* Auto-install SSM kernels (causal-conv1d, mamba-ssm) for inference loads Mamba/SSM hybrids (Nemotron-H/Nano, Falcon-H1, Granite-4.0-H, ...) lazily import mamba_ssm / causal_conv1d during from_pretrained, so loading them for chat failed with 'mamba-ssm is required by the Mamba model but cannot be imported'. The training worker already wheel-first installs these before a fine-tune; the inference worker did not. Add utils/ssm_runtime.ensure_ssm_runtime and call it from the inference load path so the same models load for inference. Training worker is untouched; a drift test keeps the shared detection and pinned versions in lockstep. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ssm_runtime: invalidate import caches, skip MLX, cover LoRA base - Invalidate importlib finder caches in _is_importable and after a successful wheel install, so a kernel installed earlier in this same process is actually importable when the modeling code lazy-imports it during from_pretrained. - Skip the SSM kernel install entirely on the MLX (Apple Silicon) load path: these are CUDA/ROCm Torch kernels with no MLX use and no macOS prebuilt wheel, so the source build would fail before the MLX backend loads the model. - For LoRA loads, also run detection over the resolved base model, since an adapter id like 'me/my-lora' won't match the SSM heuristics but its SSM base (Nemotron-H, ...) is what needs the kernels. Adds tests for cache invalidation and the MLX-skip / LoRA-base worker wiring. * Tighten SSM autoinstall comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ssm_runtime: verify wheel imports, HIP-aware source build, build heartbeat Address review feedback: - Verify a prebuilt wheel actually imports before trusting it; a CUDA/ABI-mismatched wheel now falls back to a source build instead of returning success and failing later with the cryptic lazy-import error. - HIP-aware source build: require hipcc on ROCm, inject clang --gcc-install-dir, and use the 1800s timeout, mirroring the training worker (ROCm has no prebuilt wheel). - Emit a status heartbeat every 60s during the source build so a long (ROCm) build does not trip the orchestrator's 300s inactivity timeout. Tests cover the wheel-not-importable fallback and the missing-hipcc ROCm bail. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Make causal-conv1d best-effort and harden the SSM source build - causal-conv1d is a fast path: models that merely want it (Qwen3-Next, LFM2) fall back to torch, so a failed install must not reject an otherwise loadable chat model on Windows/CPU/macOS or an ABI without a wheel. Only a true SSM model's mamba-ssm requirement stays fatal, matching the training worker which treats causal-conv1d as best-effort. - The source build is reached only when not importable, including a wheel that installed but failed to import; add --reinstall/--force-reinstall so it replaces the broken install instead of no-opping as already satisfied. - Add --no-cache to the ROCm uv source build to avoid reusing stale artifacts from a partial HIP build, mirroring the training worker. * Address review: install SSM kernels before transformers, harden import + Windows Codex: - Install the SSM kernels before importing transformers. run_inference_process imported core.inference.inference (which imports unsloth/transformers) before the load, and a sidecar transformers can evaluate its optional-backend gates against the import state; installing causal_conv1d/mamba_ssm afterwards left those gates unsatisfied and a Nemotron/Falcon/Granite load still failed with "mamba-ssm is required". The initial model's kernels are now installed in run_inference_process before the ML import, via a shared _ensure_ssm_kernels helper; _handle_load keeps calling it (idempotent) for a LoRA's base and for later in-process loads. - _is_importable now treats any import failure as "not importable", not only ImportError. An ABI-incompatible native kernel (undefined symbol after a torch/CUDA upgrade) raises OSError/RuntimeError; letting those escape reported ssm_runtime_install_failed instead of falling back to reinstall/source build. - Skip causal-conv1d on Windows (no prebuilt wheel), mirroring the training worker. A causal-conv1d-only model (Qwen3-Next/LFM2) no longer drops a chat load into a multi-minute untimed source build; it uses the torch fallback. mamba-ssm is still attempted for true SSM hybrids. Tests: test_ssm_runtime.py +5 (broken-kernel exceptions read as not-importable; causal-conv1d skipped on win32 while mamba-ssm still installs). 36 passed. * Trim comments to be more succinct * Run security gates before installing SSM kernels The SSM kernel auto-install is name-based (model_is_ssm is a substring match, no config fetch), so a model id merely containing an SSM substring triggered a native-package install (possibly a slow source build) before the malware and remote-code consent gates ran. Extract those gates into _run_security_gates and call it before the kernel install in both the pre-import path of run_inference_process and in _handle_load, so a blocked or nonexistent model is refused before any build. The gates are metadata-only and do not import transformers, so they are safe to run before the pre-import install. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve remote LoRA bases before importing transformers _resolve_base_model only reads a local adapter_config.json, so a remote LoRA adapter whose own id has no SSM substring but whose base is a Nemotron/Falcon/ Granite model had its base discovered only by ModelConfig in _handle_load, after transformers was imported and its optional-backend availability snapshotted, so the SSM kernel install there was too late. Add _remote_lora_base, a metadata-only adapter_config.json fetch (no huggingface_hub / transformers import), and use it in the pre-import path so the base is gated and its kernels pre-installed. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Gate only loaded roots, tier on the resolved base, read offline LoRA cache Three follow-ups to the pre-import resolution: - The security gate reused the SSM target list, which for a local full fine-tune includes the config.json-recorded base. That base is never loaded, so scanning it could falsely block a safe local checkpoint. Gate only the model plus a genuine LoRA base (matching _handle_load's mc.is_lora), separate from the broader SSM-install list. - Tier activation ran on the raw adapter id, so a remote LoRA whose base needs a sidecar transformers version imported the default and failed. Resolve the base once up front and activate on it. - _remote_lora_base bailed on offline before checking the hub cache, missing a cached adapter's base. Read the cached adapter_config.json when offline or when the fetch fails. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Keep the pre-import gate transformers-free; harden remote LoRA resolution The pre-import security gate called security_load_subdirs, which imports model_config and thus transformers, snapshotting optional-backend availability before the SSM kernels are installed and defeating the ordering. Add compute_subdirs to _run_security_gates and pass False in the preflight so it scans from the root only (transformers-free); _handle_load still runs the authoritative gate with full subdir scoping after the import. _remote_lora_base now skips existing local relative paths (is_local_path) so a checkpoint like outputs/run1 is never treated as a Hub repo, and distinguishes a definitive 404 (not a LoRA -> None) from transient/offline failures (read the cache), so a repo that is now a full model no longer resolves a stale cached base. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Probe a real model id for SSM kernels; respect HF_ENDPOINT model_is_ssm is a substring match, so an arbitrary name could false-match and force a mamba-ssm install that fails the load for a non-SSM model: - a LoRA adapter id like user/falcon-h1-lora (the SSM-relevant code is the base's); - a local checkpoint under an SSM-named parent dir, e.g. /runs/falcon-h1/llama-ckpt. Add ssm_probe_identifier, which resolves the base (or a bare local checkpoint's basename) and feed that to ensure_ssm_runtime from both the pre-import path and _handle_load, so detection runs against a real model id, never an adapter id or parent folders. _remote_lora_base now honors HF_ENDPOINT so enterprise/mirror deployments resolve the adapter base instead of always hitting huggingface.co. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Tighten comments in the pre-import SSM gate/install path --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
1265 lines
52 KiB
Python
1265 lines
52 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Tests for transformers version detection with local checkpoint fallbacks."""
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import json
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import logging
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import os
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import pytest
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from pathlib import Path
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from unittest.mock import patch
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# ---------------------------------------------------------------------------
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# The studio backend uses relative-style imports (``from utils.…``), so
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# add the backend directory to *sys.path* if not already present.
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# ---------------------------------------------------------------------------
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import sys
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_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
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if _BACKEND_DIR not in sys.path:
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sys.path.insert(0, _BACKEND_DIR)
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# Stub the custom logger before import so ``from loggers import
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# get_logger`` doesn't fail.
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import types as _types
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_loggers_stub = _types.ModuleType("loggers")
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_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
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sys.modules.setdefault("loggers", _loggers_stub)
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from utils.transformers_version import (
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_resolve_base_model,
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_remote_lora_base,
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_check_tokenizer_config_needs_v5,
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_check_config_needs_510,
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_check_config_needs_550,
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_config_needs_510,
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_nemotron_h_needs_mlp_support,
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_config_json_from_hf_cache,
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_load_config_json,
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_higher_tier,
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_config_json_cache,
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_tokenizer_class_cache,
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_config_needs_510_cache,
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_config_needs_550_cache,
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needs_transformers_5,
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get_transformers_tier,
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activate_transformers_for_subprocess,
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_venv_dir_is_valid,
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_ensure_venv_dir,
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)
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@pytest.fixture(autouse = True)
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def _capturable_logger(monkeypatch):
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"""Make the ``caplog`` assertions independent of test collection order.
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The ``sys.modules.setdefault("loggers", ...)`` stub above only installs the
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stdlib-logger stub when ``loggers`` has not been imported yet. In a full
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backend pytest run another module (e.g. ``test_log_filter_no_truncation``,
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collected earlier) imports the real ``loggers`` first, so the stub is a
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no-op and ``transformers_version.logger`` ends up a structlog/stdout logger
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that ``caplog`` cannot see -- the tier/activation/install log assertions
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would then fail even though the line was emitted. Bind a real stdlib logger
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for the duration of each test so the module logs through ``logging`` and
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``caplog`` captures them regardless of import order.
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"""
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monkeypatch.setattr(
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"utils.transformers_version.logger",
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logging.getLogger("utils.transformers_version"),
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)
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# ---------------------------------------------------------------------------
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# _resolve_base_model — config.json fallback
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# ---------------------------------------------------------------------------
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class TestResolveBaseModel:
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"""Tests for _resolve_base_model() local config fallbacks."""
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def test_adapter_config_takes_priority(self, tmp_path: Path):
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"""adapter_config.json should be preferred over config.json."""
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adapter_cfg = {"base_model_name_or_path": "meta-llama/Llama-3-8B"}
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config_cfg = {"_name_or_path": "different/model"}
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(tmp_path / "adapter_config.json").write_text(json.dumps(adapter_cfg))
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(tmp_path / "config.json").write_text(json.dumps(config_cfg))
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result = _resolve_base_model(str(tmp_path))
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assert result == "meta-llama/Llama-3-8B"
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def test_config_json_fallback_model_name(self, tmp_path: Path):
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"""config.json model_name should resolve when no adapter_config."""
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config_cfg = {"model_name": "Qwen/Qwen3.5-9B"}
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(tmp_path / "config.json").write_text(json.dumps(config_cfg))
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result = _resolve_base_model(str(tmp_path))
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assert result == "Qwen/Qwen3.5-9B"
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def test_config_json_fallback_name_or_path(self, tmp_path: Path):
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"""config.json _name_or_path should resolve as secondary fallback."""
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config_cfg = {"_name_or_path": "Qwen/Qwen3.5-9B"}
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(tmp_path / "config.json").write_text(json.dumps(config_cfg))
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result = _resolve_base_model(str(tmp_path))
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assert result == "Qwen/Qwen3.5-9B"
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def test_model_name_takes_priority_over_name_or_path(self, tmp_path: Path):
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"""model_name should be preferred over _name_or_path."""
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config_cfg = {
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"model_name": "Qwen/Qwen3.5-9B",
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"_name_or_path": "some/other-model",
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}
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(tmp_path / "config.json").write_text(json.dumps(config_cfg))
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result = _resolve_base_model(str(tmp_path))
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assert result == "Qwen/Qwen3.5-9B"
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def test_config_json_skips_self_referencing(self, tmp_path: Path):
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"""config.json should be ignored if model_name == the checkpoint path."""
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config_cfg = {"model_name": str(tmp_path)}
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(tmp_path / "config.json").write_text(json.dumps(config_cfg))
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result = _resolve_base_model(str(tmp_path))
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# Falls through; does not return the self-referencing path.
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assert result == str(tmp_path)
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def test_no_config_files(self, tmp_path: Path):
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"""Returns original name when no config files are present."""
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result = _resolve_base_model(str(tmp_path))
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assert result == str(tmp_path)
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def test_plain_hf_id_passthrough(self):
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"""Plain HuggingFace model IDs pass through unchanged."""
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result = _resolve_base_model("meta-llama/Llama-3-8B")
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assert result == "meta-llama/Llama-3-8B"
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class TestRemoteLoraBase:
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"""_remote_lora_base reads a remote adapter's base from its Hub adapter_config.json."""
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@staticmethod
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def _resp(cfg: dict):
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class _Resp:
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def __enter__(self):
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return self
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def __exit__(self, *a):
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return False
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def read(self):
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return json.dumps(cfg).encode()
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return _Resp()
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def test_fetches_base_from_remote_adapter_config(self, monkeypatch):
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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cfg = {"base_model_name_or_path": "nvidia/NVIDIA-Nemotron-3-Nano-4B"}
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with patch("urllib.request.urlopen", return_value = self._resp(cfg)):
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assert (
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_remote_lora_base("someuser/my-nemotron-lora") == "nvidia/NVIDIA-Nemotron-3-Nano-4B"
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)
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def test_local_or_noncanonical_returns_none(self):
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assert _remote_lora_base("/local/dir/adapter") is None
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assert _remote_lora_base("plainname") is None
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def test_respects_hf_endpoint(self, monkeypatch):
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# Enterprise mirror: the fetch must target HF_ENDPOINT, not hardcoded huggingface.co.
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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monkeypatch.setenv("HF_ENDPOINT", "https://hf.mirror.internal")
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seen = {}
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def fake_urlopen(req, timeout = 10):
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seen["url"] = req.full_url
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return self._resp({"base_model_name_or_path": "org/base"})
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with patch("urllib.request.urlopen", side_effect = fake_urlopen):
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assert _remote_lora_base("user/adapter") == "org/base"
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assert seen["url"].startswith("https://hf.mirror.internal/user/adapter/raw/main/")
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@staticmethod
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def _seed_adapter_cache(
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hub: Path,
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repo_id: str,
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base: str,
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commit: str = "deadbeef",
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):
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repo = hub / ("models--" + repo_id.replace("/", "--"))
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snap = repo / "snapshots" / commit
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snap.mkdir(parents = True)
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(snap / "adapter_config.json").write_text(json.dumps({"base_model_name_or_path": base}))
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(repo / "refs").mkdir(parents = True)
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(repo / "refs" / "main").write_text(commit)
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def test_offline_reads_base_from_hf_cache(self, tmp_path: Path, monkeypatch):
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self._seed_adapter_cache(tmp_path, "user/cached-lora", "nvidia/Nemotron-H-8B")
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.setenv("HF_HUB_OFFLINE", "1")
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with patch("urllib.request.urlopen") as mock_url:
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assert _remote_lora_base("user/cached-lora") == "nvidia/Nemotron-H-8B"
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mock_url.assert_not_called() # offline: cache only, no network
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def test_fetch_failure_falls_back_to_cache(self, tmp_path: Path, monkeypatch):
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self._seed_adapter_cache(tmp_path, "user/cached-lora", "nvidia/Nemotron-H-8B")
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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with patch("urllib.request.urlopen", side_effect = OSError("boom")):
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assert _remote_lora_base("user/cached-lora") == "nvidia/Nemotron-H-8B"
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def test_offline_uncached_makes_no_request(self, tmp_path: Path, monkeypatch):
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.setenv("HF_HUB_OFFLINE", "1")
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with patch("urllib.request.urlopen") as mock_url:
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assert _remote_lora_base("org/adapter") is None
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mock_url.assert_not_called()
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def test_non_adapter_repo_returns_none(self, tmp_path: Path, monkeypatch):
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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with patch("urllib.request.urlopen", side_effect = OSError("boom")):
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assert _remote_lora_base("org/not-an-adapter") is None
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def test_existing_relative_path_not_treated_as_repo(self, monkeypatch):
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# An existing one-slash relative path (e.g. outputs/run1) is a local checkpoint, not
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# a Hub repo: no request, no risk of matching an unrelated remote/cached adapter.
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import utils.paths as paths
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monkeypatch.setattr(paths, "is_local_path", lambda p: True)
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with patch("urllib.request.urlopen") as mock_url:
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assert _remote_lora_base("outputs/run1") is None
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mock_url.assert_not_called()
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def test_404_returns_none_not_stale_cache(self, tmp_path: Path, monkeypatch):
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import urllib.error
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# The repo is now a full model (adapter_config.json 404s) but a stale LoRA snapshot is
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# cached: a definitive 404 must return None, not the stale base.
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self._seed_adapter_cache(tmp_path, "user/was-a-lora", "old/base")
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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err = urllib.error.HTTPError("url", 404, "Not Found", {}, None)
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with patch("urllib.request.urlopen", side_effect = err):
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assert _remote_lora_base("user/was-a-lora") is None
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def test_transient_http_error_falls_back_to_cache(self, tmp_path: Path, monkeypatch):
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import urllib.error
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self._seed_adapter_cache(tmp_path, "user/cached-lora", "nvidia/Nemotron-H-8B")
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monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
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monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
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err = urllib.error.HTTPError("url", 503, "Service Unavailable", {}, None)
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with patch("urllib.request.urlopen", side_effect = err):
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assert _remote_lora_base("user/cached-lora") == "nvidia/Nemotron-H-8B"
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# ---------------------------------------------------------------------------
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# _check_tokenizer_config_needs_v5 — local file check
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# ---------------------------------------------------------------------------
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class TestCheckTokenizerConfigNeedsV5:
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"""Tests for local tokenizer_config.json fallback."""
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def setup_method(self):
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_tokenizer_class_cache.clear()
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def test_local_tokenizer_config_v5(self, tmp_path: Path):
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"""Local tokenizer_config.json with v5 tokenizer should return True."""
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tc = {"tokenizer_class": "TokenizersBackend"}
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(tmp_path / "tokenizer_config.json").write_text(json.dumps(tc))
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result = _check_tokenizer_config_needs_v5(str(tmp_path))
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assert result is True
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def test_local_tokenizer_config_v4(self, tmp_path: Path):
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"""Local tokenizer_config.json with standard tokenizer should return False."""
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tc = {"tokenizer_class": "LlamaTokenizerFast"}
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(tmp_path / "tokenizer_config.json").write_text(json.dumps(tc))
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result = _check_tokenizer_config_needs_v5(str(tmp_path))
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assert result is False
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def test_local_file_skips_network(self, tmp_path: Path):
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"""When local file exists, no network request should be made."""
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tc = {"tokenizer_class": "LlamaTokenizerFast"}
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(tmp_path / "tokenizer_config.json").write_text(json.dumps(tc))
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with patch("urllib.request.urlopen") as mock_urlopen:
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result = _check_tokenizer_config_needs_v5(str(tmp_path))
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mock_urlopen.assert_not_called()
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assert result is False
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def test_result_is_cached(self, tmp_path: Path):
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"""Subsequent calls should use the cache."""
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tc = {"tokenizer_class": "TokenizersBackend"}
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(tmp_path / "tokenizer_config.json").write_text(json.dumps(tc))
|
|
|
|
key = str(tmp_path)
|
|
_check_tokenizer_config_needs_v5(key)
|
|
assert key in _tokenizer_class_cache
|
|
assert _tokenizer_class_cache[key] is True
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# needs_transformers_5 — integration-level
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestNeedsTransformers5:
|
|
"""Integration tests for the top-level needs_transformers_5() function."""
|
|
|
|
def setup_method(self):
|
|
_tokenizer_class_cache.clear()
|
|
|
|
def test_qwen35_substring(self):
|
|
assert needs_transformers_5("Qwen/Qwen3.5-9B") is True
|
|
|
|
def test_qwen3_30b_a3b_substring(self):
|
|
assert needs_transformers_5("Qwen/Qwen3-30B-A3B-Instruct-2507") is True
|
|
|
|
def test_ministral_substring(self):
|
|
assert needs_transformers_5("mistralai/Ministral-3-8B-Instruct-2512") is True
|
|
|
|
def test_llama_does_not_need_v5(self):
|
|
"""Standard models should not trigger v5."""
|
|
# Patch network call to avoid a real fetch.
|
|
with patch(
|
|
"utils.transformers_version._check_tokenizer_config_needs_v5",
|
|
return_value = False,
|
|
):
|
|
assert needs_transformers_5("meta-llama/Llama-3-8B") is False
|
|
|
|
def test_local_checkpoint_resolved_via_config(self, tmp_path: Path):
|
|
"""Local checkpoint with config.json pointing to Qwen3.5 needs v5."""
|
|
config_cfg = {"model_name": "Qwen/Qwen3.5-9B"}
|
|
(tmp_path / "config.json").write_text(json.dumps(config_cfg))
|
|
|
|
# needs_transformers_5 only does substring matching, so test the
|
|
# full resolution chain via _resolve_base_model here.
|
|
resolved = _resolve_base_model(str(tmp_path))
|
|
assert needs_transformers_5(resolved) is True
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _check_config_needs_550 — config.json architecture/model_type check
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestCheckConfigNeeds550:
|
|
"""Tests for _check_config_needs_550() local config.json checks."""
|
|
|
|
def setup_method(self):
|
|
_config_json_cache.clear()
|
|
_config_needs_550_cache.clear()
|
|
|
|
def test_gemma4_architecture(self, tmp_path: Path):
|
|
"""config.json with Gemma4ForConditionalGeneration should return True."""
|
|
cfg = {
|
|
"architectures": ["Gemma4ForConditionalGeneration"],
|
|
"model_type": "gemma4",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_550(str(tmp_path)) is True
|
|
|
|
def test_gemma4_model_type_only(self, tmp_path: Path):
|
|
"""config.json with model_type=gemma4 (no architectures) should return True."""
|
|
cfg = {"model_type": "gemma4"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_550(str(tmp_path)) is True
|
|
|
|
def test_llama_architecture(self, tmp_path: Path):
|
|
"""config.json with LlamaForCausalLM should return False."""
|
|
cfg = {"architectures": ["LlamaForCausalLM"], "model_type": "llama"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_550(str(tmp_path)) is False
|
|
|
|
def test_no_config_json(self, tmp_path: Path):
|
|
"""Missing config.json should return False (fail-open)."""
|
|
# Patch network call to avoid a real fetch.
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
mock_urlopen.side_effect = Exception("no network")
|
|
assert _check_config_needs_550(str(tmp_path)) is False
|
|
|
|
def test_result_is_cached(self, tmp_path: Path):
|
|
"""Subsequent calls should use the cache."""
|
|
cfg = {"architectures": ["Gemma4ForConditionalGeneration"]}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
key = str(tmp_path)
|
|
_check_config_needs_550(key)
|
|
assert key in _config_needs_550_cache
|
|
assert _config_needs_550_cache[key] is True
|
|
|
|
def test_local_file_skips_network(self, tmp_path: Path):
|
|
"""When local config.json exists, no network request should be made."""
|
|
cfg = {"architectures": ["LlamaForCausalLM"]}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
_check_config_needs_550(str(tmp_path))
|
|
mock_urlopen.assert_not_called()
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _check_config_needs_510 — config.json architecture/model_type check
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestCheckConfigNeeds510:
|
|
"""Tests for _check_config_needs_510() local config.json checks."""
|
|
|
|
def setup_method(self):
|
|
_config_json_cache.clear()
|
|
_config_needs_510_cache.clear()
|
|
|
|
def test_gemma4_unified_architecture(self, tmp_path: Path):
|
|
"""config.json with Gemma4UnifiedForConditionalGeneration should return True."""
|
|
cfg = {
|
|
"architectures": ["Gemma4UnifiedForConditionalGeneration"],
|
|
"model_type": "gemma4_unified",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_unified_model_type_only(self, tmp_path: Path):
|
|
"""config.json with model_type=gemma4_unified should return True."""
|
|
cfg = {"model_type": "gemma4_unified"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_unified_assistant_architecture(self, tmp_path: Path):
|
|
"""Assistant Gemma 4 Unified configs should return True."""
|
|
cfg = {
|
|
"architectures": ["Gemma4UnifiedAssistantForCausalLM"],
|
|
"model_type": "gemma4_unified_assistant",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_unified_assistant_model_type_only(self, tmp_path: Path):
|
|
"""Assistant Gemma 4 Unified model_type should return True."""
|
|
cfg = {"model_type": "gemma4_unified_assistant"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_assistant_architecture(self, tmp_path: Path):
|
|
"""Assistant Gemma 4 configs should return True."""
|
|
cfg = {
|
|
"architectures": ["Gemma4AssistantForCausalLM"],
|
|
"model_type": "gemma4_assistant",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_assistant_model_type_only(self, tmp_path: Path):
|
|
"""Assistant Gemma 4 model_type should return True."""
|
|
cfg = {"model_type": "gemma4_assistant"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is True
|
|
|
|
def test_gemma4_non_unified_returns_false(self, tmp_path: Path):
|
|
"""Older Gemma 4 config should stay on the 550 tier."""
|
|
cfg = {
|
|
"architectures": ["Gemma4ForConditionalGeneration"],
|
|
"model_type": "gemma4",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert _check_config_needs_510(str(tmp_path)) is False
|
|
|
|
def test_no_config_json(self, tmp_path: Path):
|
|
"""Missing config.json should return False (fail-open)."""
|
|
# Patch network call to avoid real fetch
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
mock_urlopen.side_effect = Exception("no network")
|
|
assert _check_config_needs_510(str(tmp_path)) is False
|
|
|
|
def test_result_is_cached(self, tmp_path: Path):
|
|
"""Subsequent calls should use the cache."""
|
|
cfg = {"architectures": ["Gemma4UnifiedForConditionalGeneration"]}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
key = str(tmp_path)
|
|
_check_config_needs_510(key)
|
|
assert key in _config_needs_510_cache
|
|
assert _config_needs_510_cache[key] is True
|
|
|
|
def test_local_file_skips_network(self, tmp_path: Path):
|
|
"""When local config.json exists, no network request should be made."""
|
|
cfg = {"architectures": ["LlamaForCausalLM"]}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
_check_config_needs_510(str(tmp_path))
|
|
mock_urlopen.assert_not_called()
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# NemotronH dense (MLP) models need the 5.10 tier
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestNemotronHNeedsMlpSupport:
|
|
"""Dense NemotronH configs (MLP layers) require transformers >= 5.10."""
|
|
|
|
def test_hybrid_override_pattern_with_dash(self):
|
|
cfg = {
|
|
"model_type": "nemotron_h",
|
|
"hybrid_override_pattern": "M-M-M*-M-",
|
|
}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is True
|
|
|
|
def test_layers_block_type_with_mlp(self):
|
|
cfg = {
|
|
"model_type": "nemotron_h",
|
|
"layers_block_type": ["mamba", "mlp", "attention", "mamba"],
|
|
}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is True
|
|
|
|
def test_nemotron_h_moe_only_returns_false(self):
|
|
"""A pure MoE NemotronH (no MLP) does not need the 5.10 tier."""
|
|
cfg = {
|
|
"model_type": "nemotron_h",
|
|
"hybrid_override_pattern": "MEME*MEM",
|
|
}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is False
|
|
|
|
def test_non_nemotron_with_dash_returns_false(self):
|
|
"""The dash heuristic only applies to nemotron_h configs."""
|
|
cfg = {"model_type": "llama", "hybrid_override_pattern": "M-M-"}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is False
|
|
|
|
def test_config_needs_510_includes_dense_nemotron_h(self):
|
|
cfg = {
|
|
"model_type": "nemotron_h",
|
|
"hybrid_override_pattern": "M-M-M*-",
|
|
}
|
|
assert _config_needs_510(cfg) is True
|
|
|
|
def test_nested_llm_config_with_dash(self):
|
|
# VL wrapper (e.g. NemotronH_Nano_VL_V2): dense LM is under llm_config.
|
|
cfg = {
|
|
"model_type": "NemotronH_Nano_VL_V2",
|
|
"llm_config": {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"},
|
|
}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is True
|
|
assert _config_needs_510(cfg) is True
|
|
|
|
def test_nested_text_config_with_mlp(self):
|
|
cfg = {
|
|
"model_type": "wrapper",
|
|
"text_config": {"model_type": "nemotron_h", "layers_block_type": ["mamba", "mlp"]},
|
|
}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is True
|
|
|
|
def test_nested_non_nemotron_returns_false(self):
|
|
cfg = {"model_type": "wrapper", "llm_config": {"model_type": "llama"}}
|
|
assert _nemotron_h_needs_mlp_support(cfg) is False
|
|
|
|
def test_non_dict_and_missing_nested_do_not_raise(self):
|
|
assert _nemotron_h_needs_mlp_support(None) is False
|
|
assert _nemotron_h_needs_mlp_support({"model_type": "wrapper", "llm_config": None}) is False
|
|
|
|
|
|
def _hf_response(cfg: dict):
|
|
"""A urlopen() context-manager stand-in returning *cfg* as JSON bytes."""
|
|
|
|
class _Resp:
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *a):
|
|
return False
|
|
|
|
def read(self):
|
|
return json.dumps(cfg).encode()
|
|
|
|
return _Resp()
|
|
|
|
|
|
class TestConfigJsonHfCacheFallback:
|
|
"""HF hub cache is consulted only offline or after a failed fetch (never stale online)."""
|
|
|
|
def setup_method(self):
|
|
_config_json_cache.clear()
|
|
|
|
@staticmethod
|
|
def _seed_cache(
|
|
hub: Path,
|
|
repo_id: str,
|
|
cfg: dict,
|
|
commit: str = "deadbeef",
|
|
):
|
|
repo = hub / ("models--" + repo_id.replace("/", "--"))
|
|
snap = repo / "snapshots" / commit
|
|
snap.mkdir(parents = True)
|
|
(snap / "config.json").write_text(json.dumps(cfg))
|
|
(repo / "refs").mkdir(parents = True)
|
|
(repo / "refs" / "main").write_text(commit)
|
|
|
|
def test_offline_reads_from_cache(self, tmp_path: Path, monkeypatch):
|
|
cfg = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "unsloth/NVIDIA-Nemotron-3-Nano-4B", cfg)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.setenv("HF_HUB_OFFLINE", "1")
|
|
with patch("urllib.request.urlopen") as mock_url:
|
|
assert _load_config_json("unsloth/NVIDIA-Nemotron-3-Nano-4B") == cfg
|
|
mock_url.assert_not_called()
|
|
|
|
def test_online_prefers_network_over_cache(self, tmp_path: Path, monkeypatch):
|
|
stale = {"model_type": "nemotron_h", "hybrid_override_pattern": "MMMM"}
|
|
fresh = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "org/model", stale)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
|
|
with patch("urllib.request.urlopen", return_value = _hf_response(fresh)):
|
|
assert _load_config_json("org/model") == fresh # network wins, not stale cache
|
|
|
|
def test_network_failure_falls_back_to_cache(self, tmp_path: Path, monkeypatch):
|
|
cfg = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "org/model", cfg)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
with patch("urllib.request.urlopen", side_effect = OSError("boom")):
|
|
assert _load_config_json("org/model") == cfg
|
|
|
|
def test_offline_uncached_returns_none(self, tmp_path: Path, monkeypatch):
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.setenv("HF_HUB_OFFLINE", "1")
|
|
with patch("urllib.request.urlopen") as mock_url:
|
|
assert _load_config_json("private/unknown") is None
|
|
mock_url.assert_not_called()
|
|
|
|
def test_helper_ignores_local_paths(self, tmp_path: Path):
|
|
# A filesystem path is not a repo id; never treat it as one.
|
|
assert _config_json_from_hf_cache(str(tmp_path)) is None
|
|
assert _config_json_from_hf_cache("plainname") is None
|
|
|
|
def test_no_refs_main_picks_newest_snapshot(self, tmp_path: Path, monkeypatch):
|
|
# No refs/main (commit-pinned downloads): lexicographic order would pick the older
|
|
# SHA; selection must follow mtime so the newest snapshot wins.
|
|
repo = tmp_path / "models--org--model"
|
|
old = repo / "snapshots" / "0000old"
|
|
new = repo / "snapshots" / "ffffnew"
|
|
old.mkdir(parents = True)
|
|
new.mkdir(parents = True)
|
|
(old / "config.json").write_text(json.dumps({"model_type": "stale"}))
|
|
(new / "config.json").write_text(json.dumps({"model_type": "fresh"}))
|
|
os.utime(old / "config.json", (1000, 1000))
|
|
os.utime(new / "config.json", (2000, 2000))
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
assert _config_json_from_hf_cache("org/model") == {"model_type": "fresh"}
|
|
|
|
def test_transient_failure_does_not_cache_fallback(self, tmp_path: Path, monkeypatch):
|
|
stale = {"model_type": "nemotron_h", "hybrid_override_pattern": "MMMM"}
|
|
fresh = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "org/model", stale)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
# Network fails -> serve the cached snapshot, but it must not be memoized.
|
|
with patch("urllib.request.urlopen", side_effect = OSError("boom")):
|
|
assert _load_config_json("org/model") == stale
|
|
# Connectivity returns: the next call must hit the network for the fresh config.
|
|
with patch("urllib.request.urlopen", return_value = _hf_response(fresh)):
|
|
assert _load_config_json("org/model") == fresh
|
|
|
|
def test_auth_failure_does_not_serve_cache(self, tmp_path: Path, monkeypatch):
|
|
import urllib.error
|
|
|
|
# config.json cached from an earlier authorized session; an unauthenticated 4xx
|
|
# must not be handed that private metadata.
|
|
cfg = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "private/model", cfg)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
for code in (401, 403, 404):
|
|
_config_json_cache.clear()
|
|
err = urllib.error.HTTPError("url", code, "denied", {}, None)
|
|
with patch("urllib.request.urlopen", side_effect = err):
|
|
assert _load_config_json("private/model") is None
|
|
|
|
def test_server_error_still_falls_back_to_cache(self, tmp_path: Path, monkeypatch):
|
|
import urllib.error
|
|
|
|
cfg = {"model_type": "nemotron_h", "hybrid_override_pattern": "M-M*-"}
|
|
self._seed_cache(tmp_path, "org/model", cfg)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
# A 5xx is transient, not an access decision: keep serving the cache.
|
|
err = urllib.error.HTTPError("url", 503, "busy", {}, None)
|
|
with patch("urllib.request.urlopen", side_effect = err):
|
|
assert _load_config_json("org/model") == cfg
|
|
|
|
|
|
class TestTierCheckTransientRetry:
|
|
"""tier-needs checks must not memoize a transient fetch fallback."""
|
|
|
|
def setup_method(self):
|
|
_config_json_cache.clear()
|
|
_config_needs_510_cache.clear()
|
|
_config_needs_550_cache.clear()
|
|
|
|
@staticmethod
|
|
def _seed_cache(
|
|
hub: Path,
|
|
repo_id: str,
|
|
cfg: dict,
|
|
commit: str = "deadbeef",
|
|
):
|
|
repo = hub / ("models--" + repo_id.replace("/", "--"))
|
|
snap = repo / "snapshots" / commit
|
|
snap.mkdir(parents = True)
|
|
(snap / "config.json").write_text(json.dumps(cfg))
|
|
(repo / "refs").mkdir(parents = True)
|
|
(repo / "refs" / "main").write_text(commit)
|
|
|
|
def test_transient_fallback_not_memoized_then_retries(self, tmp_path: Path, monkeypatch):
|
|
stale = {"model_type": "llama"} # does not need 510
|
|
fresh = {"architectures": ["Gemma4UnifiedForConditionalGeneration"]} # needs 510
|
|
self._seed_cache(tmp_path, "org/model", stale)
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
# Network blip -> serve the cache, but do NOT pin the tier result.
|
|
with patch("urllib.request.urlopen", side_effect = OSError("boom")):
|
|
assert _check_config_needs_510("org/model") is False
|
|
assert "org/model" not in _config_needs_510_cache
|
|
# Connectivity returns: the next call re-fetches and sees the higher tier.
|
|
with patch("urllib.request.urlopen", return_value = _hf_response(fresh)):
|
|
assert _check_config_needs_510("org/model") is True
|
|
assert _config_needs_510_cache["org/model"] is True # definitive read is memoized
|
|
|
|
def test_definitive_network_read_is_memoized(self, tmp_path: Path, monkeypatch):
|
|
fresh = {"architectures": ["Gemma4ForConditionalGeneration"]} # needs 550
|
|
monkeypatch.setenv("HF_HUB_CACHE", str(tmp_path))
|
|
monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
|
|
with patch("urllib.request.urlopen", return_value = _hf_response(fresh)) as mock_url:
|
|
assert _check_config_needs_550("org/model") is True
|
|
assert _check_config_needs_550("org/model") is True
|
|
assert mock_url.call_count == 1 # second call served from the tier cache
|
|
|
|
|
|
class TestHigherTier:
|
|
def test_picks_stronger_tier(self):
|
|
assert _higher_tier("default", "510") == "510"
|
|
assert _higher_tier("530", "550") == "550"
|
|
assert _higher_tier("510", "default") == "510"
|
|
assert _higher_tier("default", "default") == "default"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# get_transformers_tier — tier detection
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestGetTransformersTier:
|
|
"""Tests for get_transformers_tier() tiered version detection."""
|
|
|
|
def setup_method(self):
|
|
_tokenizer_class_cache.clear()
|
|
_config_json_cache.clear()
|
|
_config_needs_510_cache.clear()
|
|
_config_needs_550_cache.clear()
|
|
|
|
def test_gemma4_substring_returns_550(self):
|
|
assert get_transformers_tier("google/gemma-4-E2B-it") == "550"
|
|
|
|
def test_gemma4_12b_substring_returns_510(self):
|
|
assert get_transformers_tier("unsloth/gemma-4-12b-it") == "510"
|
|
|
|
def test_gemma4_assistant_substring_returns_510(self):
|
|
assert get_transformers_tier("google/gemma-4-E2B-it-assistant") == "510"
|
|
|
|
def test_gemma4_alt_substring_returns_550(self):
|
|
assert get_transformers_tier("unsloth/gemma4-E4B-it") == "550"
|
|
|
|
def test_gemma4_config_json_returns_550(self, tmp_path: Path):
|
|
"""Local checkpoint with Gemma4 architecture → 550."""
|
|
cfg = {
|
|
"architectures": ["Gemma4ForConditionalGeneration"],
|
|
"model_type": "gemma4",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert get_transformers_tier(str(tmp_path)) == "550"
|
|
|
|
def test_gemma4_unified_config_json_returns_510(self, tmp_path: Path):
|
|
"""Local checkpoint with Gemma4 Unified architecture → 510."""
|
|
cfg = {
|
|
"architectures": ["Gemma4UnifiedForConditionalGeneration"],
|
|
"model_type": "gemma4_unified",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert get_transformers_tier(str(tmp_path)) == "510"
|
|
|
|
def test_gemma4_assistant_config_json_returns_510(self, tmp_path: Path):
|
|
"""Local checkpoint with Gemma4 Assistant architecture → 510."""
|
|
cfg = {
|
|
"architectures": ["Gemma4AssistantForCausalLM"],
|
|
"model_type": "gemma4_assistant",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
|
|
assert get_transformers_tier(str(tmp_path)) == "510"
|
|
|
|
def test_dense_nemotron_h_config_json_returns_510(self, tmp_path: Path):
|
|
"""Local dense NemotronH checkpoint → 510 (MLP layers need >= 5.10)."""
|
|
cfg = {
|
|
"model_type": "nemotron_h",
|
|
"hybrid_override_pattern": "M-M-M*-M-",
|
|
}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
# A v5 tokenizer would otherwise route this to 530; 510 must win.
|
|
(tmp_path / "tokenizer_config.json").write_text(
|
|
json.dumps({"tokenizer_class": "TokenizersBackend"})
|
|
)
|
|
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
assert get_transformers_tier(str(tmp_path)) == "510"
|
|
mock_urlopen.assert_not_called()
|
|
|
|
def test_dense_nemotron_h_remote_config_returns_510(self):
|
|
"""Remote dense NemotronH (HF id) → 510 via config.json fetch, not 530."""
|
|
|
|
class _Response:
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc, tb):
|
|
return False
|
|
|
|
def read(self):
|
|
return json.dumps(
|
|
{
|
|
"model_type": "nemotron_h",
|
|
"hybrid_override_pattern": "M-M-M*-M-",
|
|
}
|
|
).encode()
|
|
|
|
with patch("urllib.request.urlopen", return_value = _Response()):
|
|
assert get_transformers_tier("unsloth/NVIDIA-Nemotron-3-Nano-4B") == "510"
|
|
|
|
def test_local_config_json_short_circuits_path_substrings(self, tmp_path: Path):
|
|
"""Local config.json should prevent false matches from parent directory names."""
|
|
model_dir = tmp_path / "gemma-4-12b-experiment" / "llama-checkpoint"
|
|
model_dir.mkdir(parents = True)
|
|
(model_dir / "config.json").write_text(
|
|
json.dumps(
|
|
{
|
|
"architectures": ["LlamaForCausalLM"],
|
|
"model_type": "llama",
|
|
}
|
|
)
|
|
)
|
|
(model_dir / "tokenizer_config.json").write_text(
|
|
json.dumps({"tokenizer_class": "LlamaTokenizerFast"})
|
|
)
|
|
|
|
with patch("urllib.request.urlopen") as mock_urlopen:
|
|
assert get_transformers_tier(str(model_dir)) == "default"
|
|
mock_urlopen.assert_not_called()
|
|
|
|
def test_remote_config_json_is_fetched_once_for_config_tiers(self):
|
|
"""510 and 550 slow-path checks should share one config.json fetch."""
|
|
|
|
class _Response:
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc, tb):
|
|
return False
|
|
|
|
def read(self):
|
|
return json.dumps(
|
|
{
|
|
"architectures": ["Gemma4ForConditionalGeneration"],
|
|
"model_type": "gemma4",
|
|
}
|
|
).encode()
|
|
|
|
with patch("urllib.request.urlopen", return_value = _Response()) as mock_urlopen:
|
|
assert get_transformers_tier("org/no-fast-substring-model") == "550"
|
|
|
|
assert mock_urlopen.call_count == 1
|
|
|
|
def test_qwen35_returns_530(self):
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert get_transformers_tier("Qwen/Qwen3.5-9B") == "530"
|
|
|
|
def test_ministral_returns_530(self):
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert get_transformers_tier("mistralai/Ministral-3-8B-Instruct-2512") == "530"
|
|
|
|
def test_llama_returns_default(self):
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_tokenizer_config_needs_v5",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert get_transformers_tier("meta-llama/Llama-3-8B") == "default"
|
|
|
|
def test_550_checked_before_530(self):
|
|
"""5.5.0 is checked before 5.3.0 - a model matching both gets 550."""
|
|
assert get_transformers_tier("gemma-4-model") == "550"
|
|
|
|
# ---- issue #6103: the tier decision must be traceable in the logs ----
|
|
|
|
def test_tier_550_selection_is_logged(self, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
assert get_transformers_tier("google/gemma-4-E2B-it") == "550"
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "550" in text, f"tier selection not logged: {text!r}"
|
|
assert "gemma-4-e2b-it" in text, f"tier log omits the model: {text!r}"
|
|
|
|
def test_tier_530_selection_is_logged(self, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
with patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
):
|
|
assert get_transformers_tier("Qwen/Qwen3.5-9B") == "530"
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "530" in text, f"tier selection not logged: {text!r}"
|
|
assert "qwen3.5-9b" in text, f"tier log omits the model: {text!r}"
|
|
|
|
def test_tier_default_selection_is_logged(self, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_tokenizer_config_needs_v5",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert get_transformers_tier("meta-llama/Llama-3-8B") == "default"
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "default" in text, f"tier selection not logged: {text!r}"
|
|
|
|
def test_local_config_json_selection_is_logged(self, tmp_path: Path, caplog):
|
|
cfg = {"architectures": ["Gemma4ForConditionalGeneration"], "model_type": "gemma4"}
|
|
(tmp_path / "config.json").write_text(json.dumps(cfg))
|
|
caplog.set_level(logging.INFO)
|
|
assert get_transformers_tier(str(tmp_path)) == "550"
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "550" in text and "local config.json" in text, f"local tier not logged: {text!r}"
|
|
|
|
def test_needs_transformers_5_compat(self):
|
|
"""needs_transformers_5 should return True for 510, 530, and 550 models."""
|
|
assert needs_transformers_5("unsloth/gemma-4-12b-it") is True
|
|
assert needs_transformers_5("google/gemma-4-E2B-it") is True
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert needs_transformers_5("Qwen/Qwen3.5-9B") is True
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_550",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_config_needs_510",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._check_tokenizer_config_needs_v5",
|
|
return_value = False,
|
|
),
|
|
):
|
|
assert needs_transformers_5("meta-llama/Llama-3-8B") is False
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# activate_transformers_for_subprocess — issue #6103
|
|
# The early log must make clear it only prepends to sys.path; the real
|
|
# confirmation comes later from "Subprocess loaded transformers X.X.X".
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestActivateLoggingClarity:
|
|
"""issue #6103: 'Activated transformers' was misleading (path-prepend only)."""
|
|
|
|
def _snapshot_env(self):
|
|
return list(sys.path), os.environ.get("PYTHONPATH")
|
|
|
|
def _restore_env(self, snapshot):
|
|
saved_path, saved_pp = snapshot
|
|
sys.path[:] = saved_path
|
|
if saved_pp is None:
|
|
os.environ.pop("PYTHONPATH", None)
|
|
else:
|
|
os.environ["PYTHONPATH"] = saved_pp
|
|
|
|
def test_activate_550_log_clarifies_path_prepend_only(self, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
snap = self._snapshot_env()
|
|
try:
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._resolve_base_model",
|
|
side_effect = lambda m: m,
|
|
),
|
|
patch(
|
|
"utils.transformers_version.get_transformers_tier",
|
|
return_value = "550",
|
|
),
|
|
patch(
|
|
"utils.transformers_version._ensure_venv_t5_550_exists",
|
|
return_value = True,
|
|
),
|
|
):
|
|
activate_transformers_for_subprocess("google/gemma-4-E2B-it")
|
|
finally:
|
|
self._restore_env(snap)
|
|
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "5.5.0" in text, f"version not logged: {text!r}"
|
|
# Must signal this is only a sys.path manipulation, not a confirmed import.
|
|
assert (
|
|
"sys.path" in text or "path only" in text
|
|
), f"early activation log does not clarify it is path-prepend only: {text!r}"
|
|
|
|
def test_activate_530_log_clarifies_path_prepend_only(self, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
snap = self._snapshot_env()
|
|
try:
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._resolve_base_model",
|
|
side_effect = lambda m: m,
|
|
),
|
|
patch(
|
|
"utils.transformers_version.get_transformers_tier",
|
|
return_value = "530",
|
|
),
|
|
patch(
|
|
"utils.transformers_version._ensure_venv_t5_530_exists",
|
|
return_value = True,
|
|
),
|
|
):
|
|
activate_transformers_for_subprocess("Qwen/Qwen3.5-9B")
|
|
finally:
|
|
self._restore_env(snap)
|
|
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "5.3.0" in text, f"version not logged: {text!r}"
|
|
assert (
|
|
"sys.path" in text or "path only" in text
|
|
), f"early activation log does not clarify it is path-prepend only: {text!r}"
|
|
|
|
def test_activate_prefers_local_checkpoint_tier_over_resolved_base(self, caplog, tmp_path):
|
|
# Base resolves to an offline/private id (default tier); the local config.json wins.
|
|
(tmp_path / "config.json").write_text(json.dumps({"model_type": "llama"}))
|
|
local = str(tmp_path)
|
|
caplog.set_level(logging.INFO)
|
|
snap = self._snapshot_env()
|
|
tiers = {local: "510", "private/base": "default"}
|
|
try:
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._resolve_base_model",
|
|
return_value = "private/base",
|
|
),
|
|
patch(
|
|
"utils.transformers_version.get_transformers_tier",
|
|
side_effect = lambda m: tiers[m],
|
|
),
|
|
patch(
|
|
"utils.transformers_version._ensure_venv_t5_510_exists",
|
|
return_value = True,
|
|
),
|
|
):
|
|
activate_transformers_for_subprocess(local)
|
|
finally:
|
|
self._restore_env(snap)
|
|
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "5.10.2" in text, f"local checkpoint tier did not win: {text!r}"
|
|
|
|
def test_activate_adapter_without_config_skips_path_name_recheck(self, caplog, tmp_path):
|
|
# Adapter dir named 'gemma-4' but no config.json: the path-name re-check must not run.
|
|
adapter = tmp_path / "gemma-4-experiment" / "llama-lora"
|
|
adapter.mkdir(parents = True)
|
|
local = str(adapter)
|
|
caplog.set_level(logging.INFO)
|
|
snap = self._snapshot_env()
|
|
seen = []
|
|
|
|
def fake_tier(m):
|
|
seen.append(m)
|
|
return "550" if "gemma-4" in m else "default"
|
|
|
|
try:
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._resolve_base_model",
|
|
return_value = "meta/llama",
|
|
),
|
|
patch(
|
|
"utils.transformers_version.get_transformers_tier",
|
|
side_effect = fake_tier,
|
|
),
|
|
):
|
|
activate_transformers_for_subprocess(local)
|
|
finally:
|
|
self._restore_env(snap)
|
|
|
|
assert seen == ["meta/llama"], f"adapter path was re-checked via substrings: {seen!r}"
|
|
text = " ".join(r.getMessage() for r in caplog.records).lower()
|
|
assert "default transformers" in text, f"adapter wrongly upgraded: {text!r}"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _venv_dir_is_valid — issue #6103
|
|
# A version mismatch triggers a full wipe + reinstall, so it must be logged
|
|
# at WARNING (not INFO) so the reinstall is visible.
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestVenvDirIsValidLogging:
|
|
def _make_venv(self, venv_dir: Path, pkg: str, version: str):
|
|
"""Create a fake target-dir install of *pkg* at *version*."""
|
|
(venv_dir / pkg).mkdir(parents = True)
|
|
di = venv_dir / f"{pkg}-{version}.dist-info"
|
|
di.mkdir()
|
|
(di / "METADATA").write_text(f"Name: {pkg}\nVersion: {version}\n")
|
|
|
|
def test_version_mismatch_logged_at_warning(self, tmp_path: Path, caplog):
|
|
venv_dir = tmp_path / "venv"
|
|
self._make_venv(venv_dir, "transformers", "5.0.0") # wrong version
|
|
|
|
caplog.set_level(logging.INFO)
|
|
result = _venv_dir_is_valid(str(venv_dir), ("transformers==5.3.0",))
|
|
|
|
assert result is False
|
|
warnings = [r for r in caplog.records if r.levelno >= logging.WARNING]
|
|
assert warnings, (
|
|
"version mismatch must be logged at WARNING; got: "
|
|
f"{[(r.levelname, r.getMessage()) for r in caplog.records]!r}"
|
|
)
|
|
joined = " ".join(r.getMessage() for r in warnings)
|
|
assert (
|
|
"5.0.0" in joined and "5.3.0" in joined
|
|
), f"mismatch log omits the versions: {joined!r}"
|
|
|
|
def test_correct_version_does_not_warn(self, tmp_path: Path, caplog):
|
|
venv_dir = tmp_path / "venv"
|
|
self._make_venv(venv_dir, "transformers", "5.3.0") # correct version
|
|
|
|
caplog.set_level(logging.INFO)
|
|
result = _venv_dir_is_valid(str(venv_dir), ("transformers==5.3.0",))
|
|
|
|
assert result is True
|
|
assert not [
|
|
r for r in caplog.records if r.levelno >= logging.WARNING
|
|
], "no warning expected when the installed version matches"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _ensure_venv_dir — issue #6103
|
|
# A slow runtime install must log each package as it starts, otherwise it
|
|
# looks like a hang.
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestEnsureVenvDirProgressLogging:
|
|
def test_logs_each_package_with_progress(self, tmp_path: Path, caplog):
|
|
installed = []
|
|
caplog.set_level(logging.INFO)
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._venv_dir_is_valid",
|
|
return_value = False,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._install_to_dir",
|
|
side_effect = lambda pkg, d: (installed.append(pkg), True)[1],
|
|
),
|
|
):
|
|
ok = _ensure_venv_dir(
|
|
str(tmp_path / "venv"),
|
|
("transformers==5.3.0", "tokenizers==0.21.0"),
|
|
"transformers 5.3.0",
|
|
)
|
|
|
|
assert ok is True
|
|
assert installed == ["transformers==5.3.0", "tokenizers==0.21.0"]
|
|
|
|
msgs = " ".join(r.getMessage() for r in caplog.records)
|
|
assert "transformers==5.3.0" in msgs, f"first package not logged: {msgs!r}"
|
|
assert "tokenizers==0.21.0" in msgs, f"second package not logged: {msgs!r}"
|
|
# progress counter present so a slow install is not mistaken for a hang
|
|
assert "1/2" in msgs and "2/2" in msgs, f"progress count missing: {msgs!r}"
|
|
|
|
def test_no_install_logging_when_venv_already_valid(self, tmp_path: Path, caplog):
|
|
caplog.set_level(logging.INFO)
|
|
with (
|
|
patch(
|
|
"utils.transformers_version._venv_dir_is_valid",
|
|
return_value = True,
|
|
),
|
|
patch(
|
|
"utils.transformers_version._install_to_dir",
|
|
) as mock_install,
|
|
):
|
|
ok = _ensure_venv_dir(
|
|
str(tmp_path / "venv"),
|
|
("transformers==5.3.0",),
|
|
"transformers 5.3.0",
|
|
)
|
|
|
|
assert ok is True
|
|
mock_install.assert_not_called()
|
|
assert "Installing" not in " ".join(r.getMessage() for r in caplog.records)
|