patch("builtins.print"):
evaluate.finish_result(item, result, check, root, 1, 60)
saved = json.loads((item / "result.json").read_text())
self.assertEqual(saved["prompt_to_launch_seconds"], expected)
self.assertEqual(saved["generation_seconds"], 8.0)
self.assertEqual(saved["generation_latency_source"], source)
def test_benchmark_does_not_select_fast_batch_without_headroom(self):
measurements = [
{"status": "success", "batch_size": 1, "sum_tokens_per_second": 10, "peak_allocated_bytes": 5, "peak_reserved_bytes": 6, "full_context_memory_estimate": {"within_limit": True}},
{"status": "success", "batch_size": 4, "sum_tokens_per_second": 35, "peak_allocated_bytes": 8, "peak_reserved_bytes": 9, "full_context_memory_estimate": {"within_limit": True}},
{"status": "success", "batch_size": 16, "sum_tokens_per_second": 90, "peak_allocated_bytes": 10, "peak_reserved_bytes": 13, "full_context_memory_estimate": {"within_limit": True}},
{"status": "success", "batch_size": 32, "sum_tokens_per_second": 130, "peak_allocated_bytes": 9, "peak_reserved_bytes": 10, "full_context_memory_estimate": {"within_limit": False}},
{"status": "failed", "batch_size": 8},
]
best = benchmark_batch.select_candidate(measurements, 12)
self.assertEqual(best["batch_size"], 4)
self.assertIsNone(benchmark_batch.select_candidate(measurements, 5))
def test_full_context_estimate_uses_actual_qwen_dims_padding_and_gpu_reserve(self):
config = {"num_hidden_layers": 28, "num_attention_heads": 28,
"num_key_value_heads": 4, "hidden_size": 3584}
row = {"batch_size": 16, "per_prompt_input_tokens": [128, 256] * 8,
"per_prompt_tokens": [256] * 16, "peak_allocated_bytes": 7_100_000_000,
"peak_reserved_bytes": 7_360_000_000}
result = benchmark_batch.estimate_full_context_memory(row, config, 16_000_000_000, 12_500_000_000)
self.assertEqual(result["bytes_per_sequence_token"], 57344)
self.assertEqual(result["observed_cache_tokens"], 511)
self.assertEqual(result["additional_cache_tokens"], 4097)
self.assertEqual(result["estimated_peak_bytes"], 7_360_000_000 + 4097 * 16 * 57344)
self.assertEqual(result["effective_limit_bytes"], 12_500_000_000)
self.assertTrue(result["within_limit"])
smaller_gpu = benchmark_batch.estimate_full_context_memory(row, config, 12_000_000_000, 12_500_000_000)
self.assertEqual(smaller_gpu["effective_limit_bytes"], 10_000_000_000)
self.assertFalse(smaller_gpu["within_limit"])
larger = {**row, "batch_size": 32, "per_prompt_input_tokens": [256] * 32,
"per_prompt_tokens": [256] * 32, "peak_reserved_bytes": 9_000_000_000}
self.assertFalse(benchmark_batch.estimate_full_context_memory(larger, config, 16_000_000_000, 12_500_000_000)["within_limit"])
def test_memory_gate_rejects_old_report_without_estimate(self):
self.assertIsNone(benchmark_batch.select_candidate([
{"status": "success", "batch_size": 16, "sum_tokens_per_second": 80,
"peak_allocated_bytes": 7, "peak_reserved_bytes": 8}], 12))
def test_saved_backend_failure_blocks_published_rates(self):
with tempfile.TemporaryDirectory() as folder:
root = Path(folder)
item = root / "items/0000"
item.mkdir(parents=True)
(item / "result.json").write_text(json.dumps({"index": 0, "compile_success": False, "clean_run_60s": False}))
(root / "generation_infrastructure_error.json").write_text(json.dumps({"error": "CUDA out of memory", "indices": [0]}))
result = evaluate.summarize(root, 1, 60)
self.assertEqual(result["status"], "incomplete")
self.assertIsNone(result["compile_success_rate"])
self.assertGreater(result["infrastructure_failures"], 0)
def test_single_selected_batch_runs_full_capacity_without_baseline(self):
best = {"batch_size": 16}
self.assertIsNone(benchmark_batch.full_check_reason(best, None, [16], False))
self.assertIsNotNone(benchmark_batch.full_check_reason(None, None, [16], False))
self.assertIsNotNone(benchmark_batch.full_check_reason(best, None, [16], True))
self.assertIsNotNone(benchmark_batch.full_check_reason(best, None, [4, 16], False))
self.assertIsNone(benchmark_batch.full_check_reason(best, 4.5, [1, 4, 8, 16], False))
def test_malformed_batch_is_a_backend_error_before_persistence(self):
for rows in [[], [{"output": "valid"}] * 15, [{"output": None}] * 16,
["not a record"] * 16, {"output": "not a list"}]:
with self.assertRaises(RuntimeError):
evaluate.check_batch_outputs(rows, 16)
evaluate.check_batch_outputs([{"output": ""}] * 16, 16)
if __name__ == "__main__":
unittest.main()
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