File size: 6,495 Bytes
5a46e5d | 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 | from __future__ import annotations
import json
from pathlib import Path
import pytest
import torch
from safetensors.torch import save_model
from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.pre_tokenizers import Whitespace
from barunlm import BarunConfig, BarunLM
from barunlm.evaluation.generation import (
INT8_GENERATION_VERSION,
GenerationError,
generate_manifest,
)
from barunlm.quantization import export_dynamic_int8_checkpoint
from barunlm.training.data import EXAMPLE_SCHEMA_VERSION, sha256_file
def _tokenizer() -> Tokenizer:
vocabulary = {
"<pad>": 0,
"<unk>": 1,
"<eos>": 2,
"prompt": 3,
"answer": 4,
}
tokenizer = Tokenizer(WordLevel(vocab=vocabulary, unk_token="<unk>"))
tokenizer.pre_tokenizer = Whitespace()
return tokenizer
def _checkpoint(path: Path) -> dict[str, str]:
config = BarunConfig(
vocab_size=5,
dim=8,
n_layers=1,
n_heads=2,
n_kv_heads=1,
ffn_dim=16,
max_seq_len=8,
rope_fraction=0.5,
local_window=4,
full_attention_every=1,
attention_gate=False,
residual_select_every=0,
tie_embeddings=False,
)
model = BarunLM(config)
with torch.no_grad():
for parameter in model.parameters():
parameter.zero_()
path.mkdir()
save_model(model, path / "model.safetensors")
config.save_json(path / "barun_config.json")
_tokenizer().save(str(path / "tokenizer.json"))
return {
name: sha256_file(path / name)
for name in ("model.safetensors", "barun_config.json", "tokenizer.json")
}
def test_generation_preserves_raw_special_tokens_and_records_truncation(tmp_path: Path) -> None:
checkpoint = tmp_path / "checkpoint"
hashes = _checkpoint(checkpoint)
manifest = tmp_path / "dev.jsonl"
manifest.write_text(
json.dumps(
{
"schema_version": EXAMPLE_SCHEMA_VERSION,
"id": "one",
"prompt": "prompt",
"target": "answer",
"metadata": {},
}
)
+ "\n",
encoding="utf-8",
)
output = tmp_path / "predictions.jsonl"
summary = generate_manifest(
checkpoint_dir=checkpoint,
manifest_path=manifest,
manifest_sha256=sha256_file(manifest),
predictions_path=output,
device_name="cpu",
batch_size=1,
max_new_tokens=2,
expected_checkpoint_sha256=hashes,
)
record = json.loads(output.read_text(encoding="utf-8"))
assert record["prediction_raw"] == "<pad> <pad>"
assert record["truncated"] is True
assert record["generation_failure"] is None
assert summary.generated == 1
assert summary.truncated == 1
assert summary.predictions_sha256 == sha256_file(output)
assert "checkpoint_format" not in summary.to_dict()
assert "quantization_manifest_sha256" not in summary.to_dict()
def _qengine() -> str:
supported = tuple(
engine for engine in torch.backends.quantized.supported_engines if engine != "none"
)
if not supported:
raise AssertionError("the CPU test environment has no quantized engine")
return "qnnpack" if "qnnpack" in supported else supported[0]
@pytest.mark.filterwarnings("ignore:torch.ao.quantization is deprecated:DeprecationWarning")
@pytest.mark.filterwarnings("ignore:torch.quantize_per_tensor.*:UserWarning")
@pytest.mark.filterwarnings("ignore:TypedStorage is deprecated:UserWarning")
def test_generation_loads_explicit_int8_without_float_fallback(tmp_path: Path) -> None:
source = tmp_path / "source"
hashes = _checkpoint(source)
int8 = tmp_path / "int8"
exported = export_dynamic_int8_checkpoint(
source,
int8,
expected_source_sha256=hashes,
qengine=_qengine(),
)
manifest = tmp_path / "dev.jsonl"
manifest.write_text(
json.dumps(
{
"schema_version": EXAMPLE_SCHEMA_VERSION,
"id": "one",
"prompt": "prompt",
"target": "answer",
"metadata": {},
}
)
+ "\n",
encoding="utf-8",
)
summary = generate_manifest(
checkpoint_dir=int8,
checkpoint_format="int8",
expected_int8_manifest_sha256=exported.manifest_sha256,
manifest_path=manifest,
manifest_sha256=sha256_file(manifest),
predictions_path=tmp_path / "predictions.jsonl",
device_name="cpu",
batch_size=1,
max_new_tokens=2,
)
receipt = summary.to_dict()
assert summary.schema_version == INT8_GENERATION_VERSION
assert receipt["checkpoint_format"] == "int8"
assert receipt["quantization_manifest_sha256"] == exported.manifest_sha256
assert receipt["checkpoint_sha256"] == dict(exported.artifact_sha256)
assert receipt["source_checkpoint_sha256"] == hashes
assert receipt["qengine"] == exported.qengine
assert receipt["runtime"]["torch_version"] == str(torch.__version__)
@pytest.mark.parametrize(
("checkpoint_format", "device", "float_hashes", "int8_hash", "message"),
[
("int8", "cuda", None, "a" * 64, "requires device_name='cpu'"),
("int8", "cpu", None, None, "requires an expected_int8_manifest"),
("int8", "cpu", {"model.safetensors": "a" * 64}, "a" * 64, "only with"),
("float", "cpu", None, "a" * 64, "only with"),
("automatic", "cpu", None, None, "explicitly 'float' or 'int8'"),
],
)
def test_generation_rejects_incompatible_checkpoint_arguments_before_writing(
tmp_path: Path,
checkpoint_format: str,
device: str,
float_hashes: dict[str, str] | None,
int8_hash: str | None,
message: str,
) -> None:
predictions = tmp_path / "nested" / "predictions.jsonl"
with pytest.raises(GenerationError, match=message):
generate_manifest(
checkpoint_dir=tmp_path / "missing-checkpoint",
checkpoint_format=checkpoint_format,
expected_checkpoint_sha256=float_hashes,
expected_int8_manifest_sha256=int8_hash,
manifest_path=tmp_path / "missing-manifest",
manifest_sha256="b" * 64,
predictions_path=predictions,
device_name=device,
batch_size=1,
max_new_tokens=1,
)
assert not predictions.parent.exists()
|