File size: 8,801 Bytes
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import importlib.util
import json
from pathlib import Path
import pytest
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from transformers import (
Qwen3_5Config,
Qwen3_5ForConditionalGeneration,
Qwen3_5TextConfig,
)
spec = importlib.util.spec_from_file_location(
"export_low", Path(__file__).parents[1] / "export_low.py"
)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
def test_actual_32_to_16_prefix_logits_and_saved_tensors(tmp_path):
torch.set_num_threads(1)
text = Qwen3_5TextConfig(
vocab_size=64,
hidden_size=32,
intermediate_size=48,
num_hidden_layers=32,
num_attention_heads=2,
num_key_value_heads=1,
head_dim=16,
linear_key_head_dim=8,
linear_value_head_dim=8,
linear_num_key_heads=2,
linear_num_value_heads=2,
layer_types=(["linear_attention"] * 3 + ["full_attention"]) * 8,
rope_parameters={
"rope_type": "default",
"rope_theta": 10000.0,
"partial_rotary_factor": 1.0,
"mrope_section": [2, 3, 3],
},
)
config = Qwen3_5Config(
text_config=text,
vision_config={
"depth": 1,
"hidden_size": 32,
"intermediate_size": 48,
"num_heads": 2,
"out_hidden_size": 32,
"num_position_embeddings": 16,
},
)
config._attn_implementation = "eager"
config.text_config._attn_implementation = "eager"
config.text_config.num_hidden_layers = 32
config.text_config.layer_types = (["linear_attention"] * 3 + ["full_attention"]) * 8
config.tie_word_embeddings = False
config.text_config.tie_word_embeddings = False
torch.manual_seed(47)
full = Qwen3_5ForConditionalGeneration(config).eval()
state = full.state_dict()
source, output = tmp_path / "source", tmp_path / "low"
source.mkdir()
output.mkdir()
save_file(state, source / "model.safetensors", metadata={"format": "pt"})
weight_map = {k: "model.safetensors" for k in state}
kept, removed = module.select_keys(weight_map, 16, 32)
assert len(removed) > 0
assert module.READOUT <= set(kept)
assert all(
not module.LAYER.match(k) or int(module.LAYER.match(k).group(1)) < 16
for k in kept
)
files, tensors = module.copy_weights(source, output, weight_map, kept)
short_config = module.prefix_config(config.to_dict())
assert short_config["vision_config"] == config.to_dict()["vision_config"]
assert short_config["text_config"]["num_hidden_layers"] == 16
assert len(short_config["text_config"]["layer_types"]) == 16
loaded = {}
for f in files:
with safe_open(output / f["file"], framework="pt") as handle:
loaded.update({key: handle.get_tensor(key) for key in handle.keys()})
low_config = Qwen3_5Config.from_dict(short_config)
low_config._attn_implementation = "eager"
low_config.text_config._attn_implementation = "eager"
low = Qwen3_5ForConditionalGeneration(low_config).eval()
low.load_state_dict(loaded, strict=True)
assert len(low.model.language_model.layers) == 16
assert len(tensors) == len(loaded)
for key in module.READOUT:
torch.testing.assert_close(loaded[key], state[key], rtol=0, atol=0)
ids, candidates = torch.tensor([[4, 5, 6, 7]]), torch.tensor([10, 11, 12])
with torch.no_grad():
captured = []
hook = full.model.language_model.layers[15].register_forward_hook(
lambda _module, _inputs, output: captured.append(output.detach().clone())
)
try:
full(input_ids=ids, use_cache=False)
finally:
hook.remove()
# Independently capture the full model's layer-16 residual, then apply
# its original final norm and head. No monorepo runtime dependency.
native = full.lm_head(
full.model.language_model.norm(captured[0][:, -1])
).index_select(-1, candidates)
exported = (
low(input_ids=ids, use_cache=False)
.logits[:, -1]
.index_select(-1, candidates)
)
torch.testing.assert_close(native, exported, atol=1e-6, rtol=1e-5)
assert (
json.loads((output / "model.safetensors.index.json").read_text())["metadata"][
"total_size"
]
> 0
)
@pytest.mark.parametrize("missing", sorted(module.READOUT))
def test_readout_cannot_be_dropped(missing):
keys = (module.READOUT - {missing}) | {
f"model.language_model.layers.{i}.weight" for i in range(32)
}
with pytest.raises(ValueError, match="missing"):
module.select_keys(keys, 16, 32)
def test_unknown_adapter_namespace_rejected():
keys = (
module.READOUT
| {f"model.language_model.layers.{i}.weight" for i in range(32)}
| {"readout.adapter.weight"}
)
with pytest.raises(ValueError, match="unexpected"):
module.select_keys(keys, 16, 32)
def test_existing_output_never_overwritten(tmp_path):
with pytest.raises(ValueError, match="new directory"):
module.export(tmp_path / "unused", tmp_path)
def test_source_stat_change_rejects_verified_publication(tmp_path):
path = tmp_path / "weights"
path.write_bytes(b"original")
snapshot = module.source_snapshot(tmp_path)
module.assert_source_unchanged(tmp_path, snapshot)
path.write_bytes(b"changed-longer")
with pytest.raises(ValueError, match="changed during export"):
module.assert_source_unchanged(tmp_path, snapshot)
def test_launcher_full_low_weight_hash_detects_tampering(tmp_path):
config = {
"model_type": "qwen3_5",
"text_config": {
"hidden_size": 4096,
"vocab_size": 248320,
"num_hidden_layers": 16,
"layer_types": ["linear_attention"] * 16,
},
}
(tmp_path / "config.json").write_text(json.dumps(config))
keys = module.READOUT | {
f"model.language_model.layers.{i}.weight" for i in range(16)
}
(tmp_path / "model.safetensors.index.json").write_text(
json.dumps({"weight_map": {key: "model.safetensors" for key in keys}})
)
shard = tmp_path / "model.safetensors"
shard.write_bytes(b"original-weights")
manifest = {
"status": "verified",
"actual_depth": 16,
"all_retained_tensors_bit_identical": True,
"export_config_sha256": module.file_sha(tmp_path / "config.json"),
"export_index_sha256": module.file_sha(
tmp_path / "model.safetensors.index.json"
),
"export_weight_files": [
{
"file": shard.name,
"bytes": shard.stat().st_size,
"sha256": module.file_sha(shard),
}
],
}
(tmp_path / "export-low-provenance.json").write_text(json.dumps(manifest))
module.verify_artifact(tmp_path, 16)
shard.write_bytes(b"tampered-weights")
with pytest.raises(ValueError, match="weight SHA mismatch"):
module.verify_artifact(tmp_path, 16)
def test_launcher_high_release_weight_hash_detects_tampering(tmp_path):
config = {
"model_type": "qwen3_5",
"architectures": ["Qwen3_5ForConditionalGeneration"],
"tie_word_embeddings": False,
"text_config": {
"hidden_size": 4096,
"vocab_size": 248320,
"intermediate_size": 12288,
"num_hidden_layers": 32,
"tie_word_embeddings": False,
"layer_types": (["linear_attention"] * 3 + ["full_attention"]) * 8,
},
}
(tmp_path / "config.json").write_text(json.dumps(config))
(tmp_path / "depth_config.json").write_text(
json.dumps({"exit_depth": 16, "full_depth": 32})
)
for name in ("tokenizer.json", "tokenizer_config.json", "chat_template.jinja"):
(tmp_path / name).write_text("mock tokenization asset")
keys = module.READOUT | {
f"model.language_model.layers.{i}.weight" for i in range(32)
}
(tmp_path / "model.safetensors.index.json").write_text(
json.dumps({"weight_map": {key: "model.safetensors" for key in keys}})
)
shard = tmp_path / "model.safetensors"
shard.write_bytes(b"original-weights")
entries = [
{"path": p.name, "sha256": module.file_sha(p)} for p in tmp_path.iterdir()
]
(tmp_path / "release-manifest.json").write_text(json.dumps({"files": entries}))
module.verify_artifact(tmp_path, 32)
shard.write_bytes(b"tampered-weights")
with pytest.raises(ValueError, match="weight SHA mismatch"):
module.verify_artifact(tmp_path, 32)
|