diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d8e5b9c..abfd88b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -24,8 +24,14 @@ jobs: python-version: "3.12" - name: Install uv run: pip install uv + - name: Cache uv + uses: actions/cache@v4 + with: + path: ~/.cache/uv + key: uv-${{ runner.os }}-${{ hashFiles('uv.lock') }} + restore-keys: uv-${{ runner.os }}- - name: Install dependencies - run: uv sync --extra dev --extra cli + run: uv sync --extra dev --extra cli --extra torch --extra onnx --extra safetensors --extra frameworks - name: Run pytest env: SQLITE_DB_PATH: ":memory:" diff --git a/app/cli/core/inspector.py b/app/cli/core/inspector.py index ff0d2f6..b2a15e5 100644 --- a/app/cli/core/inspector.py +++ b/app/cli/core/inspector.py @@ -6,60 +6,100 @@ import sys from dataclasses import dataclass, field -# Double-brace every dict/format literal inside the script so .format() ignores them. -# Only {path} is a real substitution placeholder. -_INSPECT_SCRIPT = """\ -import os, json, sys, traceback as _tb +# --------------------------------------------------------------------------- +# Subprocess inspection script +# --------------------------------------------------------------------------- +# Each extractor runs in an isolated subprocess so that importing heavy +# frameworks (torch, onnx, safetensors) doesn't pollute the CLI process. +# The script ALWAYS exits 0 and ALWAYS prints valid JSON — errors are +# captured in raw_facts["errors"] rather than raising. +# --------------------------------------------------------------------------- + +_INSPECT_SCRIPT = r""" +from __future__ import annotations +import json, os, sys, struct path = {path!r} -ext = os.path.splitext(path)[1].lower() -# ── ONNX: inspect without loading into Python ────────────────────────────── -if ext == ".onnx": +# ── Layer 0: filesystem facts (always succeeds) ──────────────────────────── +raw = dict( + artifact_path=path, + artifact_size_mb=0.0, + extension="", + is_directory=False, + errors=[], +) +try: + raw["artifact_size_mb"] = round(os.path.getsize(path) / (1024 ** 2), 2) + raw["extension"] = os.path.splitext(path)[1].lower() + raw["is_directory"] = os.path.isdir(path) +except Exception as e: + raw["errors"].append({{"layer": "filesystem", "error": str(e)}}) + +# ── Layer 1: format detection ────────────────────────────────────────────── +def _detect_format(p: str, ext: str) -> str: + if os.path.isdir(p): + return "directory" + _EXT_MAP = {{ + ".pkl": "pickle", ".pickle": "pickle", + ".joblib": "joblib", + ".pt": "pytorch", ".pth": "pytorch", + ".onnx": "onnx", + ".safetensors": "safetensors", + }} + if ext in _EXT_MAP: + return _EXT_MAP[ext] + # magic-byte fallback try: - import onnx - model = onnx.load(path) - inputs = [ - {{"name": i.name, "shape": list(i.type.tensor_type.shape.dim[j].dim_value for j in range(len(i.type.tensor_type.shape.dim)))}} - for i in model.graph.input - ] - outputs = [ - {{"name": o.name, "shape": list(o.type.tensor_type.shape.dim[j].dim_value for j in range(len(o.type.tensor_type.shape.dim)))}} - for o in model.graph.output - ] - extra = {{"onnx_inputs": inputs, "onnx_outputs": outputs}} + with open(p, "rb") as f: + magic = f.read(8) + if magic[:2] == b"\x80\x04" or magic[:2] == b"\x80\x05": + return "pickle" + if magic[:4] == b"PK\x03\x04": # zip → likely torch + return "pytorch" + if magic[:4] == b"\x08\x00\x00\x00": # protobuf → onnx + return "onnx" + if magic[:8] == b"<<< BEG": # safetensors header marker + return "safetensors" except Exception: - extra = {{}} - result = dict( - framework="onnx", - class_name="ONNXModel", - class_hierarchy=[], - input_hint="numpy array matching ONNX input shape", - output_hint="numpy array matching ONNX output shape", - feature_count=None, - class_labels=None, - artifact_path=path, - artifact_size_mb=round(os.path.getsize(path) / (1024 * 1024), 2), - extra=extra, - ) - print(json.dumps(result)) - sys.exit(0) + pass + return "unknown" -# ── pickle-based artifacts ────────────────────────────────────────── try: - import pickle + fmt = _detect_format(path, raw["extension"]) + raw["format"] = fmt +except Exception as e: + raw["format"] = "unknown" + raw["errors"].append({{"layer": "format", "error": str(e)}}) + fmt = "unknown" - with open(path, "rb") as f: - obj = pickle.load(f) +# ── Layer 2: safe structural read (format-specific) ─────────────────────── - module = type(obj).__module__ or "" - class_name = type(obj).__name__ +def _extract_pickle(p: str, raw: dict) -> None: + try: + import joblib + obj = joblib.load(p) + raw["load_via"] = "joblib" + except Exception: + import pickle + with open(p, "rb") as f: + obj = pickle.load(f) + raw["load_via"] = "pickle" + + raw["class_name"] = type(obj).__name__ + raw["module"] = type(obj).__module__ or "" + try: + raw["attributes"] = list(vars(obj).keys()) + except Exception: + raw["attributes"] = [] + raw["has_predict"] = hasattr(obj, "predict") + raw["has_predict_proba"] = hasattr(obj, "predict_proba") + raw["has_steps"] = hasattr(obj, "steps") - # ── framework detection (order matters) ────────────────────────────────── + # ── framework detection ──────────────────────────────────────────────── framework = "generic" - extra = {{}} + module = raw["module"] - # sentence-transformers (check before transformers — it wraps HF models) try: from sentence_transformers import SentenceTransformer if isinstance(obj, SentenceTransformer): @@ -67,7 +107,6 @@ except Exception: pass - # transformers PreTrainedModel if framework == "generic": try: from transformers import PreTrainedModel @@ -76,7 +115,6 @@ except Exception: pass - # PyTorch nn.Module if framework == "generic": try: import torch @@ -85,7 +123,6 @@ except Exception: pass - # XGBoost if framework == "generic": try: import xgboost as xgb @@ -94,7 +131,6 @@ except Exception: pass - # LightGBM if framework == "generic": try: import lightgbm as lgb @@ -103,7 +139,6 @@ except Exception: pass - # CatBoost if framework == "generic": try: from catboost import CatBoost @@ -112,159 +147,281 @@ except Exception: pass - # sklearn (last — many frameworks inherit from sklearn base) if framework == "generic" and "sklearn" in module: framework = "sklearn" - # ── metadata extraction ───────────────────────────────────────────────── - class_hierarchy = [] - input_hint = "unknown" - output_hint = "unknown" - feature_count = None - class_labels = None - - if framework == "sklearn": - if hasattr(obj, "steps"): - class_hierarchy = [type(s).__name__ for _, s in obj.steps] - else: - class_hierarchy = [class_name] - - if hasattr(obj, "n_features_in_"): - feature_count = int(obj.n_features_in_) - - if hasattr(obj, "classes_"): - class_labels = obj.classes_.tolist() - elif hasattr(obj, "steps"): - for _, step in obj.steps: - if hasattr(step, "classes_"): - class_labels = step.classes_.tolist() - break - - text_vectorizers = ("TfidfVectorizer", "CountVectorizer", "HashingVectorizer") - if class_hierarchy and class_hierarchy[0] in text_vectorizers: - input_hint = "raw text string" - elif feature_count: - input_hint = "array-like of shape (n, " + str(feature_count) + ")" - else: - input_hint = "array-like" + raw["framework"] = framework - if class_labels is not None: - output_hint = "integer class label (classes: " + str(class_labels) + ")" - else: - output_hint = "float or array" - - elif framework == "pytorch": - try: + # ── Layer 3: deep attribute scan ────────────────────────────────────── + try: + if framework == "sklearn": + if hasattr(obj, "steps"): + raw["pipeline_steps"] = [type(s).__name__ for _, s in obj.steps] + if hasattr(obj, "n_features_in_"): + raw["n_features_in"] = int(obj.n_features_in_) + if hasattr(obj, "classes_"): + raw["classes"] = obj.classes_.tolist() + elif hasattr(obj, "steps"): + for _, step in obj.steps: + if hasattr(step, "classes_"): + raw["classes"] = step.classes_.tolist() + break + + elif framework == "pytorch": import torch layer_count = sum(1 for _ in obj.modules()) - extra["layer_count"] = layer_count + raw["layer_count"] = layer_count children = list(obj.named_children()) if children: - extra["first_layer"] = type(children[0][1]).__name__ - extra["last_layer"] = type(children[-1][1]).__name__ - except Exception: - pass - input_hint = "torch.Tensor" - output_hint = "torch.Tensor" + raw["first_layer"] = type(children[0][1]).__name__ + raw["last_layer"] = type(children[-1][1]).__name__ - elif framework == "transformers": - try: + elif framework == "transformers": cfg = obj.config - extra["model_type"] = getattr(cfg, "model_type", "unknown") - extra["hidden_size"] = getattr(cfg, "hidden_size", None) - extra["num_labels"] = getattr(cfg, "num_labels", None) - extra["tokenizer_class"] = getattr(cfg, "tokenizer_class", None) - except Exception: - pass - input_hint = "dict with input_ids, attention_mask (tokenized)" - output_hint = "ModelOutput (logits or last_hidden_state)" + raw["model_type"] = getattr(cfg, "model_type", None) + raw["hidden_size"] = getattr(cfg, "hidden_size", None) + raw["num_labels"] = getattr(cfg, "num_labels", None) + raw["tokenizer_class"] = getattr(cfg, "tokenizer_class", None) - elif framework == "xgboost": - try: + elif framework == "xgboost": import xgboost as xgb if isinstance(obj, xgb.XGBModel): - extra["n_estimators"] = getattr(obj, "n_estimators", None) - extra["objective"] = getattr(obj, "objective", None) + raw["n_estimators"] = getattr(obj, "n_estimators", None) + raw["objective"] = getattr(obj, "objective", None) if hasattr(obj, "n_features_in_"): - feature_count = int(obj.n_features_in_) + raw["n_features_in"] = int(obj.n_features_in_) elif isinstance(obj, xgb.Booster): - extra["num_trees"] = obj.num_trees() - except Exception: - pass - input_hint = "numpy array or pandas DataFrame" - output_hint = "numpy array of predictions" + raw["num_trees"] = obj.num_trees() - elif framework == "lightgbm": - try: + elif framework == "lightgbm": import lightgbm as lgb if isinstance(obj, lgb.LGBMModel): - extra["n_estimators"] = getattr(obj, "n_estimators", None) - extra["objective"] = getattr(obj, "objective", None) + raw["n_estimators"] = getattr(obj, "n_estimators", None) + raw["objective"] = getattr(obj, "objective", None) if hasattr(obj, "n_features_in_"): - feature_count = int(obj.n_features_in_) + raw["n_features_in"] = int(obj.n_features_in_) elif isinstance(obj, lgb.Booster): - extra["num_trees"] = obj.num_trees() - except Exception: - pass - input_hint = "numpy array or pandas DataFrame" - output_hint = "numpy array of predictions" + raw["num_trees"] = obj.num_trees() - elif framework == "catboost": - try: + elif framework == "catboost": from catboost import CatBoost - extra["loss_function"] = obj.get_param("loss_function") + raw["loss_function"] = obj.get_param("loss_function") fc = obj.get_param("feature_count") or obj.get_param("num_features") if fc is not None: - feature_count = int(fc) - except Exception: - pass - input_hint = "numpy array or pandas DataFrame" - output_hint = "numpy array of predictions" + raw["n_features_in"] = int(fc) - elif framework == "sentence_transformers": - try: - extra["model_name"] = getattr(obj, "_model_card_text", None) or class_name - modules = list(obj.modules()) - for m in modules: + elif framework == "sentence_transformers": + raw["model_name"] = getattr(obj, "_model_card_text", None) or type(obj).__name__ + for m in obj.modules(): if hasattr(m, "word_embedding_dimension"): - extra["embedding_dim"] = m.word_embedding_dimension + raw["embedding_dim"] = m.word_embedding_dimension break - except Exception: - pass - input_hint = "string or list of strings" - output_hint = "numpy array of shape (n, embedding_dim)" - - artifact_size_mb = round(os.path.getsize(path) / (1024 * 1024), 2) - print(json.dumps(dict( - framework=framework, - class_name=class_name, - class_hierarchy=class_hierarchy, - input_hint=input_hint, - output_hint=output_hint, - feature_count=feature_count, - class_labels=class_labels, - artifact_path=path, - artifact_size_mb=artifact_size_mb, - extra=extra, - ))) -except Exception as _exc: - _size = 0.0 + + except Exception as e: + raw["errors"].append({{"layer": "deep", "error": str(e)}}) + + +def _extract_pytorch(p: str, raw: dict) -> None: + import torch + # weights_only=True prevents arbitrary code execution + data = torch.load(p, map_location="cpu", weights_only=True) + raw["framework"] = "pytorch" + if isinstance(data, dict): + raw["load_format"] = "state_dict" + keys = list(data.keys()) + raw["state_dict_keys"] = keys[:30] + raw["param_count"] = sum( + v.numel() for v in data.values() if hasattr(v, "numel") + ) + else: + raw["load_format"] = "full_model" + raw["class_name"] = type(data).__name__ + raw["layer_names"] = [name for name, _ in data.named_children()] + + +def _extract_onnx(p: str, raw: dict) -> None: + import onnx + model = onnx.load(p) + raw["framework"] = "onnx" + raw["opset"] = model.opset_import[0].version if model.opset_import else None + raw["op_types"] = list({{n.op_type for n in model.graph.node}}) + + def _shape(tensor_type): + if not tensor_type.HasField("shape"): + return [] + return [ + d.dim_param if d.dim_param else d.dim_value + for d in tensor_type.shape.dim + ] + + raw["inputs"] = [ + {{"name": i.name, "shape": _shape(i.type.tensor_type), "dtype": i.type.tensor_type.elem_type}} + for i in model.graph.input + ] + raw["outputs"] = [ + {{"name": o.name, "shape": _shape(o.type.tensor_type), "dtype": o.type.tensor_type.elem_type}} + for o in model.graph.output + ] + + +def _extract_safetensors(p: str, raw: dict) -> None: + import safetensors + raw["framework"] = "safetensors" + with safetensors.safe_open(p, framework="pt") as f: + keys = list(f.keys()) + raw["tensor_keys"] = keys[:30] + raw["metadata"] = f.metadata() or {{}} + raw["tensor_shapes"] = {{k: list(f.get_slice(k).get_shape()) for k in keys[:10]}} + if raw["metadata"].get("format") == "pt": + raw["framework"] = "pytorch" + + +def _extract_directory(p: str, raw: dict) -> None: + files = os.listdir(p) + raw["directory_files"] = files + raw["framework"] = "unknown" + + cfg_path = os.path.join(p, "config.json") + if os.path.exists(cfg_path): + with open(cfg_path) as f: + cfg = json.load(f) + raw["hf_config"] = {{ + "model_type": cfg.get("model_type"), + "architectures": cfg.get("architectures"), + "hidden_size": cfg.get("hidden_size"), + "num_labels": cfg.get("num_labels"), + "num_hidden_layers": cfg.get("num_hidden_layers"), + }} + raw["framework"] = "transformers" + + tok_path = os.path.join(p, "tokenizer_config.json") + if os.path.exists(tok_path): + with open(tok_path) as f: + raw["tokenizer_class"] = json.load(f).get("tokenizer_class") + + if "saved_model.pb" in files: + raw["format"] = "tf_savedmodel" + raw["framework"] = "tensorflow" + + if "adapter_config.json" in files: + raw["is_peft_adapter"] = True + + +_EXTRACTORS = {{ + "pickle": _extract_pickle, + "joblib": _extract_pickle, + "pytorch": _extract_pytorch, + "onnx": _extract_onnx, + "safetensors": _extract_safetensors, + "directory": _extract_directory, +}} + +extractor = _EXTRACTORS.get(fmt) +if extractor is not None: try: - _size = round(os.path.getsize(path) / (1024 * 1024), 2) - except Exception: - pass - print(json.dumps(dict( - framework="unknown", - class_name="unknown", - class_hierarchy=[], - input_hint="unknown", - output_hint="unknown", - feature_count=None, - class_labels=None, - artifact_path=path, - artifact_size_mb=_size, - extra={{"inspection_warning": str(_exc), "traceback": _tb.format_exc()}}, - ))) + extractor(path, raw) + except Exception as e: + raw["errors"].append({{"layer": "extraction", "error": str(e)}}) + if "framework" not in raw: + raw["framework"] = "unknown" +else: + # GenericExtractor: try joblib then pickle + try: + _extract_pickle(path, raw) + except Exception as e: + raw["errors"].append({{"layer": "extraction", "error": str(e)}}) + raw["framework"] = "unknown" + +# ── Confidence ───────────────────────────────────────────────────────────── +def _confidence(r: dict) -> str: + if len(r.get("errors", [])) > 1: + return "low" + if r.get("framework") in (None, "unknown"): + return "low" + skip = {{"errors", "artifact_path", "is_directory"}} + filled = sum(1 for k, v in r.items() if k not in skip and v not in (None, "unknown", [])) + total = len(r) - len(skip) + ratio = filled / total if total else 0 + if r.get("errors"): + return "medium" + return "high" if ratio > 0.5 else "medium" + +raw["confidence"] = _confidence(raw) + +# ── Build legacy-compatible ArtifactMetadata fields ─────────────────────── +framework = raw.get("framework", "unknown") +class_name = raw.get("class_name", "unknown") +class_hierarchy = raw.get("pipeline_steps", [raw.get("class_name")] if raw.get("class_name") else []) +feature_count = raw.get("n_features_in") +class_labels = raw.get("classes") + +# input/output hints +input_hint = "unknown" +output_hint = "unknown" +if framework == "sklearn": + text_vecs = ("TfidfVectorizer", "CountVectorizer", "HashingVectorizer") + if class_hierarchy and class_hierarchy[0] in text_vecs: + input_hint = "raw text string" + elif feature_count: + input_hint = "array-like of shape (n, " + str(feature_count) + ")" + else: + input_hint = "array-like" + if class_labels is not None: + output_hint = "integer class label (classes: " + str(class_labels) + ")" + else: + output_hint = "float or array" +elif framework == "pytorch": + input_hint = "torch.Tensor" + output_hint = "torch.Tensor" +elif framework == "transformers": + input_hint = "dict with input_ids, attention_mask (tokenized)" + output_hint = "ModelOutput (logits or last_hidden_state)" +elif framework in ("xgboost", "lightgbm", "catboost"): + input_hint = "numpy array or pandas DataFrame" + output_hint = "numpy array of predictions" +elif framework == "sentence_transformers": + input_hint = "string or list of strings" + output_hint = "numpy array of shape (n, embedding_dim)" +elif framework == "onnx": + input_hint = "numpy array matching ONNX input shape" + output_hint = "numpy array matching ONNX output shape" + +# extra dict (legacy field — keep for backward compat with existing tests/prompts) +extra = {{}} +for k in ("layer_count", "first_layer", "last_layer", + "model_type", "hidden_size", "num_labels", "tokenizer_class", + "n_estimators", "objective", "num_trees", "loss_function", + "embedding_dim", "model_name", + "state_dict_keys", "param_count", "layer_names", "load_format", + "op_types", "opset", "tensor_keys", "metadata", "tensor_shapes", + "hf_config", "directory_files", "is_peft_adapter"): + if k in raw: + extra[k] = raw[k] + +# onnx inputs/outputs go into extra under legacy keys +if "inputs" in raw: + extra["onnx_inputs"] = raw["inputs"] +if "outputs" in raw: + extra["onnx_outputs"] = raw["outputs"] + +if raw.get("errors"): + extra["inspection_warning"] = "; ".join(e["error"] for e in raw["errors"]) + +print(json.dumps(dict( + framework=framework, + class_name=class_name, + class_hierarchy=class_hierarchy, + input_hint=input_hint, + output_hint=output_hint, + feature_count=feature_count, + class_labels=class_labels, + artifact_path=path, + artifact_size_mb=raw["artifact_size_mb"], + extra=extra, + raw_facts=raw, + confidence=raw["confidence"], + inspection_errors=raw.get("errors", []), +))) """ @@ -280,6 +437,10 @@ class ArtifactMetadata: artifact_path: str artifact_size_mb: float extra: dict = field(default_factory=dict) + # new fields (issue #15) + raw_facts: dict = field(default_factory=dict) + confidence: str = "low" + inspection_errors: list = field(default_factory=list) def inspect_artifact(path: str) -> ArtifactMetadata: @@ -293,7 +454,7 @@ def inspect_artifact(path: str) -> ArtifactMetadata: [sys.executable, "-c", script], capture_output=True, text=True, - timeout=30, + timeout=60, ) if result.returncode != 0: raise ValueError(f"Inspection failed:\n{result.stderr.strip()}") diff --git a/docs/cli/deploy.md b/docs/cli/deploy.md index 63dc94a..6845a3e 100644 --- a/docs/cli/deploy.md +++ b/docs/cli/deploy.md @@ -11,8 +11,7 @@ inference-engine deploy [options] ![CLI deploy flowchart](../assets/cli-deploy-light.png#only-light) ![CLI deploy flowchart](../assets/cli-deploy-dark.png#only-dark) -1. Shows a pickle safety warning and asks for confirmation -2. Inspects the artifact in an isolated subprocess — detects framework, pipeline steps, input/output hints +1. Inspects the artifact in an isolated subprocess — detects format from extension and magic bytes, routes to a format-specific extractor, returns structured metadata 3. Prompts for name, version, device, routing strategy, and sample input 4. Auto-increments version by scanning `models//` for existing versions 5. Shows a preview of files to be written @@ -95,22 +94,24 @@ The scaffold is valid Python that imports correctly but raises `NotImplementedEr | `canary` | 10% to new version, 90% to primary — edit `routing.py` to adjust | | `ab` | 100% weight on new version via A/B dict — edit `routing.py` to adjust | -## Supported frameworks +## Supported formats and frameworks -| Framework | Support | Metadata extracted | -|---|---|---| -| sklearn | Full | pipeline steps, feature count, class labels | -| PyTorch (`nn.Module`) | Full | layer count, first/last layer names | -| Transformers (`PreTrainedModel`) | Full | model type, num_labels, tokenizer class | -| XGBoost | Full | n_estimators, objective | -| LightGBM | Full | n_estimators, objective | -| CatBoost | Full | feature count, loss function | -| ONNX (`.onnx` file) | Full | input/output names and shapes | -| sentence-transformers | Full | embedding dimension | -| Generic | Fallback | class name only, LLM fills the gaps | - -All framework detections use lazy imports — none of these are required dependencies. -The inspector runs in an isolated subprocess, so missing frameworks degrade gracefully. +Format is detected from file extension and magic bytes before any loading attempt. + +| Format / Extension | Extractor | Load strategy | Metadata extracted | +|---|---|---|---| +| `.pkl`, `.pickle` | PickleExtractor | `joblib.load` → `pickle.load` | class name, pipeline steps, feature count, class labels | +| `.joblib` | PickleExtractor | `joblib.load` | same as pickle | +| `.pt`, `.pth` | TorchExtractor | `torch.load(..., weights_only=True)` | state dict keys (up to 30), param count, or layer names | +| `.onnx` | OnnxExtractor | `onnx.load` | opset, op types, inputs/outputs with dynamic axes | +| `.safetensors` | SafetensorsExtractor | header-only read | tensor keys, shapes, metadata | +| directory | DirectoryExtractor | JSON reads only | `config.json` fields, tokenizer class, PEFT adapter flag | +| unknown | GenericExtractor | `joblib.load` → `pickle.load` | class name, errors recorded | + +Framework detection (sklearn, PyTorch, Transformers, XGBoost, LightGBM, CatBoost, sentence-transformers) runs as a second pass for pickle-loaded objects. + +All framework libraries use lazy imports — none are required dependencies. +The inspector runs in an isolated subprocess and always exits 0; extraction errors are recorded in metadata rather than crashing the deploy pipeline. ## Note on server reload diff --git a/pyproject.toml b/pyproject.toml index 56131f8..bcd240e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,22 @@ cli = [ "rich>=13.0.0", "groq>=0.9.0", ] +torch = [ + "torch>=2.0.0", +] +onnx = [ + "onnx>=1.14.0", + "onnxruntime>=1.16.0", +] +safetensors = [ + "safetensors>=0.4.0", +] +frameworks = [ + "scikit-learn>=1.4.0", + "xgboost>=2.0.0", + "lightgbm>=4.0.0", + "joblib>=1.3.0", +] [project.scripts] inference-engine = "app.cli.__main__:main" @@ -52,4 +68,4 @@ packages = ["app"] [build-system] requires = ["hatchling"] -build-backend = "hatchling.build" +build-backend = "hatchling.build" \ No newline at end of file diff --git a/tests/test_cli_issue15_inspector.py b/tests/test_cli_issue15_inspector.py new file mode 100644 index 0000000..c2023e1 --- /dev/null +++ b/tests/test_cli_issue15_inspector.py @@ -0,0 +1,206 @@ +"""Issue #15 — format-routing inspector tests.""" +from __future__ import annotations + +import json +import warnings +from pathlib import Path + +import pytest + +FIXTURE = Path(__file__).parent / "fixtures" / "sentiment.pkl" + + +def _inspect(path): + from app.cli.core.inspector import inspect_artifact + return inspect_artifact(str(path)) + + +def _inspect_warn(path): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + meta = _inspect(path) + return meta, w + + +# --------------------------------------------------------------------------- +# New metadata fields +# --------------------------------------------------------------------------- + +class TestNewMetadataFields: + def test_raw_facts_present(self): + meta = _inspect(FIXTURE) + assert isinstance(meta.raw_facts, dict) + assert "artifact_path" in meta.raw_facts + + def test_confidence_present(self): + meta = _inspect(FIXTURE) + assert meta.confidence in ("high", "medium", "low") + + def test_inspection_errors_present(self): + meta = _inspect(FIXTURE) + assert isinstance(meta.inspection_errors, list) + + def test_sklearn_confidence_not_low(self): + meta = _inspect(FIXTURE) + assert meta.confidence in ("high", "medium") + + +# --------------------------------------------------------------------------- +# PyTorch .pt extractor +# --------------------------------------------------------------------------- + +class TestTorchExtractor: + def _make_state_dict(self, tmp_path) -> Path: + torch = pytest.importorskip("torch") + sd = {"layer.weight": torch.zeros(4, 4), "layer.bias": torch.zeros(4)} + p = tmp_path / "model.pt" + torch.save(sd, str(p)) + return p + + def test_state_dict_detected(self, tmp_path): + meta = _inspect(self._make_state_dict(tmp_path)) + assert meta.framework == "pytorch" + assert meta.extra["load_format"] == "state_dict" + + def test_state_dict_keys_extracted(self, tmp_path): + meta = _inspect(self._make_state_dict(tmp_path)) + assert "layer.weight" in meta.extra["state_dict_keys"] + + def test_param_count_extracted(self, tmp_path): + meta = _inspect(self._make_state_dict(tmp_path)) + assert meta.extra["param_count"] == 20 # 4*4 + 4 + + def test_input_output_hints(self, tmp_path): + meta = _inspect(self._make_state_dict(tmp_path)) + assert meta.input_hint == "torch.Tensor" + assert meta.output_hint == "torch.Tensor" + + def test_pth_extension(self, tmp_path): + torch = pytest.importorskip("torch") + p = tmp_path / "model.pth" + torch.save({"w": torch.zeros(2)}, str(p)) + meta = _inspect(p) + assert meta.framework == "pytorch" + + +# --------------------------------------------------------------------------- +# ONNX extractor +# --------------------------------------------------------------------------- + +class TestOnnxExtractor: + def _make_onnx(self, tmp_path) -> Path: + onnx = pytest.importorskip("onnx") + from onnx import helper, TensorProto + X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, 4]) + Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, 2]) + node = helper.make_node("Relu", ["X"], ["Y"]) + graph = helper.make_graph([node], "test", [X], [Y]) + model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)]) + p = tmp_path / "model.onnx" + onnx.save(model, str(p)) + return p + + def test_framework_detected(self, tmp_path): + assert _inspect(self._make_onnx(tmp_path)).framework == "onnx" + + def test_opset_extracted(self, tmp_path): + assert _inspect(self._make_onnx(tmp_path)).extra["opset"] == 17 + + def test_op_types_extracted(self, tmp_path): + assert "Relu" in _inspect(self._make_onnx(tmp_path)).extra["op_types"] + + def test_inputs_extracted(self, tmp_path): + inputs = _inspect(self._make_onnx(tmp_path)).extra["onnx_inputs"] + assert inputs[0]["name"] == "X" + assert len(inputs[0]["shape"]) == 2 + + def test_dynamic_axis_preserved(self, tmp_path): + """Named dim_param (e.g. 'batch_size') is kept as a string, not 0.""" + onnx = pytest.importorskip("onnx") + from onnx import helper, TensorProto + # Use a named dynamic axis so dim_param is set + X = helper.make_tensor_value_info("X", TensorProto.FLOAT, ["batch_size", 4]) + Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ["batch_size", 2]) + node = helper.make_node("Relu", ["X"], ["Y"]) + graph = helper.make_graph([node], "test", [X], [Y]) + model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)]) + p = tmp_path / "named_dynamic.onnx" + onnx.save(model, str(p)) + shape = _inspect(p).extra["onnx_inputs"][0]["shape"] + assert shape[0] == "batch_size" + + +# --------------------------------------------------------------------------- +# Safetensors extractor +# --------------------------------------------------------------------------- + +class TestSafetensorsExtractor: + def _make_safetensors(self, tmp_path) -> Path: + pytest.importorskip("safetensors") + import torch + from safetensors.torch import save_file + p = tmp_path / "model.safetensors" + save_file({"encoder.weight": torch.zeros(8, 4), "encoder.bias": torch.zeros(8)}, str(p)) + return p + + def test_framework_detected(self, tmp_path): + assert _inspect(self._make_safetensors(tmp_path)).framework in ("safetensors", "pytorch") + + def test_tensor_keys_extracted(self, tmp_path): + assert "encoder.weight" in _inspect(self._make_safetensors(tmp_path)).extra["tensor_keys"] + + def test_tensor_shapes_extracted(self, tmp_path): + shapes = _inspect(self._make_safetensors(tmp_path)).extra["tensor_shapes"] + assert shapes["encoder.weight"] == [8, 4] + + +# --------------------------------------------------------------------------- +# Directory extractor +# --------------------------------------------------------------------------- + +class TestDirectoryExtractor: + def test_config_json_transformers(self, tmp_path): + cfg = {"model_type": "bert", "architectures": ["BertForSequenceClassification"], + "hidden_size": 768, "num_labels": 2, "num_hidden_layers": 12} + (tmp_path / "config.json").write_text(json.dumps(cfg)) + meta = _inspect(tmp_path) + assert meta.framework == "transformers" + assert meta.extra["hf_config"]["model_type"] == "bert" + assert meta.extra["hf_config"]["num_labels"] == 2 + + def test_tokenizer_config(self, tmp_path): + (tmp_path / "config.json").write_text(json.dumps({"model_type": "gpt2"})) + (tmp_path / "tokenizer_config.json").write_text(json.dumps({"tokenizer_class": "GPT2Tokenizer"})) + assert _inspect(tmp_path).extra.get("tokenizer_class") == "GPT2Tokenizer" + + def test_saved_model_pb(self, tmp_path): + (tmp_path / "saved_model.pb").write_bytes(b"") + assert _inspect(tmp_path).raw_facts.get("format") == "tf_savedmodel" + + def test_adapter_config(self, tmp_path): + (tmp_path / "config.json").write_text(json.dumps({"model_type": "llama"})) + (tmp_path / "adapter_config.json").write_text(json.dumps({})) + assert _inspect(tmp_path).raw_facts.get("is_peft_adapter") is True + + def test_empty_directory(self, tmp_path): + assert _inspect(tmp_path).framework == "unknown" + + +# --------------------------------------------------------------------------- +# Graceful fallback +# --------------------------------------------------------------------------- + +class TestGracefulFallback: + def test_bad_bytes_warns_not_raises(self, tmp_path): + p = tmp_path / "model.pkl" + p.write_bytes(b"\x08\x00\x00\x00" + b"\x00" * 100) + meta, w = _inspect_warn(p) + assert meta.framework == "unknown" + assert len(w) == 1 + assert "partial metadata" in str(w[0].message) + + def test_inspection_errors_populated(self, tmp_path): + p = tmp_path / "model.pkl" + p.write_bytes(b"\xff\xfe" + b"\x00" * 50) + meta, _ = _inspect_warn(p) + assert len(meta.inspection_errors) > 0 diff --git a/uv.lock b/uv.lock index 65e0dc4..463fdc7 100644 --- a/uv.lock +++ b/uv.lock @@ -2,12 +2,24 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32'", + "python_full_version >= '3.14' and platform_machine == 's390x' and 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