diff --git a/src/python/examples/mlflow_to_fnnx.py b/src/python/examples/mlflow_to_fnnx.py new file mode 100644 index 0000000..a92d7bc --- /dev/null +++ b/src/python/examples/mlflow_to_fnnx.py @@ -0,0 +1,261 @@ +"""Hands-on demo: MLflow model -> FNNX package -> inspect -> run. + +Builds a tiny MLflow model for one framework, converts it with +``fnnx.extras.mlflow.package_mlflow_model``, prints the package contents + +manifest/env/provenance, then runs it through ``fnnx.runtime.Runtime`` and +compares against the original model. + +Usage (from src/python, with the deps for the chosen framework installed): + + python examples/mlflow_to_fnnx.py sklearn + python examples/mlflow_to_fnnx.py torch + python examples/mlflow_to_fnnx.py langchain + python examples/mlflow_to_fnnx.py inspect _fnnx_demo/sklearn.fnnx # inspect only + +Each build writes .fnnx and the source MLflow dir under ./_fnnx_demo/ +so you can poke at them afterwards (e.g. `tar -tf _fnnx_demo/sklearn.fnnx`). + +The key thing the demo illustrates is how the runtime ``inputs`` dict is keyed, +which depends on the converter's chosen ``input_mode``: + * columns mode -> {column_name: 1-D array, ...} (one key per signature column) + * tensor mode -> {"input": ndarray} (single unnamed tensor) + or {name: ndarray, ...} (named tensors) + * json mode -> {"data": } (passthrough / nested signatures) +""" + +from __future__ import annotations + +import json +import os +import shutil +import sys +import tarfile + +from fnnx.extras.mlflow import package_mlflow_model +from fnnx.extras.reader import Reader +from fnnx.runtime import Runtime + + +OUT_DIR = os.path.join(os.getcwd(), "_fnnx_demo") + + +def _reset(model_dir: str) -> None: + """mlflow.save_model refuses a non-empty target; clear it for re-runs.""" + shutil.rmtree(model_dir, ignore_errors=True) + + +def inspect(fnnx_path: str) -> None: + """Print the package file tree + manifest/env/provenance via Reader.""" + print(f"\n=== inspect {fnnx_path} ===") + with tarfile.open(fnnx_path, "r") as tar: + print("-- files --") + for name in sorted(tar.getnames()): + print(" ", name) + + reader = Reader(fnnx_path) + print("-- manifest --") + print(reader.manifest.model_dump_json(indent=2)) + print("-- input_mode (variant_config) --") + with tarfile.open(fnnx_path, "r") as tar: + vc = json.loads(tar.extractfile("variant_config.json").read().decode()) # type: ignore[union-attr] + print(" ", vc["extra_values"]["fnnx_mlflow"]) + if reader.pyenv is not None: + print("-- pip dependencies --") + for dep in reader.pyenv.dependencies: + print(" ", dep.package) + print("-- provenance (meta) --") + for entry in reader.metadata: + print(" id:", entry.id, "| tags:", entry.producer_tags) + + +def demo_sklearn() -> None: + """RandomForest on a 2-column frame -> columns input_mode.""" + import mlflow + import numpy as np + import pandas as pd + from mlflow.models.signature import infer_signature + from sklearn.ensemble import RandomForestClassifier + + model_dir = os.path.join(OUT_DIR, "sklearn_model") + out = os.path.join(OUT_DIR, "sklearn.fnnx") + + x = pd.DataFrame({"a": [0.0, 1.0, 2.0, 3.0], "b": [3.0, 2.0, 1.0, 0.0]}) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=10, random_state=0).fit(x, y) + sig = infer_signature(x, model.predict(x)) + _reset(model_dir) + mlflow.sklearn.save_model(model, model_dir, signature=sig) # type: ignore[attr-defined] + + package_mlflow_model(model_dir, out, name="sklearn-rf") + inspect(out) + + rt = Runtime(out) + inputs = { + "a": x["a"].to_numpy(np.float64), + "b": x["b"].to_numpy(np.float64), + } + res = rt.compute(inputs, {}) + print("\nfnnx predictions :", res["predictions"]) + print("sklearn predict :", model.predict(x).tolist()) + + +def demo_sklearn_array() -> None: + """RandomForest trained on a bare numpy array -> tensor input_mode. + + Inferring the signature from a numpy ndarray (instead of a DataFrame) + records a single unnamed TensorSpec, so the converter picks + input_mode="tensor": the manifest has ONE input named "input" that takes + the whole array. You call it with {"input": X} -- one key, the raw array -- + rather than one key per column. (The model still just sees model.predict(X).) + """ + import mlflow + import numpy as np + from mlflow.models.signature import infer_signature + from sklearn.ensemble import RandomForestClassifier + + model_dir = os.path.join(OUT_DIR, "sklearn_array_model") + out = os.path.join(OUT_DIR, "sklearn_array.fnnx") + + x = np.array([[0.0, 3.0], [1.0, 2.0], [2.0, 1.0], [3.0, 0.0]], dtype=np.float64) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=10, random_state=0).fit(x, y) + sig = infer_signature(x, model.predict(x)) # ndarray in -> TensorSpec + _reset(model_dir) + mlflow.sklearn.save_model(model, model_dir, signature=sig) # type: ignore[attr-defined] + + package_mlflow_model(model_dir, out, name="sklearn-rf-array") + inspect(out) + + rt = Runtime(out) + res = rt.compute({"input": x}, {}) # single "input" key holding the whole array + print("\nfnnx predictions :", res["predictions"]) + print("sklearn predict :", model.predict(x).tolist()) + + +def demo_torch() -> None: + """nn.Module in a separate module + code_paths -> tensor input_mode.""" + import mlflow + import numpy as np + import torch + from mlflow.models import ModelSignature + from mlflow.types.schema import Schema, TensorSpec + + # The module must live outside __main__ so torch's by-reference pickle can + # resolve it; code_paths then embeds the source into the package so it + # reloads on a clean machine too. + mod_path = os.path.join(OUT_DIR, "tinynet_mod.py") + with open(mod_path, "w") as f: + f.write( + "import torch\n" + "from torch import nn\n" + "N_FEATURES = 4\n" + "class TinyNet(nn.Module):\n" + " def __init__(self):\n" + " super().__init__()\n" + " self.linear = nn.Linear(N_FEATURES, 2)\n" + " def forward(self, x):\n" + " return self.linear(x)\n" + ) + if OUT_DIR not in sys.path: + sys.path.insert(0, OUT_DIR) + from tinynet_mod import N_FEATURES, TinyNet # type: ignore[import-not-found] + + torch.manual_seed(0) + model = TinyNet().eval() + sample = np.random.default_rng(0).random((5, N_FEATURES)).astype(np.float32) + + model_dir = os.path.join(OUT_DIR, "torch_model") + out = os.path.join(OUT_DIR, "torch.fnnx") + signature = ModelSignature( + inputs=Schema([TensorSpec(np.dtype("float32"), [-1, N_FEATURES])]), + outputs=Schema([TensorSpec(np.dtype("float32"), [-1, 2])]), + ) + _reset(model_dir) + # torch.save pickles the module by reference (its class must be importable); + # code_paths embeds the source so the package reloads on a clean machine. + mlflow.pytorch.save_model( # type: ignore[attr-defined] + model, + model_dir, + signature=signature, + code_paths=[mod_path], + ) + + package_mlflow_model(model_dir, out, name="torch-tiny") + inspect(out) + + rt = Runtime(out) + res = rt.compute({"input": sample}, {}) # single unnamed tensor -> "input" + with torch.no_grad(): + expected = model(torch.from_numpy(sample)).numpy() + actual = np.asarray(res["predictions"], dtype=np.float32) + print("\nmax abs diff vs torch forward:", float(np.abs(actual - expected).max())) + + +def demo_langchain() -> None: + """models-from-code RunnableLambda with an Object signature -> json mode.""" + import mlflow + from mlflow.models import ModelSignature + from mlflow.types import DataType + from mlflow.types.schema import ColSpec, Object, Property, Schema + + # models-from-code: a script that builds the runnable and registers it via + # mlflow.models.set_model. The whole script is embedded in the package, so + # it reloads without code_paths. + script_path = os.path.join(OUT_DIR, "langchain_model.py") + with open(script_path, "w") as f: + f.write( + "import mlflow\n" + "from langchain_core.runnables import RunnableLambda\n" + "def _shout(record):\n" + " payload = record.get('payload', record) if isinstance(record, dict) else record\n" + " text = payload.get('text', '') if isinstance(payload, dict) else str(payload)\n" + " return {'out': str(text).upper() + '!'}\n" + "mlflow.models.set_model(RunnableLambda(_shout))\n" + ) + + signature = ModelSignature( + inputs=Schema( + [ColSpec(name="payload", type=Object(properties=[Property("text", DataType.string)]))] + ), + outputs=Schema([ColSpec(type=DataType.string)]), + ) + + model_dir = os.path.join(OUT_DIR, "langchain_model") + out = os.path.join(OUT_DIR, "langchain.fnnx") + _reset(model_dir) + mlflow.langchain.save_model( # type: ignore[attr-defined] + lc_model=script_path, path=model_dir, signature=signature + ) + + package_mlflow_model(model_dir, out, name="langchain-shout") + inspect(out) + + rt = Runtime(out) + res = rt.compute({"data": {"payload": {"text": "hello"}}}, {}) # json mode -> "data" + direct = mlflow.pyfunc.load_model(model_dir).predict({"payload": {"text": "hello"}}) + print("\nfnnx predictions :", res["predictions"]) + print("direct predict :", direct) + + +DEMOS = { + "sklearn": demo_sklearn, + "sklearn-array": demo_sklearn_array, + "torch": demo_torch, + "langchain": demo_langchain, +} + + +def main() -> None: + if len(sys.argv) < 2 or sys.argv[1] not in (*DEMOS, "inspect"): + print(f"usage: {sys.argv[0]} {{{'|'.join(DEMOS)}|inspect }}") + raise SystemExit(2) + + os.makedirs(OUT_DIR, exist_ok=True) + if sys.argv[1] == "inspect": + inspect(sys.argv[2]) + return + DEMOS[sys.argv[1]]() + + +if __name__ == "__main__": + main() diff --git a/src/python/fnnx/__init__.py b/src/python/fnnx/__init__.py index b2f0155..6e2648a 100644 --- a/src/python/fnnx/__init__.py +++ b/src/python/fnnx/__init__.py @@ -1 +1 @@ -__version__ = "0.0.11" +__version__ = "0.0.12" diff --git a/src/python/fnnx/extras/_mlflow_wrapper.py b/src/python/fnnx/extras/_mlflow_wrapper.py new file mode 100644 index 0000000..ecc436b --- /dev/null +++ b/src/python/fnnx/extras/_mlflow_wrapper.py @@ -0,0 +1,162 @@ +"""Static runtime wrapper for FNNX packages produced by ``fnnx.extras.mlflow``. + +Every converted MLflow model uses this exact class as its pyfunc entry point; +per-model behavior is driven by ``extra_values["fnnx_mlflow"]`` plus the +manifest. This module imports only ``fnnx``; ``mlflow`` is imported lazily in +``warmup`` and ``numpy``/``pandas`` are probed at call time so this file stays +importable in environments that lack them. +""" + +from contextlib import contextmanager +from dataclasses import asdict as dataclass_asdict, is_dataclass +import os +import sys + +from fnnx.variants.pyfunc import PyFunc + + +_SINGLE_TENSOR_SENTINEL = "__single__" + +# mlflow reads this at model-load time to choose the prediction device for +# device-aware flavors (e.g. pytorch). We bridge FNNX's device_map onto it. +_MLFLOW_DEVICE_ENV = "MLFLOW_DEFAULT_PREDICTION_DEVICE" + + +def _fnnx_device_to_mlflow(accelerator) -> str | None: + """Map an FNNX ``device_map`` accelerator to an mlflow prediction device. + + Accepts the ``DeviceMap`` carried by ``Context.device`` or a bare string. + Returns ``None`` when there is no actionable device hint (``"auto"`` or an + unknown accelerator), leaving mlflow's own auto-detection in charge. + """ + if hasattr(accelerator, "accelerator"): # a DeviceMap flowed through + accelerator = accelerator.accelerator + if not isinstance(accelerator, str): + return None + a = accelerator.strip().lower() + if a == "cpu": + return "cpu" + if a in ("cuda", "gpu"): + return "cuda" + if a.startswith("cuda:"): + return accelerator # preserve an explicit index, e.g. "cuda:1" + return None # auto / mps / unknown -> let mlflow decide + + +@contextmanager +def _prediction_device(device: str | None): + """Scope ``MLFLOW_DEFAULT_PREDICTION_DEVICE`` to a model load. + + A device already present in the environment wins (an explicit runtime + override), and the previous value is always restored so the process + environment is never mutated permanently. + """ + if not device or os.environ.get(_MLFLOW_DEVICE_ENV): + yield + return + os.environ[_MLFLOW_DEVICE_ENV] = device + try: + yield + finally: + os.environ.pop(_MLFLOW_DEVICE_ENV, None) + + +def _to_jsonable(x): + """Normalize predict() output into JSON-safe data. + + Handles numpy scalars/ndarrays, pandas DataFrame/Series, pydantic models, + dataclasses, dicts, lists/tuples; falls back to ``str(x)`` for anything + else. numpy/pandas/pydantic are detected via ``sys.modules`` so this helper + works without them installed. + """ + if x is None or isinstance(x, (bool, int, float, str)): + return x + + np = sys.modules.get("numpy") + if np is not None: + if isinstance(x, np.ndarray): + return x.tolist() + if isinstance(x, np.generic): + return x.item() + + pd = sys.modules.get("pandas") + if pd is not None: + if isinstance(x, pd.DataFrame): + return x.to_dict(orient="records") + if isinstance(x, pd.Series): + return x.tolist() + + pydantic = sys.modules.get("pydantic") + if pydantic is not None: + base_model = getattr(pydantic, "BaseModel", None) + if base_model is not None and isinstance(x, base_model): + return x.model_dump() + + if is_dataclass(x) and not isinstance(x, type): + return dataclass_asdict(x) + + if isinstance(x, dict): + return {k: _to_jsonable(v) for k, v in x.items()} + if isinstance(x, (list, tuple)): + return [_to_jsonable(v) for v in x] + + return str(x) + + +class MLflowModel(PyFunc): + """Single static pyfunc wrapper for any converted MLflow model.""" + + def warmup(self): + import mlflow.pyfunc # type: ignore[import-not-found] + + cfg = self.fnnx_context.get_value("fnnx_mlflow") or {} + self._cfg = cfg + model_dir = self.fnnx_context.get_dirpath(cfg["model_dir"]) + if model_dir is None: + raise FileNotFoundError( + f"MLflow model directory '{cfg['model_dir']}' not found in package" + ) + # Bridge the FNNX runtime's device_map onto mlflow's load-time device + # selection (honored by device-aware flavors such as pytorch). The + # default device_map accelerator is "cpu", so a converted model runs on + # CPU unless the runtime is given device_map="cuda" (or an explicit + # MLFLOW_DEFAULT_PREDICTION_DEVICE env var, which still wins). + device = _fnnx_device_to_mlflow(getattr(self.fnnx_context, "device", None)) + with _prediction_device(device): + self.model = mlflow.pyfunc.load_model(model_dir) + + def _build_payload(self, inputs: dict): + cfg = self._cfg + mode = cfg.get("input_mode", "passthrough") + + if mode == "columns": + import pandas as pd + + column_order = cfg.get("column_order") or list(inputs.keys()) + return pd.DataFrame({c: inputs[c] for c in column_order}) + + if mode == "tensor": + tensor_names = cfg.get("tensor_names") or [] + if tensor_names == [_SINGLE_TENSOR_SENTINEL]: + return inputs["input"] + return {n: inputs[n] for n in tensor_names} + + return inputs["data"] + + def compute(self, inputs, dynamic_attributes): + payload = self._build_payload(inputs) + + param_names = self._cfg.get("param_names") or [] + params = { + n: dynamic_attributes[n] for n in param_names if n in dynamic_attributes + } + + if params: + result = self.model.predict(payload, params=params) + else: + result = self.model.predict(payload) + + return {"predictions": _to_jsonable(result)} + + async def compute_async(self, inputs, dynamic_attributes): + return self.compute(inputs, dynamic_attributes) diff --git a/src/python/fnnx/extras/builder.py b/src/python/fnnx/extras/builder.py index 6ecf974..525b4d0 100644 --- a/src/python/fnnx/extras/builder.py +++ b/src/python/fnnx/extras/builder.py @@ -48,6 +48,7 @@ def __init__( model_version: str | None = None, model_description: str | None = None, create_meta_callback: Callable | None = None, + python_version: str | None = None, ) -> None: self._inputs: list[NDJSON | JSON] = [] self._outputs: list[NDJSON | JSON] = [] @@ -71,6 +72,8 @@ def __init__( self._build_dependencies = [] self._rt_dependencies = [] + self._python_version = python_version + self.create_meta_callback = create_meta_callback if isinstance(pyfunc, PyFuncSpec): @@ -123,10 +126,15 @@ def add_env_var(self, env_var: Var) -> None: def set_extra_values(self, values: dict) -> None: self._extra_values = values.copy() - def define_dtype(self, name: str, dtype: Type[PydanticBaseModel]) -> None: + def define_dtype( + self, name: str, dtype: Type[PydanticBaseModel] | dict + ) -> None: if not name.startswith("ext::"): raise ValueError("dtype name must start with 'ext::'") - self._extra_dtypes[name] = dtype.model_json_schema() + if isinstance(dtype, dict): + self._extra_dtypes[name] = dtype + else: + self._extra_dtypes[name] = dtype.model_json_schema() def set_producer_info( self, name: str, version: str, tags: list[str] | None = None @@ -262,10 +270,11 @@ def add_fnnx_runtime_dependency(self, core: bool = False) -> None: self.add_runtime_dependency(f"{dependency_name}=={fnnx_version}") def _make_env(self): + python_version = self._python_version or PYTHON_VERSION return { "python3::conda_pip": asdict( Python3_CondaPip( - python_version=PYTHON_VERSION, + python_version=python_version, build_dependencies=self._build_dependencies, dependencies=[PipDependency(package=p) for p in self._rt_dependencies], ) diff --git a/src/python/fnnx/extras/mlflow.py b/src/python/fnnx/extras/mlflow.py new file mode 100644 index 0000000..b7ad285 --- /dev/null +++ b/src/python/fnnx/extras/mlflow.py @@ -0,0 +1,886 @@ +"""MLflow → FNNX packager. + +This module converts an MLflow model directory (or any MLflow URI) into an +FNNX pyfunc-variant package. The local-directory path never imports +``mlflow``: it parses ``MLmodel`` (YAML) and the JSON-encoded signature +strings directly, mirroring what MLflow itself writes. ``mlflow`` is imported +lazily only where it is unavoidable — remote URI download and optional +``verify=True``. + +Public API (added across Task 2/3): ``package_mlflow_model``, +``MLflowConverter``. Task 2 lands the readers and mapping helpers consumed by +the orchestrator (Task 3). +""" + +from __future__ import annotations + +import json +import os +import shutil +import sys +import tempfile +from dataclasses import dataclass +from typing import Any, Callable + +from fnnx import __version__ as fnnx_version +from fnnx.console import console +from fnnx.extras.builder import PyfuncBuilder, PyFuncSpec +from fnnx.extras.pydantic_models.manifest import NDJSON, JSON, Var +from fnnx.extras.pydantic_models.meta import MetaEntry + + +_PERMISSIVE_INPUT_DTYPE = "ext::mlflow::input" +_PERMISSIVE_OUTPUT_DTYPE = "ext::mlflow::output" + +_SINGLE_TENSOR_SENTINEL = "__single__" + + +class MLflowPackagingVerificationError(RuntimeError): + """Raised when ``verify=True`` fails to load or run the converted package. + + Typically indicates the underlying MLflow model is not self-contained: + a required class definition is missing from the pinned environment, the + embedded artifacts are incomplete, or a pickle dependency does not match. + The original exception is kept as ``__cause__``. + """ + +# MLflow DataType → numpy dtype token used in FNNX Array[...]. +_SCALAR_DTYPE_MAP: dict[str, str] = { + "boolean": "bool", + "integer": "int32", + "long": "int64", + "float": "float32", + "double": "float64", + "string": "string", +} + + +@dataclass +class MLModelInfo: + """Normalized view of an MLflow ``MLmodel`` document. + + Both the mlflow-backed and YAML-fallback readers return this same shape so + every downstream helper is reader-agnostic. + """ + + flavors: dict[str, dict] + signature_inputs: list[dict] | None + signature_outputs: list[dict] | None + signature_params: list[dict] | None + metadata: dict | None + mlflow_version: str | None + saved_input_example_info: dict | None + raw_mlmodel: dict + + @property + def loader_module(self) -> str | None: + """Pyfunc loader module name (e.g. ``mlflow.sklearn``), if present.""" + pf = self.flavors.get("python_function") or {} + loader = pf.get("loader_module") + return loader if isinstance(loader, str) else None + + @property + def primary_flavor(self) -> str: + """Non-python_function flavor name, or ``python_function`` as fallback.""" + for name in self.flavors: + if name != "python_function": + return name + return "python_function" + + +def _load_model_info(local_dir: str) -> MLModelInfo: + """Read an MLflow model directory into the normalized ``MLModelInfo``. + + Prefers the mlflow library when importable (its own deserializers absorb + format-version quirks); falls back to a direct YAML+JSON read when not. + """ + try: + import mlflow # type: ignore[import-not-found] # noqa: F401 + except ImportError: + return _load_model_info_yaml(local_dir) + return _load_model_info_mlflow(local_dir) + + +def _load_model_info_mlflow(local_dir: str) -> MLModelInfo: + """Read MLmodel via the mlflow library (preferred).""" + import mlflow.models # type: ignore[import-not-found] + + m = mlflow.models.Model.load(local_dir) + + sig = m.signature + signature_inputs: list[dict] | None = None + signature_outputs: list[dict] | None = None + signature_params: list[dict] | None = None + if sig is not None: + # to_json() + json.loads matches what MLflow itself writes into the + # MLmodel signature strings — tuples become lists — so the result + # equals what the YAML reader produces. + if sig.inputs is not None: + signature_inputs = json.loads(sig.inputs.to_json()) + if sig.outputs is not None: + signature_outputs = json.loads(sig.outputs.to_json()) + if sig.params is not None: + signature_params = json.loads(sig.params.to_json()) + + return MLModelInfo( + flavors=dict(m.flavors or {}), + signature_inputs=signature_inputs, + signature_outputs=signature_outputs, + signature_params=signature_params, + metadata=m.metadata, + mlflow_version=m.mlflow_version, + saved_input_example_info=m.saved_input_example_info, + raw_mlmodel=m.to_dict(), + ) + + +def _load_model_info_yaml(local_dir: str) -> MLModelInfo: + """Read MLmodel by parsing the YAML directly (no mlflow import).""" + import yaml # pyyaml — declared in fnnx[extras] + + mlmodel_path = os.path.join(local_dir, "MLmodel") + if not os.path.isfile(mlmodel_path): + raise FileNotFoundError( + f"MLflow MLmodel file not found at {mlmodel_path!r}" + ) + with open(mlmodel_path, "r") as f: + raw = yaml.safe_load(f) or {} + if not isinstance(raw, dict): + raise ValueError( + f"MLmodel at {mlmodel_path!r} did not parse to a mapping" + ) + + sig = raw.get("signature") or {} + signature_inputs = _parse_signature_field(sig.get("inputs")) + signature_outputs = _parse_signature_field(sig.get("outputs")) + signature_params = _parse_signature_field(sig.get("params")) + + return MLModelInfo( + flavors=dict(raw.get("flavors") or {}), + signature_inputs=signature_inputs, + signature_outputs=signature_outputs, + signature_params=signature_params, + metadata=raw.get("metadata"), + mlflow_version=raw.get("mlflow_version"), + saved_input_example_info=raw.get("saved_input_example_info"), + raw_mlmodel=raw, + ) + + +def _parse_signature_field(field: Any) -> list[dict] | None: + """Parse a signature field (`inputs`/`outputs`/`params`). + + MLflow writes these as JSON-encoded strings inside the MLmodel YAML, but + we tolerate already-decoded lists too. + """ + if field is None: + return None + if isinstance(field, str): + decoded = json.loads(field) + else: + decoded = field + if decoded is None: + return None + if not isinstance(decoded, list): + raise ValueError( + f"Signature field expected a JSON list, got {type(decoded).__name__}" + ) + return decoded + + +def _map_input_schema( + signature_inputs: list[dict] | None, +) -> tuple[list[NDJSON | JSON], str, dict, list[str]]: + """Translate normalized MLflow signature inputs into FNNX manifest inputs. + + Returns ``(specs, input_mode, cfg_fragment, warnings)`` where + ``cfg_fragment`` is merged into ``extra_values["fnnx_mlflow"]`` and + ``warnings`` are user-facing strings to surface via ``console.warn``. + """ + if signature_inputs is None: + spec = JSON( + name="data", + content_type="JSON", + dtype=_PERMISSIVE_INPUT_DTYPE, + ) + cfg = {"input_mode": "passthrough"} + warnings = [ + "MLflow model has no signature; using passthrough mode. " + "Pass `input_specs=` to declare typed inputs." + ] + return [spec], "passthrough", cfg, warnings + + if not signature_inputs: + raise ValueError("Signature inputs list is empty") + + is_all_tensor = all(_is_tensor_entry(e) for e in signature_inputs) + if is_all_tensor: + return _map_tensor_inputs(signature_inputs) + + is_all_simple_scalar = all(_is_simple_scalar_entry(e) for e in signature_inputs) + if is_all_simple_scalar: + return _map_columns_inputs(signature_inputs) + + # Nested ColSpec (object/array/map/any) or scalar binary/datetime → json. + return _map_json_inputs() + + +def _is_tensor_entry(entry: dict) -> bool: + return entry.get("type") == "tensor" + + +def _is_simple_scalar_entry(entry: dict) -> bool: + """Scalar ColSpec with a DataType that maps to an Array[...] token.""" + t = entry.get("type") + return isinstance(t, str) and t in _SCALAR_DTYPE_MAP + + +def _map_tensor_inputs( + entries: list[dict], +) -> tuple[list[NDJSON | JSON], str, dict, list[str]]: + specs: list[NDJSON | JSON] = [] + tensor_names: list[str] = [] + + if len(entries) == 1 and not entries[0].get("name"): + entry = entries[0] + ts = entry.get("tensor-spec") or {} + dtype_token = str(ts.get("dtype", "float32")) + shape = _normalize_shape(ts.get("shape")) + specs.append( + NDJSON( + name="input", + content_type="NDJSON", + dtype=f"Array[{dtype_token}]", + shape=shape, + ) + ) + tensor_names.append(_SINGLE_TENSOR_SENTINEL) + else: + for idx, entry in enumerate(entries): + name = entry.get("name") or f"input_{idx}" + ts = entry.get("tensor-spec") or {} + dtype_token = str(ts.get("dtype", "float32")) + shape = _normalize_shape(ts.get("shape")) + specs.append( + NDJSON( + name=name, + content_type="NDJSON", + dtype=f"Array[{dtype_token}]", + shape=shape, + ) + ) + tensor_names.append(name) + + cfg = {"input_mode": "tensor", "tensor_names": tensor_names} + return specs, "tensor", cfg, [] + + +_BATCH_DIM = "batch" + + +def _normalize_shape(shape: Any) -> list[str | int]: + """Convert an MLflow tensor shape into an FNNX shape. + + MLflow encodes a variable dimension as ``-1`` or ``None``. FNNX matches an + int dim by exact equality and treats a *string* dim as a wildcard, so a + variable dim must be emitted as a named string — never the literal int + ``-1`` (which the runtime would require the input to literally equal). The + leading variable dim becomes ``"batch"``; any other variable dim becomes + ``"dim_"``. + """ + if shape is None: + return [_BATCH_DIM] + out: list[str | int] = [] + for i, dim in enumerate(shape): + if dim is None or int(dim) < 0: + out.append(_BATCH_DIM if i == 0 else f"dim_{i}") + else: + out.append(int(dim)) + return out + + +def _map_columns_inputs( + entries: list[dict], +) -> tuple[list[NDJSON | JSON], str, dict, list[str]]: + specs: list[NDJSON | JSON] = [] + column_order: list[str] = [] + for idx, entry in enumerate(entries): + name = entry.get("name") or f"col_{idx}" + token = _SCALAR_DTYPE_MAP[entry["type"]] + specs.append( + NDJSON( + name=name, + content_type="NDJSON", + dtype=f"Array[{token}]", + shape=[_BATCH_DIM], + ) + ) + column_order.append(name) + cfg = {"input_mode": "columns", "column_order": column_order} + return specs, "columns", cfg, [] + + +def _map_json_inputs() -> tuple[list[NDJSON | JSON], str, dict, list[str]]: + spec = JSON( + name="data", + content_type="JSON", + dtype=_PERMISSIVE_INPUT_DTYPE, + ) + cfg = {"input_mode": "json"} + return [spec], "json", cfg, [] + + +def _map_output_schema() -> tuple[list[NDJSON | JSON], list[str]]: + """Outputs are always a single permissive JSON channel in v1.""" + spec = JSON( + name="predictions", + content_type="JSON", + dtype=_PERMISSIVE_OUTPUT_DTYPE, + ) + return [spec], [] + + +def _map_params( + signature_params: list[dict] | None, +) -> tuple[list[Var], list[str]]: + """Translate ParamSpec entries into FNNX dynamic_attributes + their names.""" + if not signature_params: + return [], [] + vars_: list[Var] = [] + names: list[str] = [] + for entry in signature_params: + name = entry.get("name") + if not isinstance(name, str) or not name: + raise ValueError(f"ParamSpec entry missing 'name': {entry!r}") + ptype = entry.get("type", "?") + default = entry.get("default", None) + vars_.append( + Var( + name=name, + description=( + f"MLflow predict() param '{name}' " + f"(type={ptype}, default={default!r})" + ), + ) + ) + names.append(name) + return vars_, names + + +def _map_env( + local_dir: str, + flavors: dict[str, dict] | None = None, +) -> tuple[str | None, list[str], list[str]]: + """Derive python version, build deps, and runtime deps for the FNNX env. + + Reads ``python_env.yaml`` and ``requirements.txt`` from the MLflow model + dir. Falls back to the pyfunc flavor's ``python_version`` (then the + current interpreter, signaled by ``None`` to the builder) when + ``python_env.yaml`` is absent. + """ + python_env_path = os.path.join(local_dir, "python_env.yaml") + requirements_path = os.path.join(local_dir, "requirements.txt") + + python_version: str | None = None + build_deps: list[str] = [] + + if os.path.isfile(python_env_path): + import yaml + + with open(python_env_path, "r") as f: + data = yaml.safe_load(f) or {} + if isinstance(data, dict): + pv = data.get("python") + if isinstance(pv, str) and pv: + python_version = pv + bd = data.get("build_dependencies") + if isinstance(bd, list): + build_deps = [str(x) for x in bd if isinstance(x, str)] + + if python_version is None and flavors is not None: + pf = flavors.get("python_function") or {} + pv = pf.get("python_version") + if isinstance(pv, str) and pv: + python_version = pv + + runtime_deps: list[str] = [] + if os.path.isfile(requirements_path): + runtime_deps = _parse_requirements_txt(requirements_path) + + return python_version, build_deps, runtime_deps + + +def _parse_requirements_txt(path: str) -> list[str]: + """Return one line per pip dependency. + + Skips blank lines, comments, and pip option / include lines (``-r``, + ``-c``, anything starting with ``-``). + """ + deps: list[str] = [] + with open(path, "r") as f: + for raw in f: + line = raw.strip() + if not line or line.startswith("#"): + continue + if line.startswith("-"): + continue + deps.append(line) + return deps + + +def _build_meta_payload(info: MLModelInfo, input_mode: str) -> dict: + """Provenance payload for the ``mlflow-source`` meta entry.""" + return { + "mlmodel": info.raw_mlmodel, + "flavors": list(info.flavors.keys()), + "loader_module": info.loader_module, + "signature": { + "inputs": info.signature_inputs, + "outputs": info.signature_outputs, + "params": info.signature_params, + }, + "mlflow_version": info.mlflow_version, + "input_mode": input_mode, + } + + +def create_meta_callback( + info: MLModelInfo, + input_mode: str, + producer_version: str, +) -> Callable: + """Return a callback for ``PyfuncBuilder.create_meta_callback``. + + The callback writes a single ``mlflow-source`` MetaEntry capturing the + original MLmodel content for lossless provenance. + """ + # No auto-added tags: the flavor and mlflow version are already captured + # losslessly in the payload (``flavors`` / ``mlflow_version``). + entry = MetaEntry( + id="mlflow-source", + producer="fnnx.ai", + producer_version=producer_version, + producer_tags=[], + payload=_build_meta_payload(info, input_mode), + ) + + def _callback(file_obj) -> None: + file_obj.create_file( + "meta.json", + json.dumps([entry.model_dump()], indent=4, default=str), + ) + + return _callback + + +_PICKLE_LOADERS: set[str] = { + "mlflow.pytorch", + "mlflow.sklearn", + "mlflow.pyfunc.model", +} + +_NATIVE_LOADERS: set[str] = { + "mlflow.onnx", + "mlflow.xgboost", + "mlflow.lightgbm", +} + + +def _self_containment_warnings( + info: MLModelInfo, local_dir: str +) -> list[str]: + """Emit warnings when the MLflow model dir may not be self-contained. + + The FNNX package inherits the underlying model's self-containment exactly + (never worse, never better). These heuristics flag known risky shapes + without ever blocking the build. + """ + warnings: list[str] = [] + + code_dir = os.path.join(local_dir, "code") + has_code_dir = os.path.isdir(code_dir) + + pf = info.flavors.get("python_function") or {} + model_code_path = pf.get("model_code_path") + + is_models_from_code = bool(model_code_path) + is_self_contained_ish = has_code_dir or is_models_from_code + + loader = info.loader_module + + if loader == "mlflow.transformers": + tf = info.flavors.get("transformers") or {} + save_pretrained = tf.get("save_pretrained") + if save_pretrained is False: + warnings.append( + "Transformers flavor was saved with `save_pretrained=False`: " + "model weights are referenced from the Hugging Face Hub, not " + "embedded in the model directory. The FNNX package will need " + "Hub access at load time." + ) + return warnings + + if loader in _NATIVE_LOADERS: + return warnings + + if is_self_contained_ish: + return warnings + + if loader in _PICKLE_LOADERS: + warnings.append( + f"Loader '{loader}' serializes Python objects by reference; the " + "model/estimator class must be importable from the pinned " + "requirements at load time. If it was defined in a local module, " + "re-log with `code_paths=[…]`." + ) + + return warnings + + +def emit_warnings(messages: list[str]) -> None: + """Surface a list of warning strings through fnnx.console.""" + for msg in messages: + console.warn(msg) + + +# --------------------------------------------------------------------------- +# Orchestrator +# --------------------------------------------------------------------------- + + +_MLFLOW_REMOTE_HINT = ( + "Resolving non-local MLflow URIs requires the `mlflow` package. " + "Install it with `pip install fnnx[mlflow]` (or `pip install mlflow>=3.4`)." +) + + +def _resolve_model_uri(model_uri: str) -> tuple[str, str | None]: + """Resolve ``model_uri`` to a local directory. + + Returns ``(local_dir, tempdir_to_cleanup)``. ``tempdir_to_cleanup`` is + non-None only when a remote URI was downloaded and the caller must remove + it after ``.save()``. + """ + if os.path.isdir(model_uri): + return model_uri, None + + try: + import mlflow.artifacts # type: ignore[import-not-found] + except ImportError as e: + raise ImportError( + f"{_MLFLOW_REMOTE_HINT} (triggered by model_uri={model_uri!r})" + ) from e + + tmp_root = tempfile.mkdtemp(prefix="fnnx_mlflow_") + try: + local_dir = mlflow.artifacts.download_artifacts( + artifact_uri=model_uri, dst_path=tmp_root + ) + except Exception: + shutil.rmtree(tmp_root, ignore_errors=True) + raise + return local_dir, tmp_root + + +class MLflowConverter: + """Build an FNNX pyfunc-variant package from an MLflow model directory.""" + + def __init__( + self, + model_uri: str, + *, + name: str | None = None, + version: str | None = None, + description: str | None = None, + input_specs: list[NDJSON | JSON] | None = None, + output_specs: list[NDJSON | JSON] | None = None, + extra_pip_dependencies: list[str] | None = None, + producer_tags: list[str] | None = None, + ) -> None: + self._model_uri = model_uri + self._name = name + self._version = version + self._description = description + self._input_specs_override = input_specs + self._output_specs_override = output_specs + self._extra_pip_dependencies = list(extra_pip_dependencies or []) + self._user_producer_tags = list(producer_tags or []) + + self._local_dir, self._tempdir = _resolve_model_uri(model_uri) + self._info = _load_model_info(self._local_dir) + + @property + def info(self) -> MLModelInfo: + return self._info + + @property + def local_dir(self) -> str: + return self._local_dir + + def save(self, output_path: str) -> None: + try: + self._save(output_path) + finally: + self._cleanup_tempdir() + + def _cleanup_tempdir(self) -> None: + if self._tempdir is not None: + shutil.rmtree(self._tempdir, ignore_errors=True) + self._tempdir = None + + def _save(self, output_path: str) -> None: + info = self._info + + input_specs_auto, input_mode, cfg_fragment, input_warnings = ( + _map_input_schema(info.signature_inputs) + ) + output_specs_auto, _ = _map_output_schema() + param_vars, param_names = _map_params(info.signature_params) + + python_version, build_deps, runtime_deps = _map_env( + self._local_dir, flavors=info.flavors + ) + + cfg: dict[str, Any] = {"model_dir": "mlflow_model"} + cfg.update(cfg_fragment) + cfg["param_names"] = param_names + + builder = PyfuncBuilder( + pyfunc=PyFuncSpec( + filepath=_mlflow_wrapper_filepath(), + class_name="MLflowModel", + ), + model_name=self._name, + model_version=self._version, + model_description=self._description, + create_meta_callback=create_meta_callback( + info, input_mode, producer_version=fnnx_version + ), + python_version=python_version, + ) + + # Only user-supplied tags are added; nothing is auto-injected. + builder.set_producer_info( + name="fnnx.ai", + version=fnnx_version, + tags=list(self._user_producer_tags), + ) + + input_specs = ( + self._input_specs_override + if self._input_specs_override is not None + else input_specs_auto + ) + output_specs = ( + self._output_specs_override + if self._output_specs_override is not None + else output_specs_auto + ) + + if _needs_input_permissive_dtype(input_specs): + builder.define_dtype(_PERMISSIVE_INPUT_DTYPE, {}) + if _needs_output_permissive_dtype(output_specs): + builder.define_dtype(_PERMISSIVE_OUTPUT_DTYPE, {}) + + for spec in input_specs: + builder.add_input(spec) + for spec in output_specs: + builder.add_output(spec) + for var in param_vars: + builder.add_dynamic_attribute(var) + + builder.set_extra_values({"fnnx_mlflow": cfg}) + + for dep in build_deps: + builder.add_build_dependency(dep) + for dep in runtime_deps: + builder.add_runtime_dependency(dep) + for dep in self._extra_pip_dependencies: + builder.add_runtime_dependency(dep) + builder.add_fnnx_runtime_dependency() + + builder.add_file(self._local_dir, "mlflow_model") + + builder.save(output_path) + + warnings = list(input_warnings) + warnings.extend(_self_containment_warnings(info, self._local_dir)) + emit_warnings(warnings) + + +def _mlflow_wrapper_filepath() -> str: + """Return the absolute path to the shipped ``MLflowModel`` wrapper.""" + from fnnx.extras import _mlflow_wrapper + + return _mlflow_wrapper.__file__ + + +def _needs_input_permissive_dtype(specs: list[NDJSON | JSON]) -> bool: + return any( + getattr(s, "dtype", None) == _PERMISSIVE_INPUT_DTYPE for s in specs + ) + + +def _needs_output_permissive_dtype(specs: list[NDJSON | JSON]) -> bool: + return any( + getattr(s, "dtype", None) == _PERMISSIVE_OUTPUT_DTYPE for s in specs + ) + + +_MLFLOW_VERIFY_SKIPPED_HINT = ( + "verification skipped: mlflow is not installed in the current environment; " + "install `fnnx[mlflow]` to verify" +) + + +def _example_to_fnnx_inputs( + example: Any, input_mode: str, cfg: dict +) -> dict[str, Any]: + """Convert an MLflow input example into the wrapper's expected ``inputs`` dict. + + Inverse of ``MLflowModel._build_payload``: a DataFrame becomes per-column + arrays (columns mode), a numpy ndarray or a dict of arrays becomes the + tensor payload, and anything else is wrapped under ``data`` for the + json/passthrough modes. + """ + if input_mode == "columns": + column_order = cfg.get("column_order") or [] + pd = sys.modules.get("pandas") + if pd is not None and isinstance(example, pd.DataFrame): + cols = column_order or list(example.columns) + return {c: example[c].to_numpy() for c in cols} + if isinstance(example, dict): + cols = column_order or list(example.keys()) + return {c: example[c] for c in cols} + if isinstance(example, list): + # list-of-records → reconstruct a per-column dict + cols = column_order or (list(example[0].keys()) if example else []) + return {c: [row[c] for row in example] for c in cols} + raise TypeError( + f"Cannot convert input example of type {type(example).__name__!r} " + f"to columns-mode inputs" + ) + + if input_mode == "tensor": + tensor_names = cfg.get("tensor_names") or [] + if tensor_names == [_SINGLE_TENSOR_SENTINEL]: + return {"input": example} + if isinstance(example, dict): + return {n: example[n] for n in tensor_names if n in example} + if len(tensor_names) == 1: + return {tensor_names[0]: example} + raise TypeError( + f"Tensor-mode input example must be a dict for named tensors, " + f"got {type(example).__name__!r}" + ) + + # json / passthrough + return {"data": example} + + +def _verify_package( + output_path: str, local_dir: str, info: MLModelInfo, input_mode: str, cfg: dict +) -> None: + """Smoke-test the converted package via ``fnnx.Runtime``. + + Loads the package (triggering ``warmup`` → ``mlflow.pyfunc.load_model``) and, + when the source MLflow model has an ``input_example``, runs one ``compute`` + using that example as the inputs. Any failure is re-raised as + ``MLflowPackagingVerificationError`` with a self-containment hint. + """ + try: + import mlflow # noqa: F401 + except ImportError: + console.warn(_MLFLOW_VERIFY_SKIPPED_HINT) + return + + from fnnx.runtime import Runtime + + try: + rt = Runtime(output_path) + except Exception as e: + raise MLflowPackagingVerificationError( + f"Failed to load the converted package via fnnx.Runtime: {e}. " + "The MLflow model may not be self-contained (e.g. a class " + "defined in a local module is missing from the pinned " + "requirements; re-log with `code_paths=[…]`)." + ) from e + + if not info.saved_input_example_info: + return + + try: + import mlflow.models # type: ignore[import-not-found] + + source = mlflow.models.Model.load(local_dir) + example = source.load_input_example(local_dir) + except Exception as e: + raise MLflowPackagingVerificationError( + f"Failed to load the saved input example from {local_dir!r}: {e}" + ) from e + + if example is None: + return + + try: + inputs = _example_to_fnnx_inputs(example, input_mode, cfg) + except Exception as e: + raise MLflowPackagingVerificationError( + f"Failed to convert the saved input example into FNNX inputs: {e}" + ) from e + + try: + rt.compute(inputs, {}) + except Exception as e: + raise MLflowPackagingVerificationError( + f"Round-trip compute on the saved input example failed: {e}. " + "Current-env smoke tests only prove portability to the extent the " + "current env matches the target env." + ) from e + + +def package_mlflow_model( + model_uri: str, + output_path: str, + *, + name: str | None = None, + version: str | None = None, + description: str | None = None, + input_specs: list[NDJSON | JSON] | None = None, + output_specs: list[NDJSON | JSON] | None = None, + extra_pip_dependencies: list[str] | None = None, + producer_tags: list[str] | None = None, + verify: bool = False, +) -> None: + """Convert an MLflow model into an FNNX pyfunc-variant package. + + ``model_uri`` can be a local directory or any MLflow URI (``runs:/…``, + ``models:/…``, ``file:/…``, ``s3://…``). Local directories are packaged + without importing ``mlflow``; remote URIs require ``fnnx[mlflow]``. + """ + converter = MLflowConverter( + model_uri, + name=name, + version=version, + description=description, + input_specs=input_specs, + output_specs=output_specs, + extra_pip_dependencies=extra_pip_dependencies, + producer_tags=producer_tags, + ) + try: + converter._save(output_path) + if verify: + info = converter.info + _, input_mode, cfg_fragment, _ = _map_input_schema( + info.signature_inputs + ) + _, param_names = _map_params(info.signature_params) + cfg: dict = {"model_dir": "mlflow_model"} + cfg.update(cfg_fragment) + cfg["param_names"] = param_names + _verify_package( + output_path, converter.local_dir, info, input_mode, cfg + ) + finally: + converter._cleanup_tempdir() diff --git a/src/python/pyproject.toml b/src/python/pyproject.toml index 01b7f96..db4e804 100644 --- a/src/python/pyproject.toml +++ b/src/python/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "fnnx" -version = "0.0.11" +version = "0.0.12" description = "A flexible format to package and deploy any ML model." authors = [ { name = "Oleh Kostromin", email = "oleh@beastbyte.ai" } @@ -15,5 +15,23 @@ classifiers = [ [project.optional-dependencies] core = ["numpy>=1.0.0,<3.0.0", "onnxruntime>=1.0.0,<2.0.0"] -extras = ["pydantic>=2.0.0,<3.0.0"] -test = ["jsonschema>=4.0.0,<5.0.0", "pydantic>=2.0.0,<3.0.0"] \ No newline at end of file +extras = ["pydantic>=2.0.0,<3.0.0", "pyyaml>=5.1"] +mlflow = ["mlflow>=3.4"] +test = [ + "jsonschema>=4.0.0,<5.0.0", + "pydantic>=2.0.0,<3.0.0", + "pyyaml>=5.1", + "mlflow>=3.4", + "scikit-learn>=1.0.0", + "pandas>=1.0.0", + "xgboost>=1.7.0", + "lightgbm>=3.3.0", + "catboost>=1.2.0", + "torch>=2.0.0", + "langchain>=0.3.0", + "langchain-core>=0.3.0", + "langgraph>=0.2.0", + "prophet>=1.1.0", + "tensorflow>=2.12.0", + "pytest>=9.1.1", +] diff --git a/src/python/tests/_mlflow_fixtures/__init__.py b/src/python/tests/_mlflow_fixtures/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/python/tests/_mlflow_fixtures/langchain_model.py b/src/python/tests/_mlflow_fixtures/langchain_model.py new file mode 100644 index 0000000..ac92c8c --- /dev/null +++ b/src/python/tests/_mlflow_fixtures/langchain_model.py @@ -0,0 +1,35 @@ +"""Models-from-code script: deterministic offline LangChain runnable. + +Used by the langchain e2e tests. The runnable is a pure ``RunnableLambda`` +(no LLM, no network). ``mlflow.models.set_model`` registers it so the +MLflow loader can rebuild it from this file alone — which is what makes +the converted FNNX package self-contained without ``code_paths``. +""" + +from __future__ import annotations + +import mlflow # type: ignore[import-not-found] +from langchain_core.runnables import RunnableLambda # type: ignore[import-not-found] + + +def _shout(record): + """Uppercase ``record['payload']['text']`` and append ``!``. + + MLflow's pyfunc adapter validates the input against the saved object + signature (a top-level ``payload`` Object containing ``text``) and hands + the runnable a single record dict with that shape. + """ + if isinstance(record, dict): + payload = record.get("payload", record) + if isinstance(payload, dict): + text = payload.get("text", "") + else: + text = str(payload) + else: + text = str(record) + return {"out": str(text).upper() + "!"} + + +chain = RunnableLambda(_shout) + +mlflow.models.set_model(chain) diff --git a/src/python/tests/_mlflow_fixtures/langgraph_model.py b/src/python/tests/_mlflow_fixtures/langgraph_model.py new file mode 100644 index 0000000..47becc0 --- /dev/null +++ b/src/python/tests/_mlflow_fixtures/langgraph_model.py @@ -0,0 +1,32 @@ +"""Models-from-code script: deterministic offline LangGraph state graph. + +Used by the langgraph e2e tests. A single-node graph performs pure string +manipulation; no LLM, no network. ``mlflow.models.set_model`` registers +the compiled graph so the MLflow loader can rebuild it from this file +alone — the same self-contained shape as the langchain fixture. +""" + +from __future__ import annotations + +from typing import TypedDict + +import mlflow # type: ignore[import-not-found] +from langgraph.graph import StateGraph, START, END # type: ignore[import-not-found] + + +class State(TypedDict): + payload: dict + + +def _shout(state: State) -> State: + text = state["payload"].get("text", "") + return {"payload": {"text": str(text).upper() + "!"}} + + +_graph = StateGraph(State) +_graph.add_node("shout", _shout) +_graph.add_edge(START, "shout") +_graph.add_edge("shout", END) +_compiled = _graph.compile() + +mlflow.models.set_model(_compiled) diff --git a/src/python/tests/_mlflow_fixtures/torch_net.py b/src/python/tests/_mlflow_fixtures/torch_net.py new file mode 100644 index 0000000..0ca97cf --- /dev/null +++ b/src/python/tests/_mlflow_fixtures/torch_net.py @@ -0,0 +1,24 @@ +"""Tiny ``nn.Module`` defined outside ``__main__`` for the pytorch e2e tests. + +The pytorch flavor pickles by reference: the class must be importable from a +module other than ``__main__`` to exercise the ``code_paths`` shipping path. +""" + +from __future__ import annotations + +import torch # type: ignore[import-not-found] +from torch import nn # type: ignore[import-not-found] + + +N_FEATURES = 4 + + +class TinyNet(nn.Module): + """One linear layer; deterministic by construction with fixed weights.""" + + def __init__(self) -> None: + super().__init__() + self.linear = nn.Linear(N_FEATURES, 2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.linear(x) diff --git a/src/python/tests/test_mlflow_e2e.py b/src/python/tests/test_mlflow_e2e.py new file mode 100644 index 0000000..e3e32a4 --- /dev/null +++ b/src/python/tests/test_mlflow_e2e.py @@ -0,0 +1,239 @@ +"""End-to-end test for ``package_mlflow_model``. + +Trains a tiny deterministic sklearn model, packages it with the converter, and +asserts a round-trip through ``fnnx.Runtime`` reproduces the original +``model.predict`` output. + +Also exercises the local-directory path without ``mlflow`` installed (the +packaging step must not import mlflow), and that a non-local URI under the +same condition raises ``ImportError``. +""" + +from __future__ import annotations + +import os +import sys +import tarfile +import tempfile +import unittest + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("sklearn") +pytest.importorskip("pandas") + + +def _train_tiny_sklearn(): + """Return ``(model, x_df, y)`` for a deterministic 4-row binary classifier.""" + from sklearn.ensemble import RandomForestClassifier # type: ignore[import-not-found] + import pandas as pd + + x = pd.DataFrame( + { + "a": [0.0, 1.0, 2.0, 3.0], + "b": [3.0, 2.0, 1.0, 0.0], + } + ) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=3, random_state=0) + model.fit(x, y) + return model, x, y + + +def _save_sklearn_model(tmp: str): + """Save a tiny sklearn model and return ``(model_dir, model, x_df)``.""" + import mlflow # type: ignore[import-not-found] + from mlflow.models.signature import infer_signature # type: ignore[import-not-found] + + model, x, _ = _train_tiny_sklearn() + model_dir = os.path.join(tmp, "mlflow_model") + sig = infer_signature(x, model.predict(x)) + mlflow.sklearn.save_model(model, model_dir, signature=sig) # type: ignore[attr-defined] + return model_dir, model, x + + +class TestSklearnRoundTrip(unittest.TestCase): + def test_columns_mode_round_trip(self): + import numpy as np + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, x = _save_sklearn_model(tmp) + out = os.path.join(tmp, "m.fnnx") + package_mlflow_model(model_dir, out) + + rt = Runtime(out) + inputs = { + "a": np.asarray(x["a"].values, dtype=np.float64), + "b": np.asarray(x["b"].values, dtype=np.float64), + } + res = rt.compute(inputs, {}) + + self.assertIn("predictions", res) + self.assertEqual(res["predictions"], model.predict(x).tolist()) + + def test_package_shape_and_manifest(self): + import json + + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir, _, _ = _save_sklearn_model(tmp) + out = os.path.join(tmp, "m.fnnx") + package_mlflow_model(model_dir, out, name="sklearn-rf") + + with tarfile.open(out, "r") as tar: + names = tar.getnames() + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + env = json.loads( + tar.extractfile("env.json").read().decode() # type: ignore[union-attr] + ) + meta = json.loads( + tar.extractfile("meta.json").read().decode() # type: ignore[union-attr] + ) + + # The MLflow dir is embedded verbatim. + self.assertIn("variant_artifacts/extra_files/mlflow_model/MLmodel", names) + + # Manifest derived from the inferred signature: 2 NDJSON columns. + self.assertEqual(manifest["variant"], "pyfunc") + self.assertEqual(manifest["name"], "sklearn-rf") + # Producer is fnnx.ai with no auto-injected tags. + self.assertEqual(manifest["producer_name"], "fnnx.ai") + self.assertEqual(manifest["producer_tags"], []) + self.assertEqual(len(manifest["inputs"]), 2) + for spec in manifest["inputs"]: + self.assertEqual(spec["content_type"], "NDJSON") + self.assertTrue(spec["dtype"].startswith("Array[float64]")) + self.assertEqual(manifest["outputs"][0]["name"], "predictions") + self.assertEqual(manifest["outputs"][0]["dtype"], "ext::mlflow::output") + + # Variant config exposes the wrapper class + fnnx_mlflow cfg. + self.assertEqual(variant_config["pyfunc_classname"], "MLflowModel") + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual(cfg["model_dir"], "mlflow_model") + self.assertEqual(cfg["input_mode"], "columns") + self.assertEqual(cfg["column_order"], ["a", "b"]) + + # Env pins both mlflow and fnnx via the MLflow requirements + builder. + deps = [d["package"] for d in env["python3::conda_pip"]["dependencies"]] + self.assertTrue(any(d.startswith("mlflow") for d in deps)) + self.assertTrue(any(d.startswith("scikit-learn") for d in deps)) + self.assertTrue(any(d.startswith("fnnx") for d in deps)) + + # Meta carries the full MLmodel for lossless provenance. + self.assertEqual(meta[0]["id"], "mlflow-source") + self.assertEqual(meta[0]["producer"], "fnnx.ai") + self.assertEqual(meta[0]["payload"]["input_mode"], "columns") + self.assertIn("mlmodel", meta[0]["payload"]) + + def test_extra_pip_dependencies_appended(self): + import json + + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir, _, _ = _save_sklearn_model(tmp) + out = os.path.join(tmp, "m.fnnx") + package_mlflow_model( + model_dir, out, extra_pip_dependencies=["my-pkg==1.2.3"] + ) + + with tarfile.open(out, "r") as tar: + env = json.loads( + tar.extractfile("env.json").read().decode() # type: ignore[union-attr] + ) + + deps = [d["package"] for d in env["python3::conda_pip"]["dependencies"]] + self.assertIn("my-pkg==1.2.3", deps) + + def test_user_producer_tags_flow_through(self): + import json + + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir, _, _ = _save_sklearn_model(tmp) + out = os.path.join(tmp, "m.fnnx") + package_mlflow_model(model_dir, out, producer_tags=["team-x", "v2"]) + + with tarfile.open(out, "r") as tar: + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + + # Exactly the user's tags — no auto-injected mlflow/flavor/version tags. + self.assertEqual(manifest["producer_tags"], ["team-x", "v2"]) + + +class TestNoMlflowEnv(unittest.TestCase): + """The local-directory path must not import mlflow at packaging time.""" + + def setUp(self) -> None: + self._saved: dict[str, object] = {} + # Drop and block any cached mlflow modules. + for name in list(sys.modules): + if name == "mlflow" or name.startswith("mlflow."): + self._saved[name] = sys.modules.pop(name) + sys.modules["mlflow"] = None # type: ignore[assignment] + + def tearDown(self) -> None: + sys.modules.pop("mlflow", None) + for name, mod in self._saved.items(): + sys.modules[name] = mod # type: ignore[assignment] + + def _save_model_then_block(self, tmp: str) -> str: + # Save the model before we block mlflow, then re-block. + sys.modules.pop("mlflow", None) + for name, mod in self._saved.items(): + sys.modules[name] = mod # type: ignore[assignment] + try: + model_dir, _, _ = _save_sklearn_model(tmp) + finally: + for name in list(sys.modules): + if name == "mlflow" or name.startswith("mlflow."): + self._saved[name] = sys.modules.pop(name) + sys.modules["mlflow"] = None # type: ignore[assignment] + return model_dir + + def test_local_dir_packages_without_mlflow(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir = self._save_model_then_block(tmp) + out = os.path.join(tmp, "out.fnnx") + package_mlflow_model(model_dir, out) + + # Resulting package is a valid tarball with the MLflow dir embedded. + with tarfile.open(out, "r") as tar: + names = set(tar.getnames()) + self.assertIn("manifest.json", names) + self.assertIn( + "variant_artifacts/extra_files/mlflow_model/MLmodel", names + ) + + # mlflow was never imported during packaging. + self.assertIs(sys.modules.get("mlflow"), None) + + def test_remote_uri_without_mlflow_raises_importerror(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + out = os.path.join(tmp, "out.fnnx") + with self.assertRaises(ImportError) as cm: + package_mlflow_model("models:/my_model/3", out) + + self.assertIn("fnnx[mlflow]", str(cm.exception)) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_frameworks.py b/src/python/tests/test_mlflow_frameworks.py new file mode 100644 index 0000000..533591b --- /dev/null +++ b/src/python/tests/test_mlflow_frameworks.py @@ -0,0 +1,213 @@ +"""End-to-end tests for the MLflow → FNNX converter across the gradient-boosting +framework matrix: XGBoost, LightGBM, and CatBoost. + +For each framework: train a tiny classifier (fixed seed, ~20 rows, few +estimators), save with the matching ``mlflow.`` flavor and an inferred +signature, package via ``package_mlflow_model``, load with ``fnnx.Runtime``, and +assert the round-trip predictions match the original ``model.predict`` exactly. +Also asserts ``input_mode="columns"``, env pins the framework, and that no +self-containment warning is emitted (native, self-describing artifacts). +""" + +from __future__ import annotations + +import json +import os +import tarfile +import tempfile +import unittest +from unittest import mock + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("pandas") +pytest.importorskip("sklearn") + + +def _make_training_data(): + """Return ``(x_df, y)`` — 20-row binary classification frame, fixed seed.""" + import numpy as np + import pandas as pd + + rng = np.random.default_rng(0) + n = 20 + a = rng.random(n) + x = pd.DataFrame( + { + "a": a, + "b": rng.random(n), + "c": rng.random(n), + } + ) + # Deterministic, separable labels driven by column `a`. + y = (a > 0.5).astype(int).tolist() + return x, y + + +def _train_xgboost(): + import xgboost as xgb # type: ignore[import-not-found] + + x, y = _make_training_data() + model = xgb.XGBClassifier(n_estimators=3, max_depth=2, random_state=0) + model.fit(x, y) + return model, x + + +def _train_lightgbm(): + import lightgbm as lgb # type: ignore[import-not-found] + + x, y = _make_training_data() + model = lgb.LGBMClassifier( + n_estimators=3, + max_depth=2, + random_state=0, + min_data_in_leaf=1, + min_data_in_bin=1, + verbose=-1, + ) + model.fit(x, y) + return model, x + + +def _train_catboost(): + import catboost as cb # type: ignore[import-not-found] + + x, y = _make_training_data() + model = cb.CatBoostClassifier( + iterations=3, + depth=2, + random_seed=0, + allow_writing_files=False, + verbose=False, + ) + model.fit(x, y) + return model, x + + +# (flavor name, trainer fn, importorskip target, expected loader_module, +# framework package prefix in requirements.txt). +_FRAMEWORK_MATRIX = [ + ("xgboost", _train_xgboost, "xgboost", "mlflow.xgboost", "xgboost"), + ("lightgbm", _train_lightgbm, "lightgbm", "mlflow.lightgbm", "lightgbm"), + ("catboost", _train_catboost, "catboost", "mlflow.catboost", "catboost"), +] + + +class TestGradientBoostingFrameworks(unittest.TestCase): + """Each gradient-boosting framework round-trips through FNNX losslessly.""" + + def _run_framework( + self, + flavor: str, + trainer, + loader_module: str, + framework_pkg: str, + ) -> None: + pytest.importorskip(flavor) + import mlflow # type: ignore[import-not-found] + import numpy as np + from mlflow.models.signature import infer_signature # type: ignore[import-not-found] + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + model, x = trainer() + save_fn = getattr(mlflow, flavor).save_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir = os.path.join(tmp, f"{flavor}_model") + sig = infer_signature(x, model.predict(x)) + save_fn(model, model_dir, signature=sig) + + out = os.path.join(tmp, f"{flavor}.fnnx") + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + # Inspect the produced package. + with tarfile.open(out, "r") as tar: + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + env = json.loads( + tar.extractfile("env.json").read().decode() # type: ignore[union-attr] + ) + meta = json.loads( + tar.extractfile("meta.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual( + cfg["input_mode"], + "columns", + f"{flavor}: expected columns mode, got {cfg['input_mode']!r}", + ) + self.assertEqual(cfg["column_order"], ["a", "b", "c"]) + + # Manifest: one NDJSON input per column with Array[float64]. + self.assertEqual(len(manifest["inputs"]), 3) + for spec in manifest["inputs"]: + self.assertEqual(spec["content_type"], "NDJSON") + self.assertTrue(spec["dtype"].startswith("Array[float64]")) + + # Env pins the framework + mlflow + fnnx. + deps = [ + d["package"] + for d in env["python3::conda_pip"]["dependencies"] + ] + self.assertTrue( + any(d.startswith(framework_pkg) for d in deps), + f"{flavor}: expected {framework_pkg} in env deps, got {deps!r}", + ) + self.assertTrue(any(d.startswith("mlflow") for d in deps)) + self.assertTrue(any(d.startswith("fnnx") for d in deps)) + + # Native flavor → no self-containment warning emitted. + warned = [c.args[0] for c in warn_mock.call_args_list] + for msg in warned: + self.assertNotIn( + "serializes Python objects by reference", msg + ) + self.assertNotIn("referenced from the Hugging Face Hub", msg) + + # Meta records the right loader. + self.assertEqual( + meta[0]["payload"]["loader_module"], loader_module + ) + + # Round-trip through fnnx.Runtime: predictions match exactly. + rt = Runtime(out) + inputs = { + "a": np.asarray(x["a"].values, dtype=np.float64), + "b": np.asarray(x["b"].values, dtype=np.float64), + "c": np.asarray(x["c"].values, dtype=np.float64), + } + result = rt.compute(inputs, {}) + + self.assertIn("predictions", result) + expected = model.predict(x).tolist() + self.assertEqual( + result["predictions"], + expected, + f"{flavor}: round-trip predictions diverged from native model.predict", + ) + + def test_xgboost_round_trip(self) -> None: + flavor, trainer, _, loader, pkg = _FRAMEWORK_MATRIX[0] + self._run_framework(flavor, trainer, loader, pkg) + + def test_lightgbm_round_trip(self) -> None: + flavor, trainer, _, loader, pkg = _FRAMEWORK_MATRIX[1] + self._run_framework(flavor, trainer, loader, pkg) + + def test_catboost_round_trip(self) -> None: + flavor, trainer, _, loader, pkg = _FRAMEWORK_MATRIX[2] + self._run_framework(flavor, trainer, loader, pkg) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_langchain.py b/src/python/tests/test_mlflow_langchain.py new file mode 100644 index 0000000..b824286 --- /dev/null +++ b/src/python/tests/test_mlflow_langchain.py @@ -0,0 +1,188 @@ +"""End-to-end tests for the MLflow → FNNX converter on LangChain/LangGraph +models-from-code (offline). + +Each test logs a deterministic models-from-code script via +``mlflow.langchain.save_model``, packages with ``package_mlflow_model``, +loads via ``fnnx.Runtime``, and asserts: + +* the embedded ``mlflow_model/`` contains the model-code ``.py`` (the file + models-from-code points ``model_code_path`` at), +* **no** code-availability self-containment warning fires (models-from-code + triggers the self-contained-ish path even for the ``mlflow.langchain`` + loader), +* a round-trip ``compute`` on the converted package matches what the + embedded runnable/graph returns when invoked directly via + ``mlflow.pyfunc.load_model``. + +Both fixtures use an ``Object`` ColSpec so the converter selects +``input_mode="json"`` — the natural shape for GenAI-style I/O. +""" + +from __future__ import annotations + +import os +import tarfile +import tempfile +import unittest +from unittest import mock + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("langchain_core") +pytest.importorskip("langgraph") + + +_FIXTURES_DIR = os.path.join(os.path.dirname(__file__), "_mlflow_fixtures") +_LC_FIXTURE = os.path.join(_FIXTURES_DIR, "langchain_model.py") +_LG_FIXTURE = os.path.join(_FIXTURES_DIR, "langgraph_model.py") + + +def _payload_signature(): + """Object-typed signature shared by both fixtures → json input_mode.""" + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types import DataType # type: ignore[import-not-found] + from mlflow.types.schema import ( # type: ignore[import-not-found] + ColSpec, + Object, + Property, + Schema, + ) + + return ModelSignature( + inputs=Schema( + [ + ColSpec( + name="payload", + type=Object(properties=[Property("text", DataType.string)]), + ) + ] + ), + outputs=Schema([ColSpec(type=DataType.string)]), + ) + + +def _save_langchain_model(tmp: str) -> str: + import mlflow # type: ignore[import-not-found] + + model_dir = os.path.join(tmp, "lc_model") + mlflow.langchain.save_model( # type: ignore[attr-defined] + lc_model=_LC_FIXTURE, + path=model_dir, + signature=_payload_signature(), + ) + return model_dir + + +def _save_langgraph_model(tmp: str) -> str: + import mlflow # type: ignore[import-not-found] + + model_dir = os.path.join(tmp, "lg_model") + mlflow.langchain.save_model( # type: ignore[attr-defined] + lc_model=_LG_FIXTURE, + path=model_dir, + signature=_payload_signature(), + ) + return model_dir + + +def _assert_no_code_availability_warning(warn_mock) -> None: + warned = [c.args[0] for c in warn_mock.call_args_list] + for msg in warned: + assert "serializes Python objects by reference" not in msg, ( + f"unexpected by-reference warning: {msg!r}" + ) + assert "referenced from the Hugging Face Hub" not in msg, ( + f"unexpected Hub-reference warning: {msg!r}" + ) + + +def _assert_model_code_embedded(out_path: str, fixture_basename: str) -> None: + with tarfile.open(out_path, "r") as tar: + names = set(tar.getnames()) + expected = ( + f"variant_artifacts/extra_files/mlflow_model/{fixture_basename}" + ) + assert expected in names, ( + f"expected {expected!r} in package, got {sorted(names)!r}" + ) + + +class TestLangChainModelsFromCode(unittest.TestCase): + """Pure ``RunnableLambda`` round-trips through FNNX.""" + + def test_round_trip(self): + import mlflow # type: ignore[import-not-found] + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_langchain_model(tmp) + out = os.path.join(tmp, "langchain.fnnx") + + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + _assert_model_code_embedded(out, "langchain_model.py") + _assert_no_code_availability_warning(warn_mock) + + rt = Runtime(out) + payload = {"text": "hello"} + result = rt.compute({"data": {"payload": payload}}, {}) + + # Compare to direct invocation through the embedded model. + direct = mlflow.pyfunc.load_model(model_dir).predict( # type: ignore[attr-defined] + {"payload": payload} + ) + + self.assertIn("predictions", result) + self.assertEqual(result["predictions"], direct) + + +class TestLangGraphModelsFromCode(unittest.TestCase): + """Trivial ``StateGraph`` round-trips through FNNX.""" + + def test_round_trip(self): + import mlflow # type: ignore[import-not-found] + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_langgraph_model(tmp) + out = os.path.join(tmp, "langgraph.fnnx") + + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + _assert_model_code_embedded(out, "langgraph_model.py") + _assert_no_code_availability_warning(warn_mock) + + # Signature is an Object ColSpec → input_mode="json". + import json + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + self.assertEqual( + variant_config["extra_values"]["fnnx_mlflow"]["input_mode"], + "json", + ) + + rt = Runtime(out) + payload = {"text": "hello"} + result = rt.compute({"data": {"payload": payload}}, {}) + + direct = mlflow.pyfunc.load_model(model_dir).predict( # type: ignore[attr-defined] + {"payload": payload} + ) + + self.assertIn("predictions", result) + self.assertEqual(result["predictions"], direct) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_mapping.py b/src/python/tests/test_mlflow_mapping.py new file mode 100644 index 0000000..11b676b --- /dev/null +++ b/src/python/tests/test_mlflow_mapping.py @@ -0,0 +1,687 @@ +"""Unit tests for the MLflow signature / env / meta / warning helpers. + +The mapping helpers (`_map_input_schema`, `_map_params`, `_map_env`, +`_self_containment_warnings`, `_build_meta_payload`, `create_meta_callback`) +consume the same normalized JSON entries that MLflow serializes into the +`MLmodel` file, so the bulk of these tests build those entries directly and +do not require an mlflow install. The reader equivalence test gates on +mlflow via `pytest.importorskip`. +""" + +from __future__ import annotations + +import json +import os +import tarfile +import tempfile +import unittest +from textwrap import dedent + +import pytest + +from fnnx.extras.builder import File +from fnnx.extras.pydantic_models.manifest import NDJSON, JSON, Var +from fnnx.extras.mlflow import ( + MLModelInfo, + _build_meta_payload, + _load_model_info_yaml, + _map_env, + _map_input_schema, + _map_output_schema, + _map_params, + _normalize_shape, + _parse_requirements_txt, + _parse_signature_field, + _self_containment_warnings, + create_meta_callback, +) + + +# --------------------------------------------------------------------------- +# _map_input_schema +# --------------------------------------------------------------------------- + + +class TestMapInputSchemaPassthrough(unittest.TestCase): + def test_no_signature_yields_passthrough(self): + specs, mode, cfg, warnings = _map_input_schema(None) + + self.assertEqual(mode, "passthrough") + self.assertEqual(cfg, {"input_mode": "passthrough"}) + self.assertEqual(len(specs), 1) + spec = specs[0] + self.assertIsInstance(spec, JSON) + self.assertEqual(spec.name, "data") + self.assertEqual(spec.dtype, "ext::mlflow::input") + self.assertTrue(warnings, "expected a warning about missing signature") + self.assertIn("passthrough", warnings[0]) + + +class TestMapInputSchemaColumns(unittest.TestCase): + def test_named_double_columns(self): + entries = [ + {"type": "double", "name": "sepal_len"}, + {"type": "double", "name": "sepal_wid"}, + {"type": "double", "name": "petal_len"}, + {"type": "double", "name": "petal_wid"}, + ] + specs, mode, cfg, warnings = _map_input_schema(entries) + + self.assertEqual(mode, "columns") + self.assertEqual(warnings, []) + self.assertEqual( + cfg, + { + "input_mode": "columns", + "column_order": [ + "sepal_len", + "sepal_wid", + "petal_len", + "petal_wid", + ], + }, + ) + self.assertEqual(len(specs), 4) + for spec in specs: + assert isinstance(spec, NDJSON) + self.assertEqual(spec.dtype, "Array[float64]") + self.assertEqual(spec.shape, ["batch"]) + + def test_columns_unnamed_get_col_index_names(self): + entries = [{"type": "long"}, {"type": "long"}] + specs, mode, cfg, _ = _map_input_schema(entries) + + self.assertEqual(mode, "columns") + self.assertEqual(cfg["column_order"], ["col_0", "col_1"]) + for spec in specs: + assert isinstance(spec, NDJSON) + self.assertEqual(spec.dtype, "Array[int64]") + + def test_columns_dtype_table_covers_all_simple_scalars(self): + cases = { + "boolean": "bool", + "integer": "int32", + "long": "int64", + "float": "float32", + "double": "float64", + "string": "string", + } + for mlflow_type, token in cases.items(): + entries = [{"type": mlflow_type, "name": "x"}] + specs, mode, _, _ = _map_input_schema(entries) + assert mode == "columns" + assert isinstance(specs[0], NDJSON) + self.assertEqual( + specs[0].dtype, + f"Array[{token}]", + msg=f"{mlflow_type} should map to Array[{token}]", + ) + + +class TestNormalizeShape(unittest.TestCase): + """Variable dims must become named strings, never the literal int -1. + + FNNX's shape validator matches int dims exactly and treats string dims as + wildcards, so a -1 would mean "must equal -1" and reject every real input. + """ + + def test_variable_dims_become_strings(self): + self.assertEqual(_normalize_shape([-1, 4]), ["batch", 4]) + self.assertEqual(_normalize_shape([None, 3]), ["batch", 3]) + self.assertEqual(_normalize_shape([-1]), ["batch"]) + self.assertEqual(_normalize_shape(None), ["batch"]) + + def test_inner_variable_dims_named_by_index(self): + # leading variable dim -> "batch"; inner variable dims -> "dim_" + self.assertEqual(_normalize_shape([-1, -1, 8]), ["batch", "dim_1", 8]) + self.assertEqual(_normalize_shape([5, None]), [5, "dim_1"]) + + def test_no_literal_minus_one_emitted(self): + for shape in ([-1, 4], [None, 3], [-1], [-1, -1, 8], [5, None]): + self.assertNotIn(-1, _normalize_shape(shape)) + + def test_static_dims_preserved_as_ints(self): + self.assertEqual(_normalize_shape([2, 3, 4]), [2, 3, 4]) + + +class TestMapInputSchemaTensor(unittest.TestCase): + def test_single_unnamed_tensor(self): + entries = [ + { + "type": "tensor", + "tensor-spec": {"dtype": "float32", "shape": [-1, 4]}, + } + ] + specs, mode, cfg, warnings = _map_input_schema(entries) + + self.assertEqual(mode, "tensor") + self.assertEqual(warnings, []) + self.assertEqual(cfg["input_mode"], "tensor") + self.assertEqual(cfg["tensor_names"], ["__single__"]) + self.assertEqual(len(specs), 1) + assert isinstance(specs[0], NDJSON) + self.assertEqual(specs[0].name, "input") + self.assertEqual(specs[0].dtype, "Array[float32]") + self.assertEqual(specs[0].shape, ["batch", 4]) + + def test_multiple_named_tensors(self): + entries = [ + { + "type": "tensor", + "name": "a", + "tensor-spec": {"dtype": "float32", "shape": [None, 3]}, + }, + { + "type": "tensor", + "name": "b", + "tensor-spec": {"dtype": "int64", "shape": [-1]}, + }, + ] + specs, mode, cfg, _ = _map_input_schema(entries) + + self.assertEqual(mode, "tensor") + self.assertEqual(cfg["tensor_names"], ["a", "b"]) + self.assertEqual(len(specs), 2) + names = [s.name for s in specs] + self.assertEqual(names, ["a", "b"]) + assert isinstance(specs[0], NDJSON) + self.assertEqual(specs[0].dtype, "Array[float32]") + self.assertEqual(specs[0].shape, ["batch", 3]) + assert isinstance(specs[1], NDJSON) + self.assertEqual(specs[1].dtype, "Array[int64]") + self.assertEqual(specs[1].shape, ["batch"]) + + +class TestMapInputSchemaJson(unittest.TestCase): + def test_nested_object_forces_json(self): + entries = [ + { + "name": "messages", + "type": "array", + "items": {"type": "object", "properties": {}}, + } + ] + specs, mode, cfg, _ = _map_input_schema(entries) + + self.assertEqual(mode, "json") + self.assertEqual(cfg, {"input_mode": "json"}) + self.assertEqual(len(specs), 1) + self.assertIsInstance(specs[0], JSON) + self.assertEqual(specs[0].name, "data") + self.assertEqual(specs[0].dtype, "ext::mlflow::input") + + def test_datetime_column_forces_json(self): + entries = [ + {"type": "double", "name": "y"}, + {"type": "datetime", "name": "ds"}, + ] + _, mode, _, _ = _map_input_schema(entries) + self.assertEqual(mode, "json") + + def test_binary_column_forces_json(self): + entries = [ + {"type": "double", "name": "x"}, + {"type": "binary", "name": "blob"}, + ] + _, mode, _, _ = _map_input_schema(entries) + self.assertEqual(mode, "json") + + def test_mixed_tensor_and_scalar_is_not_tensor_mode(self): + entries = [ + { + "type": "tensor", + "tensor-spec": {"dtype": "float32", "shape": [-1]}, + }, + {"type": "double", "name": "y"}, + ] + # Mixed tensor + scalar: scalar entries cannot be treated as + # tensor; loop falls through to json (the most permissive option). + _, mode, _, _ = _map_input_schema(entries) + self.assertEqual(mode, "json") + + +class TestMapInputSchemaEmpty(unittest.TestCase): + def test_empty_signature_inputs_list_raises(self): + with self.assertRaises(ValueError): + _map_input_schema([]) + + +# --------------------------------------------------------------------------- +# _map_output_schema +# --------------------------------------------------------------------------- + + +class TestMapOutputSchema(unittest.TestCase): + def test_outputs_always_single_permissive_predictions(self): + specs, warnings = _map_output_schema() + self.assertEqual(warnings, []) + self.assertEqual(len(specs), 1) + out = specs[0] + self.assertIsInstance(out, JSON) + self.assertEqual(out.name, "predictions") + self.assertEqual(out.dtype, "ext::mlflow::output") + + +# --------------------------------------------------------------------------- +# _map_params +# --------------------------------------------------------------------------- + + +class TestMapParams(unittest.TestCase): + def test_none_yields_empty(self): + vars_, names = _map_params(None) + self.assertEqual(vars_, []) + self.assertEqual(names, []) + + def test_empty_yields_empty(self): + vars_, names = _map_params([]) + self.assertEqual(vars_, []) + self.assertEqual(names, []) + + def test_params_become_dynamic_attributes(self): + entries = [ + {"name": "temperature", "type": "double", "default": 0.7, "shape": None}, + {"name": "max_tokens", "type": "long", "default": 256, "shape": None}, + ] + vars_, names = _map_params(entries) + self.assertEqual(names, ["temperature", "max_tokens"]) + self.assertEqual(len(vars_), 2) + for v in vars_: + self.assertIsInstance(v, Var) + self.assertIn("temperature", vars_[0].description) + self.assertIn("type=double", vars_[0].description) + self.assertIn("default=0.7", vars_[0].description) + + def test_param_without_name_raises(self): + with self.assertRaises(ValueError): + _map_params([{"type": "double", "default": 0.0}]) + + +# --------------------------------------------------------------------------- +# _parse_signature_field +# --------------------------------------------------------------------------- + + +class TestParseSignatureField(unittest.TestCase): + def test_none_returns_none(self): + self.assertIsNone(_parse_signature_field(None)) + + def test_json_string_parsed(self): + s = '[{"type": "double", "name": "x"}]' + out = _parse_signature_field(s) + self.assertEqual(out, [{"type": "double", "name": "x"}]) + + def test_already_decoded_list_passthrough(self): + v = [{"type": "long", "name": "k"}] + self.assertEqual(_parse_signature_field(v), v) + + def test_non_list_raises(self): + with self.assertRaises(ValueError): + _parse_signature_field('{"type": "double"}') + + +# --------------------------------------------------------------------------- +# _map_env +# --------------------------------------------------------------------------- + + +class TestMapEnv(unittest.TestCase): + def _write(self, tmp: str, name: str, body: str) -> str: + path = os.path.join(tmp, name) + with open(path, "w") as f: + f.write(body) + return path + + def test_reads_python_env_and_requirements(self): + with tempfile.TemporaryDirectory() as tmp: + self._write( + tmp, + "python_env.yaml", + dedent( + """ + python: 3.9.8 + build_dependencies: + - pip==21.3.1 + - setuptools==58.0.4 + - wheel==0.37.0 + dependencies: + - -r requirements.txt + """ + ).strip(), + ) + self._write( + tmp, + "requirements.txt", + dedent( + """ + # auto-generated by mlflow + mlflow==3.4.0 + scikit-learn==1.4.0 + cloudpickle==3.0.0 + -r extra.txt + --index-url https://example.com/simple + """ + ).strip(), + ) + + py, build, runtime = _map_env(tmp, flavors=None) + + self.assertEqual(py, "3.9.8") + self.assertEqual( + build, + ["pip==21.3.1", "setuptools==58.0.4", "wheel==0.37.0"], + ) + self.assertEqual( + runtime, + ["mlflow==3.4.0", "scikit-learn==1.4.0", "cloudpickle==3.0.0"], + ) + + def test_falls_back_to_pyfunc_python_version_without_env_yaml(self): + with tempfile.TemporaryDirectory() as tmp: + self._write(tmp, "requirements.txt", "mlflow==3.4.0\n") + flavors = {"python_function": {"python_version": "3.11.4"}} + py, build, runtime = _map_env(tmp, flavors=flavors) + self.assertEqual(py, "3.11.4") + self.assertEqual(build, []) + self.assertEqual(runtime, ["mlflow==3.4.0"]) + + def test_no_env_yaml_no_flavor_returns_none(self): + with tempfile.TemporaryDirectory() as tmp: + py, build, runtime = _map_env(tmp, flavors={}) + self.assertIsNone(py) + self.assertEqual(build, []) + self.assertEqual(runtime, []) + + +class TestParseRequirementsTxt(unittest.TestCase): + def test_skips_comments_blank_and_options(self): + with tempfile.TemporaryDirectory() as tmp: + path = os.path.join(tmp, "r.txt") + with open(path, "w") as f: + f.write( + "\n# comment\nmlflow==3.4.0\n \n" + "-r other.txt\n-c constraints.txt\n--pre\n" + "scikit-learn==1.4.0\n" + ) + deps = _parse_requirements_txt(path) + self.assertEqual(deps, ["mlflow==3.4.0", "scikit-learn==1.4.0"]) + + +# --------------------------------------------------------------------------- +# _self_containment_warnings +# --------------------------------------------------------------------------- + + +def _info_with_flavors(flavors: dict) -> MLModelInfo: + return MLModelInfo( + flavors=flavors, + signature_inputs=None, + signature_outputs=None, + signature_params=None, + metadata=None, + mlflow_version=None, + saved_input_example_info=None, + raw_mlmodel={}, + ) + + +class TestSelfContainmentWarnings(unittest.TestCase): + def test_no_warning_when_code_dir_present(self): + with tempfile.TemporaryDirectory() as tmp: + os.makedirs(os.path.join(tmp, "code")) + info = _info_with_flavors( + {"python_function": {"loader_module": "mlflow.sklearn"}} + ) + self.assertEqual(_self_containment_warnings(info, tmp), []) + + def test_no_warning_when_models_from_code(self): + with tempfile.TemporaryDirectory() as tmp: + info = _info_with_flavors( + { + "python_function": { + "loader_module": "mlflow.langchain", + "model_code_path": "langchain_model.py", + } + } + ) + self.assertEqual(_self_containment_warnings(info, tmp), []) + + def test_no_warning_for_native_flavors(self): + with tempfile.TemporaryDirectory() as tmp: + for loader in ("mlflow.onnx", "mlflow.xgboost", "mlflow.lightgbm"): + info = _info_with_flavors( + {"python_function": {"loader_module": loader}} + ) + self.assertEqual( + _self_containment_warnings(info, tmp), [], + msg=f"{loader} should emit no warning", + ) + + def test_pickle_loader_without_code_dir_warns(self): + with tempfile.TemporaryDirectory() as tmp: + for loader in ("mlflow.pytorch", "mlflow.sklearn", "mlflow.pyfunc.model"): + info = _info_with_flavors( + {"python_function": {"loader_module": loader}} + ) + warnings = _self_containment_warnings(info, tmp) + self.assertTrue( + warnings, msg=f"{loader} should emit a warning" + ) + self.assertIn(loader, warnings[0]) + self.assertIn("code_paths", warnings[0]) + + def test_transformers_save_pretrained_false_warns(self): + with tempfile.TemporaryDirectory() as tmp: + info = _info_with_flavors( + { + "python_function": {"loader_module": "mlflow.transformers"}, + "transformers": {"save_pretrained": False}, + } + ) + warnings = _self_containment_warnings(info, tmp) + self.assertEqual(len(warnings), 1) + self.assertIn("Hugging Face Hub", warnings[0]) + + def test_transformers_save_pretrained_true_no_warn(self): + with tempfile.TemporaryDirectory() as tmp: + info = _info_with_flavors( + { + "python_function": {"loader_module": "mlflow.transformers"}, + "transformers": {"save_pretrained": True}, + } + ) + self.assertEqual(_self_containment_warnings(info, tmp), []) + + +# --------------------------------------------------------------------------- +# _build_meta_payload + create_meta_callback +# --------------------------------------------------------------------------- + + +class TestMetaPayload(unittest.TestCase): + def _sample_info(self) -> MLModelInfo: + return MLModelInfo( + flavors={ + "python_function": {"loader_module": "mlflow.sklearn"}, + "sklearn": {"sklearn_version": "1.4.0"}, + }, + signature_inputs=[{"type": "double", "name": "x"}], + signature_outputs=[{"type": "double"}], + signature_params=None, + metadata={"foo": "bar"}, + mlflow_version="3.4.0", + saved_input_example_info=None, + raw_mlmodel={"flavors": {"sklearn": {}}, "mlflow_version": "3.4.0"}, + ) + + def test_payload_contains_provenance_fields(self): + info = self._sample_info() + payload = _build_meta_payload(info, input_mode="columns") + self.assertEqual(payload["loader_module"], "mlflow.sklearn") + self.assertEqual(payload["mlflow_version"], "3.4.0") + self.assertEqual(payload["input_mode"], "columns") + self.assertEqual( + set(payload["flavors"]), {"python_function", "sklearn"} + ) + self.assertEqual(payload["mlmodel"], info.raw_mlmodel) + self.assertEqual( + payload["signature"]["inputs"], info.signature_inputs + ) + + def test_create_meta_callback_writes_one_entry(self): + info = self._sample_info() + cb = create_meta_callback(info, "columns", producer_version="0.0.99") + + with tempfile.TemporaryDirectory() as tmp: + path = os.path.join(tmp, "out.tar") + f = File(path) + cb(f) + f.close() + + with tarfile.open(path, "r") as tar: + member = tar.getmember("meta.json") + stream = tar.extractfile(member) + assert stream is not None + entries = json.loads(stream.read().decode()) + + self.assertEqual(len(entries), 1) + entry = entries[0] + self.assertEqual(entry["id"], "mlflow-source") + self.assertEqual(entry["producer"], "fnnx.ai") + self.assertEqual(entry["producer_version"], "0.0.99") + # No auto-added tags; flavor/version live in the payload instead. + self.assertEqual(entry["producer_tags"], []) + self.assertIn("sklearn", entry["payload"]["flavors"]) + self.assertEqual(entry["payload"]["mlflow_version"], "3.4.0") + self.assertEqual(entry["payload"]["input_mode"], "columns") + self.assertEqual(entry["payload"]["loader_module"], "mlflow.sklearn") + + +# --------------------------------------------------------------------------- +# _load_model_info_yaml +# --------------------------------------------------------------------------- + + +_SAMPLE_MLMODEL = dedent( + """ + artifact_path: model + flavors: + python_function: + env: conda.yaml + loader_module: mlflow.sklearn + python_version: 3.10.4 + sklearn: + pickled_model: model.pkl + sklearn_version: 1.4.0 + mlflow_version: 3.4.0 + metadata: + task: classification + saved_input_example_info: + artifact_path: input_example.json + type: dataframe + signature: + inputs: '[{"type": "double", "name": "x"}, {"type": "double", "name": "y"}]' + outputs: '[{"type": "long"}]' + params: '[{"name": "temperature", "type": "double", "default": 0.7, "shape": null}]' + """ +).strip() + + +class TestLoadModelInfoYaml(unittest.TestCase): + def test_parses_hand_authored_mlmodel(self): + with tempfile.TemporaryDirectory() as tmp: + with open(os.path.join(tmp, "MLmodel"), "w") as f: + f.write(_SAMPLE_MLMODEL) + info = _load_model_info_yaml(tmp) + + self.assertEqual(info.mlflow_version, "3.4.0") + self.assertEqual(set(info.flavors.keys()), {"python_function", "sklearn"}) + self.assertEqual(info.loader_module, "mlflow.sklearn") + self.assertEqual(info.primary_flavor, "sklearn") + self.assertEqual(info.metadata, {"task": "classification"}) + self.assertEqual( + info.signature_inputs, + [ + {"type": "double", "name": "x"}, + {"type": "double", "name": "y"}, + ], + ) + self.assertEqual(info.signature_outputs, [{"type": "long"}]) + assert info.signature_params is not None + self.assertEqual(info.signature_params[0]["name"], "temperature") + self.assertEqual( + info.saved_input_example_info, + {"artifact_path": "input_example.json", "type": "dataframe"}, + ) + + def test_missing_mlmodel_file_raises(self): + with tempfile.TemporaryDirectory() as tmp: + with self.assertRaises(FileNotFoundError): + _load_model_info_yaml(tmp) + + def test_missing_signature_is_none(self): + body = dedent( + """ + flavors: + python_function: + loader_module: mlflow.pyfunc.model + mlflow_version: 3.4.0 + """ + ).strip() + with tempfile.TemporaryDirectory() as tmp: + with open(os.path.join(tmp, "MLmodel"), "w") as f: + f.write(body) + info = _load_model_info_yaml(tmp) + self.assertIsNone(info.signature_inputs) + self.assertIsNone(info.signature_outputs) + self.assertIsNone(info.signature_params) + + +# --------------------------------------------------------------------------- +# Reader equivalence (mlflow vs YAML) — only runs with mlflow installed. +# --------------------------------------------------------------------------- + + +class TestReaderEquivalence(unittest.TestCase): + def test_mlflow_and_yaml_readers_agree(self): + mlflow = pytest.importorskip("mlflow") + pytest.importorskip("sklearn") + pytest.importorskip("pandas") + + from sklearn.ensemble import RandomForestClassifier # type: ignore[import-not-found] + import pandas as pd + from mlflow.models.signature import infer_signature # type: ignore[import-not-found] + + from fnnx.extras.mlflow import ( + _load_model_info_mlflow, + _load_model_info_yaml, + ) + + # Tiny deterministic model + signature. + x = pd.DataFrame( + {"a": [0.0, 1.0, 2.0, 3.0], "b": [3.0, 2.0, 1.0, 0.0]} + ) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=2, random_state=0) + model.fit(x, y) + sig = infer_signature(x, model.predict(x)) + + with tempfile.TemporaryDirectory() as tmp: + model_dir = os.path.join(tmp, "m") + mlflow.sklearn.save_model( # type: ignore[attr-defined] + model, model_dir, signature=sig + ) + info_mlflow = _load_model_info_mlflow(model_dir) + info_yaml = _load_model_info_yaml(model_dir) + + self.assertEqual(info_mlflow.signature_inputs, info_yaml.signature_inputs) + self.assertEqual( + info_mlflow.signature_outputs, info_yaml.signature_outputs + ) + self.assertEqual(info_mlflow.signature_params, info_yaml.signature_params) + self.assertEqual(info_mlflow.flavors, info_yaml.flavors) + self.assertEqual(info_mlflow.mlflow_version, info_yaml.mlflow_version) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_prophet.py b/src/python/tests/test_mlflow_prophet.py new file mode 100644 index 0000000..e01c21e --- /dev/null +++ b/src/python/tests/test_mlflow_prophet.py @@ -0,0 +1,136 @@ +"""End-to-end test for the MLflow → FNNX converter on a Prophet model. + +Exercises the datetime-column code path: ``ds`` is an MLflow ``datetime`` +ColSpec, which falls outside the FNNX ``Array[...]`` token table and therefore +forces ``input_mode="json"`` (single permissive ``data`` input). The original +MLflow signature stays preserved in ``meta.json``, and a round-trip forecast +through ``fnnx.Runtime`` reproduces the model's own ``predict`` output for the +same future dataframe. +""" + +from __future__ import annotations + +import json +import os +import tarfile +import tempfile +import unittest +import warnings + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("prophet") +pytest.importorskip("pandas") + + +def _train_tiny_prophet(): + """Return ``(model, future_df)`` — deterministic linear trend, no seasonality.""" + import numpy as np + import pandas as pd + from prophet import Prophet # type: ignore[import-not-found] + + dates = pd.date_range("2024-01-01", periods=20, freq="D") + history = pd.DataFrame({"ds": dates, "y": np.linspace(1.0, 5.0, 20)}) + + model = Prophet( + daily_seasonality=False, + weekly_seasonality=False, + yearly_seasonality=False, + uncertainty_samples=False, + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + model.fit(history) + + future = pd.DataFrame( + {"ds": pd.date_range("2024-01-21", periods=5, freq="D")} + ) + return model, future + + +def _save_prophet_model(tmp: str): + """Save the trained Prophet model with an inferred signature.""" + import mlflow # type: ignore[import-not-found] + from mlflow.models.signature import infer_signature # type: ignore[import-not-found] + + model, future = _train_tiny_prophet() + forecast = model.predict(future) + sig = infer_signature(future, forecast) + + model_dir = os.path.join(tmp, "prophet_model") + mlflow.prophet.save_model(model, model_dir, signature=sig) # type: ignore[attr-defined] + return model_dir, model, future + + +class TestProphetDatetimeJsonMode(unittest.TestCase): + """Datetime column → json input_mode; forecast round-trips losslessly.""" + + def test_round_trip_and_input_mode(self): + import numpy as np + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, future = _save_prophet_model(tmp) + out = os.path.join(tmp, "prophet.fnnx") + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + meta = json.loads( + tar.extractfile("meta.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual( + cfg["input_mode"], + "json", + f"expected json mode for datetime column, got {cfg['input_mode']!r}", + ) + + self.assertEqual(len(manifest["inputs"]), 1) + self.assertEqual(manifest["inputs"][0]["name"], "data") + self.assertEqual(manifest["inputs"][0]["content_type"], "JSON") + self.assertEqual( + manifest["inputs"][0]["dtype"], "ext::mlflow::input" + ) + + # Original MLflow signature (datetime column) preserved verbatim. + sig_inputs = meta[0]["payload"]["signature"]["inputs"] + self.assertEqual(len(sig_inputs), 1) + self.assertEqual(sig_inputs[0]["type"], "datetime") + self.assertEqual(sig_inputs[0]["name"], "ds") + + # Meta records the prophet loader for provenance. + self.assertEqual( + meta[0]["payload"]["loader_module"], "mlflow.prophet" + ) + + rt = Runtime(out) + # json mode: ``inputs["data"]`` is forwarded straight to predict(). + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + result = rt.compute({"data": future}, {}) + expected = model.predict(future) + + self.assertIn("predictions", result) + + # Predictions normalized to list-of-records via _to_jsonable(DataFrame). + actual_yhat = np.asarray([rec["yhat"] for rec in result["predictions"]]) + expected_yhat = expected["yhat"].to_numpy() + self.assertTrue( + np.allclose(actual_yhat, expected_yhat, atol=1e-8), + f"yhat diverged: expected={expected_yhat!r}, actual={actual_yhat!r}", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_tensorflow.py b/src/python/tests/test_mlflow_tensorflow.py new file mode 100644 index 0000000..8cb6694 --- /dev/null +++ b/src/python/tests/test_mlflow_tensorflow.py @@ -0,0 +1,130 @@ +"""End-to-end test for the MLflow → FNNX converter on a Keras functional model +with two named inputs. + +Exercises the named-tensor branch of ``_map_input_schema``: when the signature +carries multiple TensorSpecs with explicit names, ``input_mode="tensor"`` is +selected with ``tensor_names=["a", "b"]``, the manifest has one ``NDJSON`` +input per named tensor, and the wrapper assembles a dict-of-arrays payload for +``predict``. +""" + +from __future__ import annotations + +import json +import os +import tarfile +import tempfile +import unittest + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("tensorflow") + + +N_FEATURES_A = 3 +N_FEATURES_B = 2 + + +def _build_tiny_keras_model(): + """Build a deterministic two-input Keras functional model and a sample batch.""" + import numpy as np + import tensorflow as tf # type: ignore[import-not-found] + + tf.random.set_seed(0) + + input_a = tf.keras.Input(shape=(N_FEATURES_A,), name="a", dtype=tf.float32) + input_b = tf.keras.Input(shape=(N_FEATURES_B,), name="b", dtype=tf.float32) + concat = tf.keras.layers.Concatenate()([input_a, input_b]) + output = tf.keras.layers.Dense(2, activation="linear", name="out")(concat) + model = tf.keras.Model(inputs={"a": input_a, "b": input_b}, outputs=output) + + rng = np.random.default_rng(0) + sample_a = rng.random((4, N_FEATURES_A)).astype(np.float32) + sample_b = rng.random((4, N_FEATURES_B)).astype(np.float32) + return model, sample_a, sample_b + + +def _save_keras_model(tmp: str): + """Save the Keras model with a two-named TensorSpec signature.""" + import mlflow # type: ignore[import-not-found] + import numpy as np + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types.schema import Schema, TensorSpec # type: ignore[import-not-found] + + model, sample_a, sample_b = _build_tiny_keras_model() + signature = ModelSignature( + inputs=Schema( + [ + TensorSpec(np.dtype("float32"), [-1, N_FEATURES_A], name="a"), + TensorSpec(np.dtype("float32"), [-1, N_FEATURES_B], name="b"), + ] + ), + outputs=Schema([TensorSpec(np.dtype("float32"), [-1, 2])]), + ) + + model_dir = os.path.join(tmp, "keras_model") + mlflow.tensorflow.save_model(model, model_dir, signature=signature) # type: ignore[attr-defined] + return model_dir, model, sample_a, sample_b + + +class TestKerasNamedTensorRoundTrip(unittest.TestCase): + """Named multi-input Keras model round-trips through FNNX losslessly.""" + + def test_round_trip_and_manifest(self): + import numpy as np + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, sample_a, sample_b = _save_keras_model(tmp) + out = os.path.join(tmp, "keras.fnnx") + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual( + cfg["input_mode"], + "tensor", + f"expected tensor mode, got {cfg['input_mode']!r}", + ) + self.assertEqual(cfg["tensor_names"], ["a", "b"]) + + # Manifest: one NDJSON input per named tensor. + self.assertEqual(len(manifest["inputs"]), 2) + specs_by_name = {s["name"]: s for s in manifest["inputs"]} + self.assertEqual(set(specs_by_name), {"a", "b"}) + + spec_a = specs_by_name["a"] + self.assertEqual(spec_a["content_type"], "NDJSON") + self.assertEqual(spec_a["dtype"], "Array[float32]") + self.assertEqual(spec_a["shape"], ["batch", N_FEATURES_A]) + + spec_b = specs_by_name["b"] + self.assertEqual(spec_b["content_type"], "NDJSON") + self.assertEqual(spec_b["dtype"], "Array[float32]") + self.assertEqual(spec_b["shape"], ["batch", N_FEATURES_B]) + + rt = Runtime(out) + result = rt.compute({"a": sample_a, "b": sample_b}, {}) + + self.assertIn("predictions", result) + expected = model.predict({"a": sample_a, "b": sample_b}, verbose=0) # type: ignore[arg-type] + actual = np.asarray(result["predictions"], dtype=np.float32) + self.assertTrue( + np.allclose(actual, expected, atol=1e-5), + f"predictions diverged: expected={expected!r}, actual={actual!r}", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_torch.py b/src/python/tests/test_mlflow_torch.py new file mode 100644 index 0000000..2d1c8e8 --- /dev/null +++ b/src/python/tests/test_mlflow_torch.py @@ -0,0 +1,165 @@ +"""End-to-end tests for the MLflow → FNNX converter on a PyTorch ``nn.Module``. + +Covers the two self-containment paths for pickle-based pytorch models whose +class lives outside ``__main__``: + +* Saved with ``code_paths=[]`` → the class source is embedded under + ``mlflow_model/code/`` and the wrapper can re-import it at load time. The + round-trip predictions match the source model and no code-availability + warning is emitted. +* Saved without ``code_paths`` → the build still succeeds, no ``code/`` + directory is embedded, and a self-containment warning is emitted naming + the by-reference pickle flavor. +""" + +from __future__ import annotations + +import os +import sys +import tarfile +import tempfile +import unittest +from unittest import mock + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("torch") + + +# Make the fixture importable as ``torch_net`` (mlflow.pytorch.save_model +# pickles the model by reference, so the class's module must be importable at +# save time; ``code_paths`` then re-exposes it at load time). +_FIXTURES_DIR = os.path.join(os.path.dirname(__file__), "_mlflow_fixtures") +if _FIXTURES_DIR not in sys.path: + sys.path.insert(0, _FIXTURES_DIR) + + +def _build_tiny_net(): + """Build a fixed-weight TinyNet plus a deterministic input batch.""" + import numpy as np + import torch # type: ignore[import-not-found] + + from torch_net import N_FEATURES, TinyNet # type: ignore[import-not-found] + + torch.manual_seed(0) + model = TinyNet() + model.eval() + + rng = np.random.default_rng(0) + sample = rng.random((5, N_FEATURES), dtype=np.float64).astype(np.float32) + return model, sample, N_FEATURES + + +def _native_forward(model, sample): + import torch # type: ignore[import-not-found] + + with torch.no_grad(): + return model(torch.from_numpy(sample)).numpy() + + +def _save_pytorch_model(tmp: str, *, with_code_paths: bool): + """Save a tiny pytorch model with a TensorSpec signature.""" + import mlflow # type: ignore[import-not-found] + import numpy as np + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types.schema import Schema, TensorSpec # type: ignore[import-not-found] + + model, sample, n_features = _build_tiny_net() + signature = ModelSignature( + inputs=Schema([TensorSpec(np.dtype("float32"), [-1, n_features])]), + outputs=Schema([TensorSpec(np.dtype("float32"), [-1, 2])]), + ) + + model_dir = os.path.join(tmp, "torch_model") + # mlflow.pytorch always serializes via torch.save (pickle, by-reference), + # which is exactly what exercises the by-reference self-containment + # heuristic. (There is no `serialization_format` arg on this flavor — it + # belongs to mlflow.sklearn — and passing it raises in torch.save.) + kwargs: dict = {"signature": signature} + if with_code_paths: + kwargs["code_paths"] = [os.path.join(_FIXTURES_DIR, "torch_net.py")] + mlflow.pytorch.save_model(model, model_dir, **kwargs) # type: ignore[attr-defined] + return model_dir, model, sample + + +class TestPyTorchWithCodePaths(unittest.TestCase): + """code_paths embeds the class source → round-trip works, no warning.""" + + def test_round_trip_and_no_warning(self): + import numpy as np + + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, sample = _save_pytorch_model(tmp, with_code_paths=True) + out = os.path.join(tmp, "torch.fnnx") + + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + # ``code/`` directory was embedded verbatim under mlflow_model. + with tarfile.open(out, "r") as tar: + names = set(tar.getnames()) + self.assertTrue( + any( + n.startswith("variant_artifacts/extra_files/mlflow_model/code/") + and n.endswith("torch_net.py") + for n in names + ), + f"expected mlflow_model/code/torch_net.py in package, got {sorted(names)!r}", + ) + + # No "by reference" / Hub-reference self-containment warning fires. + warned = [c.args[0] for c in warn_mock.call_args_list] + for msg in warned: + self.assertNotIn("serializes Python objects by reference", msg) + self.assertNotIn("referenced from the Hugging Face Hub", msg) + + rt = Runtime(out) + result = rt.compute({"input": sample}, {}) + + self.assertIn("predictions", result) + expected = _native_forward(model, sample) + actual = np.asarray(result["predictions"], dtype=np.float32) + self.assertTrue( + np.allclose(actual, expected, atol=1e-5), + f"predictions diverged: expected={expected!r}, actual={actual!r}", + ) + + +class TestPyTorchWithoutCodePaths(unittest.TestCase): + """Without code_paths, the build succeeds but a warning is emitted.""" + + def test_self_containment_warning(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir, _, _ = _save_pytorch_model(tmp, with_code_paths=False) + out = os.path.join(tmp, "torch_no_code.fnnx") + + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + # No ``code/`` subdir was embedded. + with tarfile.open(out, "r") as tar: + names = set(tar.getnames()) + self.assertFalse( + any( + n.startswith("variant_artifacts/extra_files/mlflow_model/code/") + for n in names + ), + f"unexpected code/ subdir in package: {sorted(names)!r}", + ) + + warned = [c.args[0] for c in warn_mock.call_args_list] + self.assertTrue( + any("serializes Python objects by reference" in msg for msg in warned), + f"expected by-reference pickle warning, got {warned!r}", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_variants.py b/src/python/tests/test_mlflow_variants.py new file mode 100644 index 0000000..e7bc35e --- /dev/null +++ b/src/python/tests/test_mlflow_variants.py @@ -0,0 +1,346 @@ +"""Broader end-to-end coverage for ``package_mlflow_model``. + +Each test trains/saves a tiny deterministic MLflow model exercising a specific +input mode (json/custom dict output, tensor, passthrough, params), packages it +with the converter, loads via ``fnnx.Runtime``, and asserts a round-trip +behavior matching direct invocation through MLflow. + +Models-from-code is used for the custom-PythonModel scenarios (json / params) +because the class must be loadable in a fresh process at warmup time without +relying on test-module import paths. +""" + +from __future__ import annotations + +import json +import os +import tarfile +import tempfile +import textwrap +import unittest +from unittest import mock + +import pytest + + +pytest.importorskip("mlflow") +pytest.importorskip("sklearn") +pytest.importorskip("pandas") + + +# --------------------------------------------------------------------------- +# Custom PythonModel (json input_mode, dict output) +# --------------------------------------------------------------------------- + + +_DICT_MODEL_SCRIPT = textwrap.dedent( + """ + import mlflow + + + class DictModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input, params=None): + # model_input arrives as a list of records (pyfunc serialization). + if hasattr(model_input, "to_dict"): + records = model_input.to_dict(orient="records") + else: + records = model_input + first = records[0] if records else {} + items = first.get("items", []) if isinstance(first, dict) else [] + return {"count": len(items), "items": list(items)} + + + mlflow.models.set_model(DictModel()) + """ +).lstrip() + + +def _save_dict_model(tmp: str) -> str: + """Save a custom PythonModel (models-from-code) with a nested ColSpec signature.""" + import mlflow # type: ignore[import-not-found] + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types import DataType # type: ignore[import-not-found] + from mlflow.types.schema import Array, ColSpec, Schema # type: ignore[import-not-found] + + script_path = os.path.join(tmp, "dict_model_script.py") + with open(script_path, "w") as f: + f.write(_DICT_MODEL_SCRIPT) + + signature = ModelSignature( + inputs=Schema([ColSpec(name="items", type=Array(DataType.string))]), + outputs=Schema([ColSpec(type=DataType.string)]), + ) + + model_dir = os.path.join(tmp, "dict_model") + mlflow.pyfunc.save_model( # type: ignore[attr-defined] + path=model_dir, + python_model=script_path, + signature=signature, + ) + return model_dir + + +class TestCustomPythonModelJsonMode(unittest.TestCase): + """Nested-ColSpec signature → json input_mode; dict output is normalized.""" + + def test_round_trip(self): + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_dict_model(tmp) + out = os.path.join(tmp, "dict.fnnx") + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual(cfg["input_mode"], "json") + self.assertEqual(len(manifest["inputs"]), 1) + self.assertEqual(manifest["inputs"][0]["name"], "data") + self.assertEqual(manifest["inputs"][0]["content_type"], "JSON") + self.assertEqual(manifest["inputs"][0]["dtype"], "ext::mlflow::input") + + rt = Runtime(out) + records = [{"items": ["a", "b", "c"]}] + result = rt.compute({"data": records}, {}) + + self.assertIn("predictions", result) + predictions = result["predictions"] + self.assertEqual(predictions, {"count": 3, "items": ["a", "b", "c"]}) + + +# --------------------------------------------------------------------------- +# TensorSpec signature → tensor input_mode +# --------------------------------------------------------------------------- + + +def _save_tensor_model(tmp: str): + """Train a tiny regressor and save it with a single unnamed TensorSpec signature.""" + import mlflow # type: ignore[import-not-found] + import numpy as np + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types.schema import Schema, TensorSpec # type: ignore[import-not-found] + from sklearn.ensemble import RandomForestRegressor # type: ignore[import-not-found] + + rng = np.random.default_rng(0) + X = rng.random((20, 4)).astype(np.float32) + y = rng.random(20).astype(np.float64) + model = RandomForestRegressor(n_estimators=3, random_state=0) + model.fit(X, y) + + signature = ModelSignature( + inputs=Schema([TensorSpec(np.dtype("float32"), [-1, 4])]), + outputs=Schema([TensorSpec(np.dtype("float64"), [-1])]), + ) + + model_dir = os.path.join(tmp, "tensor_model") + mlflow.sklearn.save_model(model, model_dir, signature=signature) # type: ignore[attr-defined] + return model_dir, model, X + + +class TestTensorMode(unittest.TestCase): + def test_unnamed_tensor_round_trip(self): + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, X = _save_tensor_model(tmp) + out = os.path.join(tmp, "tensor.fnnx") + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual(cfg["input_mode"], "tensor") + self.assertEqual(cfg["tensor_names"], ["__single__"]) + self.assertEqual(len(manifest["inputs"]), 1) + spec = manifest["inputs"][0] + self.assertEqual(spec["name"], "input") + self.assertEqual(spec["content_type"], "NDJSON") + self.assertEqual(spec["dtype"], "Array[float32]") + self.assertEqual(spec["shape"], ["batch", 4]) + + rt = Runtime(out) + sample = X[:5] + result = rt.compute({"input": sample}, {}) + + self.assertIn("predictions", result) + expected = model.predict(sample).tolist() + self.assertEqual(result["predictions"], expected) + + +# --------------------------------------------------------------------------- +# No-signature model → passthrough input_mode (with warning) +# --------------------------------------------------------------------------- + + +def _save_no_signature_model(tmp: str): + import mlflow # type: ignore[import-not-found] + import pandas as pd + from sklearn.ensemble import RandomForestClassifier # type: ignore[import-not-found] + + x = pd.DataFrame( + { + "a": [0.0, 1.0, 2.0, 3.0], + "b": [3.0, 2.0, 1.0, 0.0], + } + ) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=3, random_state=0) + model.fit(x, y) + + model_dir = os.path.join(tmp, "no_sig_model") + mlflow.sklearn.save_model(model, model_dir) # type: ignore[attr-defined] + return model_dir, model, x + + +class TestPassthroughMode(unittest.TestCase): + def test_round_trip_and_warning(self): + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir, model, x = _save_no_signature_model(tmp) + out = os.path.join(tmp, "passthrough.fnnx") + + with mock.patch("fnnx.extras.mlflow.console.warn") as warn_mock: + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual(cfg["input_mode"], "passthrough") + self.assertEqual(len(manifest["inputs"]), 1) + self.assertEqual(manifest["inputs"][0]["name"], "data") + self.assertEqual(manifest["inputs"][0]["dtype"], "ext::mlflow::input") + + warned = [c.args[0] for c in warn_mock.call_args_list] + self.assertTrue( + any("passthrough" in m for m in warned), + f"expected passthrough warning, got {warned!r}", + ) + + rt = Runtime(out) + # In passthrough mode the wrapper hands inputs["data"] straight to + # predict(); pyfunc accepts a DataFrame for sklearn models. + result = rt.compute({"data": x}, {}) + + self.assertIn("predictions", result) + self.assertEqual(result["predictions"], model.predict(x).tolist()) + + +# --------------------------------------------------------------------------- +# Params schema → dynamic_attributes + predict(params=...) +# --------------------------------------------------------------------------- + + +_PARAMS_MODEL_SCRIPT = textwrap.dedent( + """ + import mlflow + + + class ParamsModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input, params=None): + params = params or {} + return { + "temperature_used": params.get("temperature", -1.0), + "max_tokens_used": params.get("max_tokens", -1), + } + + + mlflow.models.set_model(ParamsModel()) + """ +).lstrip() + + +def _save_params_model(tmp: str) -> str: + import mlflow # type: ignore[import-not-found] + from mlflow.models import ModelSignature # type: ignore[import-not-found] + from mlflow.types import DataType # type: ignore[import-not-found] + from mlflow.types.schema import ColSpec, ParamSchema, ParamSpec, Schema # type: ignore[import-not-found] + + script_path = os.path.join(tmp, "params_model_script.py") + with open(script_path, "w") as f: + f.write(_PARAMS_MODEL_SCRIPT) + + signature = ModelSignature( + inputs=Schema([ColSpec(name="prompt", type=DataType.string)]), + outputs=Schema([ColSpec(type=DataType.string)]), + params=ParamSchema( + [ + ParamSpec("temperature", DataType.double, 0.7), + ParamSpec("max_tokens", DataType.long, 256), + ] + ), + ) + + model_dir = os.path.join(tmp, "params_model") + mlflow.pyfunc.save_model( # type: ignore[attr-defined] + path=model_dir, + python_model=script_path, + signature=signature, + ) + return model_dir + + +class TestParamsModeDynamicAttributes(unittest.TestCase): + """Params schema → dynamic_attributes; values reach predict(..., params=...).""" + + def test_manifest_and_round_trip(self): + from fnnx.extras.mlflow import package_mlflow_model + from fnnx.runtime import Runtime + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_params_model(tmp) + out = os.path.join(tmp, "params.fnnx") + package_mlflow_model(model_dir, out) + + with tarfile.open(out, "r") as tar: + manifest = json.loads( + tar.extractfile("manifest.json").read().decode() # type: ignore[union-attr] + ) + variant_config = json.loads( + tar.extractfile("variant_config.json").read().decode() # type: ignore[union-attr] + ) + + attrs = manifest["dynamic_attributes"] + attr_names = {a["name"] for a in attrs} + self.assertEqual(attr_names, {"temperature", "max_tokens"}) + + cfg = variant_config["extra_values"]["fnnx_mlflow"] + self.assertEqual(sorted(cfg["param_names"]), ["max_tokens", "temperature"]) + + rt = Runtime(out) + # Pass temperature, omit max_tokens (lets MLflow default apply). + result = rt.compute( + {"prompt": ["hello"]}, + {"temperature": 0.1, "max_tokens": 42}, + ) + + self.assertIn("predictions", result) + predictions = result["predictions"] + self.assertEqual(predictions["temperature_used"], 0.1) + self.assertEqual(predictions["max_tokens_used"], 42) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_verify.py b/src/python/tests/test_mlflow_verify.py new file mode 100644 index 0000000..59a8201 --- /dev/null +++ b/src/python/tests/test_mlflow_verify.py @@ -0,0 +1,259 @@ +"""Tests for ``verify=True`` smoke testing and ``_example_to_fnnx_inputs``. + +The conversion path is unchanged here; we exercise the post-save verification +that loads the FNNX package via ``fnnx.Runtime`` and (when an MLflow input +example exists) runs one ``compute`` to prove the package is portable enough +in the current environment. +""" + +from __future__ import annotations + +import os +import sys +import tempfile +import unittest +from unittest import mock + +import numpy as np +import pandas as pd +import pytest + + +# Unit tests for _example_to_fnnx_inputs do not need mlflow at all. +from fnnx.extras.mlflow import _example_to_fnnx_inputs + + +# --------------------------------------------------------------------------- +# _example_to_fnnx_inputs unit tests (no mlflow needed) +# --------------------------------------------------------------------------- + + +class TestExampleToFnnxInputsColumns(unittest.TestCase): + def test_dataframe_split_by_column_order(self): + df = pd.DataFrame({"a": [1.0, 2.0], "b": [3.0, 4.0], "c": [5.0, 6.0]}) + cfg = {"column_order": ["a", "b"]} + inputs = _example_to_fnnx_inputs(df, "columns", cfg) + + self.assertEqual(set(inputs.keys()), {"a", "b"}) + np.testing.assert_array_equal(inputs["a"], [1.0, 2.0]) + np.testing.assert_array_equal(inputs["b"], [3.0, 4.0]) + + def test_dataframe_without_column_order_uses_df_columns(self): + df = pd.DataFrame({"x": [10.0], "y": [20.0]}) + inputs = _example_to_fnnx_inputs(df, "columns", {}) + + self.assertEqual(set(inputs.keys()), {"x", "y"}) + np.testing.assert_array_equal(inputs["x"], [10.0]) + + def test_dict_of_arrays_passes_through(self): + ex = {"a": [1.0, 2.0], "b": [3.0, 4.0]} + cfg = {"column_order": ["a", "b"]} + inputs = _example_to_fnnx_inputs(ex, "columns", cfg) + + self.assertEqual(inputs["a"], [1.0, 2.0]) + self.assertEqual(inputs["b"], [3.0, 4.0]) + + def test_list_of_records_reshaped_to_columns(self): + ex = [{"a": 1, "b": 2}, {"a": 3, "b": 4}] + cfg = {"column_order": ["a", "b"]} + inputs = _example_to_fnnx_inputs(ex, "columns", cfg) + + self.assertEqual(inputs["a"], [1, 3]) + self.assertEqual(inputs["b"], [2, 4]) + + def test_unsupported_type_raises(self): + with self.assertRaises(TypeError): + _example_to_fnnx_inputs(42, "columns", {"column_order": ["a"]}) + + +class TestExampleToFnnxInputsTensor(unittest.TestCase): + def test_single_unnamed_tensor_wrapped_as_input(self): + arr = np.array([[1.0, 2.0]], dtype=np.float32) + cfg = {"tensor_names": ["__single__"]} + inputs = _example_to_fnnx_inputs(arr, "tensor", cfg) + + self.assertEqual(set(inputs.keys()), {"input"}) + self.assertIs(inputs["input"], arr) + + def test_named_tensors_pass_dict_through(self): + a = np.array([1.0], dtype=np.float32) + b = np.array([2.0], dtype=np.float32) + cfg = {"tensor_names": ["a", "b"]} + inputs = _example_to_fnnx_inputs({"a": a, "b": b}, "tensor", cfg) + + self.assertEqual(set(inputs.keys()), {"a", "b"}) + self.assertIs(inputs["a"], a) + self.assertIs(inputs["b"], b) + + def test_single_named_tensor_accepts_bare_ndarray(self): + arr = np.array([1.0, 2.0]) + cfg = {"tensor_names": ["only"]} + inputs = _example_to_fnnx_inputs(arr, "tensor", cfg) + + self.assertEqual(set(inputs.keys()), {"only"}) + self.assertIs(inputs["only"], arr) + + def test_named_tensors_non_dict_raises(self): + cfg = {"tensor_names": ["a", "b"]} + with self.assertRaises(TypeError): + _example_to_fnnx_inputs([1.0, 2.0], "tensor", cfg) + + +class TestExampleToFnnxInputsJsonPassthrough(unittest.TestCase): + def test_json_mode_wraps_under_data(self): + ex = {"prompt": "hi", "temperature": 0.1} + inputs = _example_to_fnnx_inputs(ex, "json", {}) + self.assertEqual(inputs, {"data": ex}) + + def test_passthrough_mode_wraps_under_data(self): + inputs = _example_to_fnnx_inputs("hello", "passthrough", {}) + self.assertEqual(inputs, {"data": "hello"}) + + +# --------------------------------------------------------------------------- +# End-to-end verify=True tests (need mlflow + sklearn + pandas) +# --------------------------------------------------------------------------- + + +pytest.importorskip("mlflow") +pytest.importorskip("sklearn") + + +def _train_tiny_sklearn(): + from sklearn.ensemble import RandomForestClassifier # type: ignore[import-not-found] + + x = pd.DataFrame( + { + "a": [0.0, 1.0, 2.0, 3.0], + "b": [3.0, 2.0, 1.0, 0.0], + } + ) + y = [0, 1, 0, 1] + model = RandomForestClassifier(n_estimators=3, random_state=0) + model.fit(x, y) + return model, x, y + + +def _save_sklearn_model(tmp: str, *, include_input_example: bool = True) -> str: + import mlflow # type: ignore[import-not-found] + from mlflow.models.signature import infer_signature # type: ignore[import-not-found] + + model, x, _ = _train_tiny_sklearn() + model_dir = os.path.join(tmp, "mlflow_model") + sig = infer_signature(x, model.predict(x)) + kwargs = {"signature": sig} + if include_input_example: + kwargs["input_example"] = x + mlflow.sklearn.save_model(model, model_dir, **kwargs) # type: ignore[attr-defined] + return model_dir + + +class TestVerifySuccess(unittest.TestCase): + def test_verify_true_with_input_example_passes(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_sklearn_model(tmp, include_input_example=True) + out = os.path.join(tmp, "m.fnnx") + # Should run the input example through Runtime.compute() without + # raising. + package_mlflow_model(model_dir, out, verify=True) + + # The package was written (verification did not delete it). + self.assertTrue(os.path.isfile(out)) + + def test_verify_true_without_input_example_passes(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_sklearn_model(tmp, include_input_example=False) + out = os.path.join(tmp, "m.fnnx") + # No saved input example -> verification only loads the package. + package_mlflow_model(model_dir, out, verify=True) + self.assertTrue(os.path.isfile(out)) + + +class TestVerifyLoaderFails(unittest.TestCase): + def test_load_failure_raises_verification_error(self): + from fnnx.extras.mlflow import ( + MLflowPackagingVerificationError, + package_mlflow_model, + ) + + with tempfile.TemporaryDirectory() as tmp: + model_dir = _save_sklearn_model(tmp, include_input_example=False) + out = os.path.join(tmp, "m.fnnx") + + # Monkeypatch mlflow.pyfunc.load_model to raise so the Runtime warmup + # blows up, mimicking a non-self-contained model on a clean env. + import mlflow.pyfunc as pyfunc_mod # type: ignore[import-not-found] + + def _boom(*_args, **_kwargs): + raise ImportError("simulated: missing CustomModel class") + + with mock.patch.object(pyfunc_mod, "load_model", _boom): + with self.assertRaises(MLflowPackagingVerificationError) as cm: + package_mlflow_model(model_dir, out, verify=True) + + err = cm.exception + self.assertIsInstance(err.__cause__, Exception) + self.assertIn("self-contained", str(err)) + # The package was still written before verification ran. + self.assertTrue(os.path.isfile(out)) + + +class TestVerifyWithoutMlflow(unittest.TestCase): + """verify=True must degrade to a skip warning when mlflow is unimportable.""" + + def setUp(self) -> None: + self._saved: dict[str, object] = {} + # Save and block all mlflow modules to simulate a no-mlflow env. + for name in list(sys.modules): + if name == "mlflow" or name.startswith("mlflow."): + self._saved[name] = sys.modules.pop(name) + + def tearDown(self) -> None: + sys.modules.pop("mlflow", None) + for name, mod in self._saved.items(): + sys.modules[name] = mod # type: ignore[assignment] + + def _save_model_then_block(self, tmp: str) -> str: + # Re-enable mlflow temporarily to save the source model. + for name, mod in self._saved.items(): + sys.modules[name] = mod # type: ignore[assignment] + try: + model_dir = _save_sklearn_model(tmp, include_input_example=False) + finally: + for name in list(sys.modules): + if name == "mlflow" or name.startswith("mlflow."): + self._saved[name] = sys.modules.pop(name) + sys.modules["mlflow"] = None # type: ignore[assignment] + return model_dir + + def test_verify_true_no_mlflow_warns_and_skips(self): + from fnnx.extras.mlflow import package_mlflow_model + + with tempfile.TemporaryDirectory() as tmp: + model_dir = self._save_model_then_block(tmp) + out = os.path.join(tmp, "m.fnnx") + + # Capture stdout to check the warning. + with mock.patch( + "fnnx.extras.mlflow.console.warn" + ) as warn_mock: + package_mlflow_model(model_dir, out, verify=True) + + # Package was written. + self.assertTrue(os.path.isfile(out)) + # Warning was emitted; mlflow stayed un-importable. + calls = [c.args[0] for c in warn_mock.call_args_list] + self.assertTrue( + any("verification skipped" in m for m in calls), + f"expected skip warning, got {calls!r}", + ) + # mlflow was never imported during verify. + self.assertIs(sys.modules.get("mlflow"), None) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/python/tests/test_mlflow_wrapper.py b/src/python/tests/test_mlflow_wrapper.py new file mode 100644 index 0000000..8cd284f --- /dev/null +++ b/src/python/tests/test_mlflow_wrapper.py @@ -0,0 +1,451 @@ +"""Unit tests for the static MLflow runtime wrapper and the PyfuncBuilder +``python_version`` override. + +These tests deliberately stub out ``mlflow.pyfunc.load_model`` and the loaded +model object so the wrapper's input assembly / param passing / output +normalization logic can be exercised without an actual mlflow install. +""" + +from dataclasses import dataclass +import json +import os +import sys +import tarfile +import tempfile +import unittest +from typing import cast +from unittest import mock + +import numpy as np +import pandas as pd + +from fnnx.variants.pyfunc import Context +from fnnx.extras._mlflow_wrapper import MLflowModel, _to_jsonable +from fnnx.extras.builder import PyfuncBuilder, PyFuncSpec, PYTHON_VERSION + + +class _FakeContext: + def __init__( + self, values: dict, dirs: dict | None = None, device: object | None = None + ): + self._values = values + self._dirs = dirs or {} + self.device = device + + def get_value(self, key): + return self._values.get(key) + + def get_dirpath(self, name): + return self._dirs.get(name) + + +class _RecordingModel: + """Stand-in for an mlflow pyfunc model that records calls.""" + + def __init__(self, return_value=None): + self.calls = [] + self.return_value = return_value if return_value is not None else [1, 2, 3] + + def predict(self, payload, params=None): + self.calls.append({"payload": payload, "params": params}) + return self.return_value + + +def _make_wrapper(cfg: dict, model: _RecordingModel) -> MLflowModel: + """Construct an MLflowModel with a fake context and pre-assigned model.""" + ctx = _FakeContext({"fnnx_mlflow": cfg}, dirs={cfg["model_dir"]: "/tmp/fake"}) + wrapper = MLflowModel(cast(Context, ctx)) + wrapper._cfg = cfg + wrapper.model = model # type: ignore[assignment] + return wrapper + + +class TestComputeColumnsMode(unittest.TestCase): + def test_dataframe_assembled_from_column_order(self): + model = _RecordingModel(return_value=np.array([0.1, 0.2])) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "columns", + "column_order": ["a", "b"], + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + inputs = { + "a": np.array([1.0, 2.0]), + "b": np.array([10.0, 20.0]), + # extra columns not in column_order should be ignored + "c": np.array([99.0, 99.0]), + } + + out = wrapper.compute(inputs, {}) + + self.assertEqual(len(model.calls), 1) + payload = model.calls[0]["payload"] + self.assertIsInstance(payload, pd.DataFrame) + self.assertEqual(list(payload.columns), ["a", "b"]) + np.testing.assert_array_equal(payload["a"].values, [1.0, 2.0]) + np.testing.assert_array_equal(payload["b"].values, [10.0, 20.0]) + self.assertIsNone(model.calls[0]["params"]) + self.assertEqual(out["predictions"], [0.1, 0.2]) + + +class TestComputeTensorMode(unittest.TestCase): + def test_single_unnamed_tensor_passes_ndarray(self): + model = _RecordingModel(return_value=np.array([[1.0, 2.0]])) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "tensor", + "tensor_names": ["__single__"], + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + arr = np.array([[1.0, 2.0, 3.0, 4.0]]) + out = wrapper.compute({"input": arr}, {}) + + payload = model.calls[0]["payload"] + self.assertIs(payload, arr) + self.assertEqual(out["predictions"], [[1.0, 2.0]]) + + def test_named_tensors_pass_dict(self): + model = _RecordingModel(return_value={"y": [0.0]}) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "tensor", + "tensor_names": ["a", "b"], + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + a = np.array([1, 2], dtype=np.int32) + b = np.array([3, 4], dtype=np.int32) + wrapper.compute({"a": a, "b": b}, {}) + + payload = model.calls[0]["payload"] + self.assertIsInstance(payload, dict) + self.assertEqual(set(payload.keys()), {"a", "b"}) + self.assertIs(payload["a"], a) + self.assertIs(payload["b"], b) + + +class TestComputeJsonAndPassthrough(unittest.TestCase): + def test_json_mode_unwraps_data(self): + model = _RecordingModel(return_value={"answer": 42}) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "json", + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + records = [{"x": 1, "y": "hello"}] + out = wrapper.compute({"data": records}, {}) + + self.assertEqual(model.calls[0]["payload"], records) + self.assertEqual(out["predictions"], {"answer": 42}) + + def test_passthrough_mode_unwraps_data(self): + model = _RecordingModel(return_value=[1, 2, 3]) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + out = wrapper.compute({"data": "any-payload"}, {}) + + self.assertEqual(model.calls[0]["payload"], "any-payload") + self.assertEqual(out["predictions"], [1, 2, 3]) + + +class TestParamPassing(unittest.TestCase): + def test_known_params_forwarded_to_predict(self): + model = _RecordingModel(return_value="ok") + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": ["temperature", "max_tokens"], + } + wrapper = _make_wrapper(cfg, model) + + wrapper.compute( + {"data": "x"}, + {"temperature": 0.1, "max_tokens": 256, "unknown": "ignored"}, + ) + + params = model.calls[0]["params"] + self.assertEqual(params, {"temperature": 0.1, "max_tokens": 256}) + + def test_missing_params_omitted_no_params_kwarg(self): + model = _RecordingModel(return_value="ok") + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": ["temperature"], + } + wrapper = _make_wrapper(cfg, model) + + # No matching dynamic attribute -> predict called WITHOUT params kwarg + wrapper.compute({"data": "x"}, {}) + self.assertIsNone(model.calls[0]["params"]) + + def test_empty_param_names_does_not_pass_params(self): + model = _RecordingModel(return_value="ok") + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + wrapper.compute({"data": "x"}, {"temperature": 0.5}) + self.assertIsNone(model.calls[0]["params"]) + + +class TestComputeAsyncDelegates(unittest.TestCase): + def test_async_returns_sync_result(self): + import asyncio + + model = _RecordingModel(return_value=[1]) + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + wrapper = _make_wrapper(cfg, model) + + out = asyncio.run(wrapper.compute_async({"data": "x"}, {})) + self.assertEqual(out, {"predictions": [1]}) + + +class TestToJsonable(unittest.TestCase): + def test_primitives_returned_as_is(self): + self.assertEqual(_to_jsonable(None), None) + self.assertEqual(_to_jsonable(True), True) + self.assertEqual(_to_jsonable(7), 7) + self.assertEqual(_to_jsonable(1.5), 1.5) + self.assertEqual(_to_jsonable("hello"), "hello") + + def test_numpy_ndarray_and_scalar(self): + arr = np.array([[1, 2], [3, 4]], dtype=np.int32) + self.assertEqual(_to_jsonable(arr), [[1, 2], [3, 4]]) + # numpy scalar via .item() + self.assertEqual(_to_jsonable(np.int64(5)), 5) + self.assertEqual(_to_jsonable(np.float32(0.5)), 0.5) + + def test_pandas_dataframe_and_series(self): + df = pd.DataFrame({"a": [1, 2], "b": ["x", "y"]}) + self.assertEqual( + _to_jsonable(df), + [{"a": 1, "b": "x"}, {"a": 2, "b": "y"}], + ) + s = pd.Series([10, 20, 30]) + self.assertEqual(_to_jsonable(s), [10, 20, 30]) + + def test_nested_dict_list_with_numpy(self): + payload = { + "vals": np.array([1.0, 2.0]), + "items": [np.int64(3), {"k": np.float64(1.25)}], + } + self.assertEqual( + _to_jsonable(payload), + {"vals": [1.0, 2.0], "items": [3, {"k": 1.25}]}, + ) + + def test_pydantic_model(self): + from pydantic import BaseModel + + class M(BaseModel): + name: str + value: int + + self.assertEqual( + _to_jsonable(M(name="a", value=1)), + {"name": "a", "value": 1}, + ) + + def test_dataclass(self): + @dataclass + class D: + x: int + y: str + + self.assertEqual(_to_jsonable(D(1, "z")), {"x": 1, "y": "z"}) + + def test_fallback_str(self): + class Custom: + def __str__(self): + return "custom-repr" + + self.assertEqual(_to_jsonable(Custom()), "custom-repr") + + +class TestWarmup(unittest.TestCase): + def test_warmup_invokes_mlflow_pyfunc_load(self): + """warmup() must call mlflow.pyfunc.load_model on the resolved dir.""" + + # Build a fake "mlflow.pyfunc" module that we can inject. + fake_pyfunc = mock.MagicMock() + fake_pyfunc.load_model.return_value = "fake-model-object" + fake_mlflow = mock.MagicMock() + fake_mlflow.pyfunc = fake_pyfunc + + with mock.patch.dict( + sys.modules, + {"mlflow": fake_mlflow, "mlflow.pyfunc": fake_pyfunc}, + ): + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + ctx = _FakeContext( + {"fnnx_mlflow": cfg}, + dirs={"mlflow_model": "/abs/path/to/mlflow_model"}, + ) + wrapper = MLflowModel(cast(Context, ctx)) + wrapper.warmup() + + fake_pyfunc.load_model.assert_called_once_with("/abs/path/to/mlflow_model") + self.assertEqual(wrapper.model, "fake-model-object") + self.assertEqual(wrapper._cfg, cfg) + + def test_warmup_missing_dir_raises(self): + fake_pyfunc = mock.MagicMock() + fake_mlflow = mock.MagicMock() + fake_mlflow.pyfunc = fake_pyfunc + + with mock.patch.dict( + sys.modules, + {"mlflow": fake_mlflow, "mlflow.pyfunc": fake_pyfunc}, + ): + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + ctx = _FakeContext({"fnnx_mlflow": cfg}, dirs={}) # not registered + wrapper = MLflowModel(cast(Context, ctx)) + with self.assertRaises(FileNotFoundError): + wrapper.warmup() + + +class TestWarmupDevice(unittest.TestCase): + """The FNNX device_map should drive mlflow's load-time prediction device.""" + + def _warmup_with_device(self, device_attr) -> dict: + from fnnx.extras._mlflow_wrapper import _MLFLOW_DEVICE_ENV + + seen: dict = {} + + def fake_load(model_dir): + seen["dir"] = model_dir + seen["env"] = os.environ.get(_MLFLOW_DEVICE_ENV) + return "model-obj" + + fake_pyfunc = mock.MagicMock() + fake_pyfunc.load_model.side_effect = fake_load + fake_mlflow = mock.MagicMock() + fake_mlflow.pyfunc = fake_pyfunc + + cfg = { + "model_dir": "mlflow_model", + "input_mode": "passthrough", + "param_names": [], + } + ctx = _FakeContext( + {"fnnx_mlflow": cfg}, + dirs={"mlflow_model": "/abs/x"}, + device=device_attr, # the wrapper reads Context.device + ) + + with mock.patch.dict( + sys.modules, {"mlflow": fake_mlflow, "mlflow.pyfunc": fake_pyfunc} + ): + wrapper = MLflowModel(cast(Context, ctx)) + wrapper.warmup() + return seen + + def test_cuda_devicemap_sets_env_during_load_and_restores(self): + from fnnx.device import DeviceMap + from fnnx.extras._mlflow_wrapper import _MLFLOW_DEVICE_ENV + + self.assertNotIn(_MLFLOW_DEVICE_ENV, os.environ) + seen = self._warmup_with_device( + DeviceMap(accelerator="cuda", node_device_map={}) + ) + self.assertEqual(seen["env"], "cuda") + # restored after load — process env not leaked + self.assertNotIn(_MLFLOW_DEVICE_ENV, os.environ) + + def test_cpu_string_forces_cpu(self): + seen = self._warmup_with_device("cpu") + self.assertEqual(seen["env"], "cpu") + + def test_cuda_indexed_preserved(self): + from fnnx.device import DeviceMap + + seen = self._warmup_with_device( + DeviceMap(accelerator="cuda:1", node_device_map={}) + ) + self.assertEqual(seen["env"], "cuda:1") + + def test_unknown_accelerator_leaves_env_unset(self): + seen = self._warmup_with_device("auto") + self.assertIsNone(seen["env"]) + + def test_external_env_var_wins(self): + from fnnx.device import DeviceMap + from fnnx.extras._mlflow_wrapper import _MLFLOW_DEVICE_ENV + + os.environ[_MLFLOW_DEVICE_ENV] = "cuda:3" + try: + seen = self._warmup_with_device( + DeviceMap(accelerator="cpu", node_device_map={}) + ) + self.assertEqual(seen["env"], "cuda:3") # device_map did not override + finally: + os.environ.pop(_MLFLOW_DEVICE_ENV, None) + + +class TestBuilderPythonVersion(unittest.TestCase): + def _build(self, python_version: str | None) -> dict: + # Use the static wrapper module as the PyFunc source — its filepath + # exists on disk so PyfuncBuilder can read it without a real PyFunc class. + from fnnx.extras import _mlflow_wrapper + + with tempfile.TemporaryDirectory() as tmp: + out_path = os.path.join(tmp, "test.fnnx") + kwargs = {} + if python_version is not None: + kwargs["python_version"] = python_version + builder = PyfuncBuilder( + pyfunc=PyFuncSpec( + filepath=_mlflow_wrapper.__file__, class_name="MLflowModel" + ), + **kwargs, + ) + builder.add_runtime_dependency("mlflow==3.4.0") + builder.save(out_path) + + with tarfile.open(out_path, "r") as tar: + env_member = tar.extractfile("env.json") + assert 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