mirror of
https://github.com/elder-plinius/OBLITERATUS.git
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99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
"""Pinned, networked tiny-model load plus cache-only replay."""
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from __future__ import annotations
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import os
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import uuid
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import pytest
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import torch
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from obliteratus.architecture_profiles import ArchitectureClass, detect_architecture
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from obliteratus.models.loader import _select_model_class, load_model
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from transformers import AutoConfig, AutoModelForImageTextToText
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pytestmark = [pytest.mark.network, pytest.mark.download]
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MODEL = "hf-internal-testing/tiny-random-gpt2"
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REVISION = "71034c5d8bde858ff824298bdedc65515b97d2b9"
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MISTRAL4_MODEL = "mistralai/Mistral-Small-4-119B-2603"
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MISTRAL4_REVISION = "a11f36bebf709121056b1dbcc943d1c6afbe494d"
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QWEN38_MODEL = "Qwen/Qwen3.8-27B"
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def test_pinned_tiny_model_download_inference_and_offline_cache(monkeypatch):
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handle = load_model(
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MODEL,
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revision=REVISION,
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device="cpu",
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dtype="float32",
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trust_remote_code=False,
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skip_snapshot=True,
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)
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encoded = handle.tokenizer("conditional gate", return_tensors="pt")
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with torch.no_grad():
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output = handle.model(**encoded)
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assert output.logits.shape[:2] == encoded["input_ids"].shape
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assert next(handle.model.parameters()).device.type == "cpu"
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handle.cleanup()
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monkeypatch.setenv("HF_HUB_OFFLINE", "1")
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monkeypatch.setenv("TRANSFORMERS_OFFLINE", "1")
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cached = load_model(
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MODEL,
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revision=REVISION,
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device="cpu",
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local_files_only=True,
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trust_remote_code=False,
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skip_snapshot=True,
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)
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assert cached.model_name == MODEL
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cached.cleanup()
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missing = f"obliteratus/offline-missing-{uuid.uuid4().hex}"
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with pytest.raises(OSError):
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load_model(missing, revision=REVISION, device="cpu", local_files_only=True)
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def test_pinned_mistral4_config_resolves_composite_contract_without_remote_code():
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config = AutoConfig.from_pretrained(
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MISTRAL4_MODEL,
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revision=MISTRAL4_REVISION,
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trust_remote_code=False,
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)
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assert config.model_type == "mistral3"
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assert config.architectures == ["Mistral3ForConditionalGeneration"]
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assert config.text_config.model_type == "mistral4"
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assert config.text_config.n_routed_experts == 128
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assert config.text_config.num_experts_per_tok == 4
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assert _select_model_class("causal_lm", config) is AutoModelForImageTextToText
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profile = detect_architecture(MISTRAL4_MODEL, config=config)
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assert profile.model_type == "mistral4"
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assert profile.arch_class is ArchitectureClass.LARGE_MOE
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assert (profile.num_experts, profile.num_active_experts) == (128, 4)
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@pytest.mark.gpu
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def test_qwen38_bf16_pristine_baseline_blocks_unvalidated_surgery(tmp_path):
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"""Operator-gated 27B regression: healthy stock model, zero modified weights."""
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if os.environ.get("OBLITERATUS_QWEN38_E2E") != "1":
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pytest.skip("set OBLITERATUS_QWEN38_E2E=1 on a >=80 GiB GPU runner")
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from obliteratus.abliterate import AbliterationPipeline, PipelineValidationError
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pipeline = AbliterationPipeline(
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QWEN38_MODEL,
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output_dir=str(tmp_path / "qwen38-output"),
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dtype="bfloat16",
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quantization=None,
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)
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pipeline._summon()
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pipeline._capture_stock_baseline()
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assert pipeline._stock_baseline["perplexity"] > 0
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assert pipeline._stock_baseline["coherence"] > 0
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with pytest.raises(PipelineValidationError, match="no validated projection allowlist"):
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pipeline._validate_architecture_surgery_support()
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assert pipeline._excise_modified_count is None
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