From 255961beaf1b45cc649f373c19d87eca01cf9c6f Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 22:25:28 -0400 Subject: [PATCH] custom kserve runtime --- services/inference/Dockerfile | 44 + services/inference/Makefile | 30 + services/inference/README.md | 76 + services/inference/poetry.lock | 2911 +++++++++++++++++ services/inference/pyproject.toml | 36 + services/inference/tests/test_inference.py | 153 + services/inference/tsfmservices/__init__.py | 47 + .../inference/tsfmservices/common/__init__.py | 0 .../tsfmservices/common/constants.py | 4 + .../tsfmservices/common/logging.conf | 35 + .../inference/tsfmservices/common/util.py | 162 + .../tsfmservices/config/default_config.yml | 8 + .../tsfmservices/inference/__init__.py | 1 + .../tsfmservices/inference/inference.py | 178 + .../inference/tsfmservices/inference/main.py | 51 + .../tsfmservices/inference/payloads.py | 138 + 16 files changed, 3874 insertions(+) create mode 100644 services/inference/Dockerfile create mode 100644 services/inference/Makefile create mode 100644 services/inference/README.md create mode 100644 services/inference/poetry.lock create mode 100644 services/inference/pyproject.toml create mode 100644 services/inference/tests/test_inference.py create mode 100644 services/inference/tsfmservices/__init__.py create mode 100644 services/inference/tsfmservices/common/__init__.py create mode 100644 services/inference/tsfmservices/common/constants.py create mode 100644 services/inference/tsfmservices/common/logging.conf create mode 100644 services/inference/tsfmservices/common/util.py create mode 100644 services/inference/tsfmservices/config/default_config.yml create mode 100644 services/inference/tsfmservices/inference/__init__.py create mode 100644 services/inference/tsfmservices/inference/inference.py create mode 100644 services/inference/tsfmservices/inference/main.py create mode 100644 services/inference/tsfmservices/inference/payloads.py diff --git a/services/inference/Dockerfile b/services/inference/Dockerfile new file mode 100644 index 00000000..0df00f8d --- /dev/null +++ b/services/inference/Dockerfile @@ -0,0 +1,44 @@ +# based on https://github.com/opendatahub-io/caikit-tgis-serving/blob/main/Dockerfile + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as builder + +RUN microdnf -y update && \ + microdnf -y install \ + git shadow-utils python3.11-pip python-wheel && \ + pip3.11 install --no-cache-dir --upgrade pip wheel && \ + microdnf clean all + +ENV POETRY_VIRTUALENVS_IN_PROJECT=1 + +COPY tsfmservices* /tsfmservices/tsfmservices +COPY pyproject.toml /tsfmservices/ +COPY poetry.lock /tsfmservices/ +WORKDIR /tsfmservices +# RUN python3.11 -m venv /tsfmservices/.venv +#RUN source /tsfmservices/.venv/bin/activate && \ +# pip3.11 install --no-cache-dir ".[services]" +RUN pip3.11 install poetry && poetry install + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as deploy +RUN microdnf -y update && \ + microdnf -y install \ + shadow-utils python3.11 && \ + microdnf clean all + +WORKDIR /tsfmservices + +COPY --from=builder /tsfmservices/ /tsfmservices/ + +ENV VIRTUAL_ENV=/tsfmservices/.venv +ENV PATH="$VIRTUAL_ENV/bin:$PATH" +ENV HF_HOME=/tmp + +RUN groupadd --system tsfmservices --gid 1001 && \ + adduser --system --uid 1001 --gid 0 --groups tsfmservices \ + --create-home --home-dir /tsfmservices --shell /sbin/nologin \ + --comment "TSFMServices User" tsfmservices + +USER tsfmservices + +# CMD ["python", "-m", "tsfmservices.inference.main"] +CMD ["python", "-m", "uvicorn","tsfmservices.inference.main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/services/inference/Makefile b/services/inference/Makefile new file mode 100644 index 00000000..9d515e20 --- /dev/null +++ b/services/inference/Makefile @@ -0,0 +1,30 @@ +CONTAINER_BUILDER ?= docker + +# starts the inference service (used mainly for test cases) +start_service_local: + python -m tsfmservices.inference.main & + sleep 10 +stop_service_local: + pkill -f 'python.*tsfmservices.*' + sleep 10 + +image: + $(CONTAINER_BUILDER) build -t tsfmservices -f Dockerfile . + +start_service_image: image + $(CONTAINER_BUILDER) run -p 8000:8000 -d --rm --name tsfmserver tsfmservices + sleep 10 +stop_service_image: + $(CONTAINER_BUILDER) stop tsfmserver + +test_local: start_service_local + pytest tests + $(MAKE) stop_service_local + +test_image: start_service_image + pytest tests + $(MAKE) stop_service_image + + + + diff --git a/services/inference/README.md b/services/inference/README.md new file mode 100644 index 00000000..90683065 --- /dev/null +++ b/services/inference/README.md @@ -0,0 +1,76 @@ +# TSFM Services + +This component provides basic RESTful services for the IBM tsfm-granite +class of timeseries foundation models. At present it can serve the following models: + +* https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1 +* https://huggingface.co/ibm-granite/granite-timeseries-patchtst +* https://huggingface.co/ibm-granite/granite-timeseries-patchtsmixer + + + +## Prerequisites: + +* GNU make +* python 3.10 or 3.11 +* poetry (`pip install poetry`) +* zsh or bash (zsh is preferred) + +_Note that our primary target environment for services deployment is x86_64 Linux. You may encounter hiccups if you try to use this on a different environment. If so, please +file an issue. Some of our developers do use a Mac so you're likely to find a quick resolution. None of our developers use native Windows, however._ + +## Known issues: + +* Use of pkill statements in Makefile may not work properly on Mac OS. This will be apparent if you have left over processs after running test related make targets. Please help us put OS-specific checks into our Makefile to handle these cases by filing a PR. + +### Installation + +```sh +pip install poetry && poetry install --with dev +``` + +### Testing using a local server instance + +```sh +make test_local +``` + +### Creating an image + +_You must have either docker or podman installed on your system for this to +work. You must also have proper permissions on your system to build images._ + +```sh +CONTAINER_BUILDER= make image +# e.g, CONTAINER_BUILDER=docker make image +``` + +After a successful build you should have a local image named `tsfmservices:latest` + +```sh +(py311) ➜ tsfm-services git:(revised-build-system) ✗ docker images | grep tsfmservices | head -n 1 +tsfmservices latest df592dcb0533 46 seconds ago 1.49GB +# some of the numeric and hash values on your machine could be different +``` + +### Testing using the built image + +```sh +make test_image +``` + +### Viewing the OpenAPI 3.x specification and swagger page + +```sh +make start_local_server +``` + +Then open your browser to http://127.0.0.1:8000/docs, click on the `/openapi.json` link at the top of this page. + +To stop the server run: + +```sh +# may not work properly on a Mac +# if not, kill the uvicorn process manually. +make stop_local_server +``` diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock new 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+[tool.poetry] +name = "tsfmservices" +version = "0.0.1" +description = "Service layer for TSFM granite models." +authors = ["IBM"] +license = "https://github.com/ibm-granite/granite-tsfm/blob/main/LICENSE" +packages = [{ include = "tsfmservices/**/*.py" }] + +[tool.poetry.dependencies] +# including 3.9 causes poetry lock to run forever +python = ">=3.10,<3.12" + +numpy = { version = "<2" } +tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.5" } + +# trying to pick up cpu version for inference +# to make image smaller +torch = { version = ">2,<3", source = "pytorch" } + +fastapi = { version = "*" } +pydantic = { version = "*" } +uvicorn = { version = "*" } + +[[tool.poetry.source]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[tool.poetry.group.dev] +optional = true +[tool.poetry.group.dev.dependencies] +pytest = "*" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/services/inference/tests/test_inference.py b/services/inference/tests/test_inference.py new file mode 100644 index 00000000..5fb8ed0c --- /dev/null +++ b/services/inference/tests/test_inference.py @@ -0,0 +1,153 @@ +# Standard +from datetime import datetime +from io import BytesIO +from typing import Any, Dict +import json + +# Third Party +import numpy as np +import pandas as pd +import pytest +import requests + +# First Party +from tsfm_public.toolkit.util import select_by_index +from tsfm_public import TinyTimeMixerForPrediction + +from transformers import PatchTSTForPrediction, PatchTSMixerForPrediction + + +@pytest.fixture(scope="module") +def ts_data(): + dataset_path = ( + "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" + ) + + forecast_length = 96 + context_length = 512 + timestamp_column = "date" + + data = pd.read_csv( + dataset_path, + parse_dates=[timestamp_column], + ) + + test_data = select_by_index( + data, + start_index=12 * 30 * 24 + 4 * 30 * 24 - context_length * 5, + end_index=12 * 30 * 24 + 4 * 30 * 24, + ).reset_index(drop=True) + + test_data["id"] = np.array(["a", "b", "c", "d", "e"]).repeat(context_length) + + return test_data, { + "timestamp_column": timestamp_column, + "id_columns": ["id"], + "target_columns": ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"], + "prediction_length": forecast_length, + } + + +def get_inference_response( + msg: Dict[str, Any], +) -> pd.DataFrame: + URL = "http://127.0.0.1:8000/v1/inference/forecasting" + headers = {} + req = requests.post(URL, json=msg, headers=headers) + resp = req.json() + + df = [pd.DataFrame.from_dict(r) for r in resp["results"]] + return df + + +def encode_data(df: pd.DataFrame, timestamp_column: str) -> Dict[str, Any]: + df[timestamp_column] = df[timestamp_column].apply(lambda x: x.isoformat()) + data_payload = df.to_dict(orient="list") + return data_payload + + +def test_zero_shot_forecast_inference(ts_data): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + num_ids = test_data[id_columns[0]].nunique() + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length + + # test multi-time series + test_data_ = test_data.copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length * num_ids + + +@pytest.mark.parametrize( + "model_path", + [ + "ibm-granite/granite-timeseries-patchtst", + "ibm-granite/granite-timeseries-patchtsmixer", + ], +) +def test_trained_model_inference(ts_data, model_path): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + encoded_data = encode_data(test_data_, params["timestamp_column"]) + + msg = { + "model_id": model_path, + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encoded_data, + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfmservices/__init__.py new file mode 100644 index 00000000..34dd794f --- /dev/null +++ b/services/inference/tsfmservices/__init__.py @@ -0,0 +1,47 @@ +import logging +import os + +TSFM_PYTHON_LOGGING_LEVEL = os.getenv("TSFM_PYTHON_LOGGING_LEVEL", "INFO") + +LevelNamesMapping = dict( + INFO=logging.INFO, + WARN=logging.WARN, + WARNING=logging.WARNING, + ERROR=logging.ERROR, + CRITICAL=logging.CRITICAL, + DEBUG=logging.DEBUG, + FATAL=logging.FATAL, +) + +TSFM_PYTHON_LOGGING_LEVEL = ( + logging.getLevelNamesMapping()[TSFM_PYTHON_LOGGING_LEVEL] + if hasattr(logging, "getLevelNamesMapping") + else LevelNamesMapping[TSFM_PYTHON_LOGGING_LEVEL] +) +TSFM_PYTHON_LOGGING_FORMAT = os.getenv( + "TSFM_PYTHON_LOGGING_FORMAT", + "%(levelname)s:p-%(process)d:t-%(thread)d:%(filename)s:%(funcName)s:%(message)s", +) + +# Do we have redis? + +try: + # Third Party + import redis + import rq + + HAVE_REDIS_BACKEND = True +except ImportError: + HAVE_REDIS_BACKEND = False + +TSFM_USE_KFTO_ASYNC_BACKEND = int(os.getenv("TSFM_USE_KFTO_ASYNC_BACKEND", "0")) == 1 +USE_REDIS_ASYNC_BACKEND = HAVE_REDIS_BACKEND and not TSFM_USE_KFTO_ASYNC_BACKEND + +TSFM_ALLOW_LOAD_FROM_HF_HUB = int(os.getenv("TSFM_ALLOW_LOAD_FROM_HF_HUB", "1")) == 1 + +TSFM_CONFIG_FILE = os.getenv( + "TSFM_CONFIG_FILE", + os.path.realpath( + os.path.join(os.path.dirname(__file__), "config", "default_config.yml") + ), +) diff --git a/services/inference/tsfmservices/common/__init__.py b/services/inference/tsfmservices/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/services/inference/tsfmservices/common/constants.py b/services/inference/tsfmservices/common/constants.py new file mode 100644 index 00000000..15f42cd3 --- /dev/null +++ b/services/inference/tsfmservices/common/constants.py @@ -0,0 +1,4 @@ +"""Holds constant values used across modules.""" + +# What will appear in our paths for fast API version +API_VERSION = "v1" diff --git a/services/inference/tsfmservices/common/logging.conf b/services/inference/tsfmservices/common/logging.conf new file mode 100644 index 00000000..2e2b37e9 --- /dev/null +++ b/services/inference/tsfmservices/common/logging.conf @@ -0,0 +1,35 @@ +[loggers] +keys=root,file + +[handlers] +keys=console,file + +[formatters] +keys=console,file + +[logger_root] +level=DEBUG +handlers=console,file + +[logger_file] +level=DEBUG +handlers=file +qualname=file + +[handler_console] +class=StreamHandler +level=DEBUG +formatter=console +args=(sys.stdout,) + +[handler_file] +class=FileHandler +level=DEBUG +formatter=file +args=('server.log','w',) + +[formatter_console] +format=%(asctime)s - %(levelname)s - %(name)s - %(message)s + +[formatter_file] +format=%(asctime)s - %(levelname)s - %(name)s - %(funcName)s:%(lineno)d - %(message)s diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfmservices/common/util.py new file mode 100644 index 00000000..bcb551ec --- /dev/null +++ b/services/inference/tsfmservices/common/util.py @@ -0,0 +1,162 @@ +"""Utilities to support custom time series models""" + +# Standard +import importlib +import logging +import pathlib +from typing import Optional, Union + +import transformers + +# Third Party +from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel + +LOGGER = logging.getLogger(__file__) + + +def register_config(model_type: str, model_config_name: str, module_path: str) -> None: + """Register a configuration for a particular model architecture + + Args: + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + RuntimeError: Raised when the module cannot be imported from the provided module path. + """ + # example + # model_type: "tinytimemixer" + # model_config_name: "TinyTimeMixerConfig" + # module_path: "tsfm" # place where config should be importable + + # AutoConfig.register("tinytimemixer", TinyTimeMixerConfig) + try: + mod = importlib.import_module(module_path) + conf_class = getattr(mod, model_config_name, None) + except ModuleNotFoundError as exc: # modulenot found, key error ? + raise RuntimeError( + f"Could not load {model_config_name} from {module_path}" + ) from exc + + if conf_class is not None: + AutoConfig.register(model_type, conf_class) + else: + # issue warning? + pass + + +def load_config( + model_path: Union[str, pathlib.Path], + model_type: Optional[str] = None, + model_config_name: Optional[str] = None, + module_path: Optional[str] = None, +) -> PretrainedConfig: + """Load configuration + + Attempts to load the configuration, if it is not loadable, then we register it with the AutoConfig mechanism. + + Args: + model_path (pathlib.Path): The path from which to load the config. + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Returns: + PretrainedConfig: The configuration object corresponding to the pretrained model. + """ + # load config first try autoconfig, if not then we register and load + + try: + conf = AutoConfig.from_pretrained(model_path) + except (KeyError, ValueError) as exc: # determine error raised by autoconfig + if model_type is None or model_config_name is None or module_path is None: + raise ValueError( + "model_type, model_config_name, and module_path should be specified." + ) from exc + + register_config(model_type, model_config_name, module_path) + conf = AutoConfig.from_pretrained(model_path) + + return conf + + +def _get_model_class( + config: PretrainedConfig, module_path: Optional[str] = None +) -> type: + """Helper to find model class based on config object + + First the module_path will be checked if it can be loaded in the current environment. If not + then the transformers library will be used. + + Args: + config (PretrainedConfig): HF configuration for the model. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + AttributeError: Raised if the module at module_path cannot be loaded. + AttributeError: If the architecture provided by the config cannot be loaded from + the module. + + Returns: + type: The class for the model. + """ + if module_path is not None: + try: + mod = importlib.import_module(module_path) + except ModuleNotFoundError as exc: + raise AttributeError("Could not load module '{module_path}'.") from exc + else: + mod = transformers + + # get architecture from model config + architectures = getattr(config, "architectures", []) + for arch in architectures: + try: + model_class = getattr(mod, arch) + return model_class + except AttributeError as exc: + # catch specific error import error or attribute error + raise AttributeError( + "Could not load model class for architecture '{arch}'." + ) from exc + + +def load_model( + model_path: Union[str, pathlib.Path], + config: Optional[PretrainedConfig] = None, + module_path: Optional[str] = None, +) -> PreTrainedModel: + """Load a pretrained model. + If module_path is provided, load the model using the provided module path. + + Args: + model_path (Union[str, pathlib.Path]): Path to a location where the model can be loaded. + config (Optional[PretrainedConfig], optional): HF Configuration object. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + ValueError: Raised if loading from a module_path and a configuration object is not provided. + + Returns: + PreTrainedModel: The loaded pretrained model. + """ + + if module_path is not None and config is None: + raise ValueError( + "Config must be provided when loading from a custom module_path" + ) + + if config is not None: + model_class = _get_model_class(config, module_path=module_path) + LOGGER.info(f"Found model class: {model_class.__name__}") + model = model_class.from_pretrained(model_path, config=config) + return model + + model = AutoModel.from_pretrained(model_path) + LOGGER.info(f"Found model class: {model.__class__.__name__}") + return model diff --git a/services/inference/tsfmservices/config/default_config.yml b/services/inference/tsfmservices/config/default_config.yml new file mode 100644 index 00000000..0acedeaf --- /dev/null +++ b/services/inference/tsfmservices/config/default_config.yml @@ -0,0 +1,8 @@ +# custom models that will be supported +custom_modules: + - module_path: tsfm_public + model_type: tinytimemixer + model_config_name: TinyTimeMixerConfig + +# path where local models will be loaded from +model_dir: /Users/wmgifford/Downloads/ \ No newline at end of file diff --git a/services/inference/tsfmservices/inference/__init__.py b/services/inference/tsfmservices/inference/__init__.py new file mode 100644 index 00000000..e016c88e --- /dev/null +++ b/services/inference/tsfmservices/inference/__init__.py @@ -0,0 +1 @@ +from .inference import InferenceRuntime diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py new file mode 100644 index 00000000..ad91e87c --- /dev/null +++ b/services/inference/tsfmservices/inference/inference.py @@ -0,0 +1,178 @@ +"""Inference Runtime""" + +import datetime +import logging +from typing import Any, Dict, List +from pathlib import Path + +import pandas as pd +from fastapi import APIRouter, HTTPException +from tsfm_public import TimeSeriesForecastingPipeline, TimeSeriesPreprocessor + +from ..common.constants import API_VERSION +from ..common.util import load_config, load_model, register_config +from .payloads import ForecastingInferenceInput, PredictOutput + +from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB + + +LOGGER = logging.getLogger(__file__) + + +class InferenceRuntime: + + def __init__(self, config: Dict[str, Any] = {}): + + self.config = config + model_map = {} + + if "custom_modules" in config: + for custom_module in config["custom_modules"]: + register_config( + custom_module["model_type"], + custom_module["model_config_name"], + custom_module["module_path"], + ) + LOGGER.info(f"registered {custom_module['model_type']}") + + model_map[custom_module["model_config_name"]] = custom_module[ + "module_path" + ] + + self.model_to_module_map = model_map + + def add_routes(self, app): + + self.router = APIRouter(prefix=f"/{API_VERSION}/inference", tags=["inference"]) + self.router.add_api_route( + "/forecasting", + self.forecast, + methods=["POST"], + response_model=PredictOutput, + ) + app.include_router(self.router) + + def load(self, model_path: str): + + try: + preprocessor = TimeSeriesPreprocessor.from_pretrained(model_path) + LOGGER.info("Successfully loaded preprocessor") + except OSError: + preprocessor = None + LOGGER.info("No preprocessor found") + + # load config and model + conf = load_config( + model_path, + ) + + model = load_model( + model_path, + config=conf, + module_path=self.model_to_module_map.get(conf.__class__.__name__, None), + ) + LOGGER.info("Successfully loaded model") + return model, preprocessor + + def forecast(self, input: ForecastingInferenceInput): + try: + LOGGER.info("calling forecast_common") + answer = self._forecast_common(input) + LOGGER.info("done, returning.") + return answer + except Exception as e: + LOGGER.exception(e) + raise HTTPException(status_code=500, detail=repr(e)) + + def _forecast_common( + self, input_payload: ForecastingInferenceInput + ) -> PredictOutput: + + # we need some sort of model registry + # payload = input_payload.model_dump() # do we need? + + data = decode_data(input_payload.data, input_payload.metadata) + future_data = decode_data(input_payload.future_data, input_payload.metadata) + + model_path = Path(self.config["model_dir"]) / input_payload.model_id + + if not model_path.is_dir(): + LOGGER.info(f"Could not find model at path: {model_path}") + if TSFM_ALLOW_LOAD_FROM_HF_HUB: + model_path = input_payload.model_id + LOGGER.info(f"Using HuggingFace Hub: {model_path}") + else: + raise RuntimeError( + f"Could not load model {input_payload.model_id} from {self.config['model_dir']}. If trying to load directly from the HuggingFace Hub please ensure that `TSFM_ALLOW_LOAD_FROM_HF_HUB=1`" + ) + + model, preprocessor = self.load(model_path) + + preprocessor_params = input_payload.metadata.model_dump() + + # preprocess + if preprocessor is None: + preprocessor = TimeSeriesPreprocessor( + **preprocessor_params, + scaling=False, + encode_categorical=False, + ) + # we don't set context length or prediction length above because it is not needed for inference + + # train to estimate freq if not available + preprocessor.train(data) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + forecast_pipeline = TimeSeriesForecastingPipeline( + model=model, + explode_forecasts=True, + feature_extractor=preprocessor, + add_known_ground_truth=False, + ) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + # truncate data length when exploding + # context_length = model.config.context_length + # if explode_forecasts and len(data) > context_length: + # data = select_by_index( + # data, id_columns=input.id_columns, start_index=-context_length + # ) + + test_data = preprocessor.preprocess(data) + + if future_data is not None: + # future data needs some values for targets, but they are unused + # Eventually this will be part of the forecast pipeline. + future_data[input_payload.target_columns] = 0 + future_data = preprocessor.preprocess(future_data) + future_data.drop(columns=input_payload.target_columns) + + forecasts = forecast_pipeline( + test_data, future_time_series=future_data, inverse_scale_outputs=True + ) + + return PredictOutput( + model_id=input_payload.model_id, + created_at=datetime.datetime.now().isoformat(), + results=[forecasts.to_dict(orient="list")], + ) + + +def decode_data(data: Dict[str, List[Any]], metadata: Dict[str, Any]) -> pd.DataFrame: + if not data: + return None + + df = pd.DataFrame.from_dict(data) + if ts_col := metadata.timestamp_column: + df[ts_col] = pd.to_datetime(df[ts_col]) + return df diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfmservices/inference/main.py new file mode 100644 index 00000000..3a31a1db --- /dev/null +++ b/services/inference/tsfmservices/inference/main.py @@ -0,0 +1,51 @@ +"""Primary entry point for inference services""" + +import logging +from fastapi import FastAPI +import yaml + +from tsfmservices import ( + TSFM_PYTHON_LOGGING_FORMAT, + TSFM_PYTHON_LOGGING_LEVEL, + TSFM_CONFIG_FILE, +) +from tsfmservices.common.constants import API_VERSION +from tsfmservices.inference import InferenceRuntime + +logging.basicConfig( + format=TSFM_PYTHON_LOGGING_FORMAT, + level=TSFM_PYTHON_LOGGING_LEVEL, +) + + +if TSFM_CONFIG_FILE: + with open(TSFM_CONFIG_FILE, "r") as file: + config = yaml.safe_load(file) +else: + config = {} + + +app = FastAPI( + title="FM for Time Series API", + version=API_VERSION, + description="This FastAPI application provides service endpoints for performing inference tasks on TSFM HF models.", +) +ir = InferenceRuntime(config=config) +ir.add_routes(app) + + +@app.get("/") +def root(): + # Endpoint at the root URL ("/") returns a welcome message with a clickable link + message = "Welcome. Go to /docs to access the API documentation." + return {"message": message} + + +if __name__ == "__main__": + + # Third Party + import uvicorn + + # Run the FastAPI application on the local host and port 7860 + # CMD["uvicorn", "app:app", "--host", "127.0.0.1", "--port", "7860", "--reload"] + uvicorn.run(app, host="127.0.0.1", port=8000) diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfmservices/inference/payloads.py new file mode 100644 index 00000000..0ecb0661 --- /dev/null +++ b/services/inference/tsfmservices/inference/payloads.py @@ -0,0 +1,138 @@ +"""Payload definitions for inference""" + +# Standard +from typing import Annotated, Any, Dict, List, Optional + +# Third Party +from pydantic import BaseModel, ConfigDict, Field, model_validator + +# WARNING: DO NOT IMPORT util here or else you'll get a circular dependency + +EverythingPatternedString = Annotated[ + str, Field(min_length=0, max_length=100, pattern=".*") +] + + +class BaseMetadataInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + timestamp_column: str = Field( + description="A valid column in the data that should be treated as the timestamp.", + pattern=".*", + min_length=1, + max_length=100, + example="date", + ) + id_columns: List[EverythingPatternedString] = Field( + description="Columns that define a unique key for time series.", + default_factory=list, + max_length=10, + example=["ID1", "ID2"], + min_length=0, + ) + freq: Optional[str] = Field( + description="""A freqency indicator for the given timestamp_column. + See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. + If not provided, we will attempt to infer it from the data.""", + default=None, + pattern="\d+[B|D|W|M|Q|Y|h|min|s|ms|us|ns]|^\s*$", + min_length=0, + max_length=100, + example="1h", + ) + + +class ForecastingMetadataInput(BaseMetadataInput): + target_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["HUFL", "HULL"], + description="An array of column headings which constitute the target variables.", + ) + observable_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["OBS1", "OBS2"], + description="An optional array of column headings which constitute the observable variables.", + ) + control_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CNTRL1", "CNTRL2"], + description="An optional array of column headings which constitute the control variables.", + ) + conditional_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CONDL1", "CONDL2"], + description="An optional array of column headings which constitute the conditional variables.", + ) + static_categorical_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["SCV1", "SCV2"], + description="An optional array of column headings which constitute the static categorical variables.", + ) + + +class ForecastingParameters(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + prediction_length: Optional[int] = Field( + description="The prediction length for the forecast.", + default=None, + ) + + +class BaseInferenceInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + model_id: str = Field( + description="A model identifier.", + pattern="^\S+$", + min_length=1, + max_length=100, + example="ibm/tinytimemixer-monash-fl_96", + ) + + +class ForecastingInferenceInput(BaseInferenceInput): + metadata: ForecastingMetadataInput + + parameters: ForecastingParameters + + data: Dict[str, List[Any]] = Field( + description="Data", + ) + + future_data: Optional[Dict[str, List[Any]]] = Field( + description="Future data", default=None + ) + + @model_validator(mode="before") + @classmethod + def grpcswaparoos(cls, data: Any) -> Any: + if isinstance(data, dict): + if "future_data" in data and data["future_data"] is None: + data["future_data"] = "" + return data + + +class PredictOutput(BaseModel): + model_id: str = Field( + description="Model ID for the model that produced the prediction.", + default=None, + ) + created_at: str = Field( + description="Timestamp indicating when the prediction was created. ISO 8601 format.", + default=None, + ) + results: List[Dict[str, List[Any]]] = Field( + description="List of prediction results.", + default=None, + )