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fix(gpt): support timemoe (#30410)

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Haojun Liao 2025-03-24 18:58:58 +08:00 committed by GitHub
parent 30402f66a6
commit daa129dbb1
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4 changed files with 102 additions and 103 deletions
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4
tools/tdgpt/taosanalytics/algo/fc/gpt.py
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@ -20,10 +20,6 @@ class _GPTService(AbstractForecastService):
self.service_host = 'http://127.0.0.1:5000/tdtsfm'
self.headers = {'Content-Type': 'application/json'}
self.std = None
self.threshold = None
self.time_interval = None
def execute(self):
if self.list is None or len(self.list) < self.period:

81
tools/tdgpt/taosanalytics/algo/fc/lstm.py
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@ -1,81 +0,0 @@
# encoding:utf-8
# pylint: disable=c0103
""" auto encoder algorithms to detect anomaly for time series data"""
import os.path
import keras
from taosanalytics.algo.forecast import insert_ts_list
from taosanalytics.conf import app_logger, conf
from taosanalytics.service import AbstractForecastService
class _LSTMService(AbstractForecastService):
name = 'sample_forecast_model'
desc = "sample forecast model based on LSTM"
def __init__(self):
super().__init__()
self.table_name = None
self.mean = None
self.std = None
self.threshold = None
self.time_interval = None
self.model = None
self.dir = 'sample-fc-lstm'
self.root_path = conf.get_model_directory()
self.root_path = self.root_path + f'/{self.dir}/'
if not os.path.exists(self.root_path):
app_logger.log_inst.error(
"%s ad algorithm failed to locate default module directory:"
"%s, not active", self.__class__.__name__, self.root_path)
else:
app_logger.log_inst.info("%s ad algorithm root path is: %s", self.__class__.__name__,
self.root_path)
def execute(self):
if self.input_is_empty():
return []
if self.model is None:
raise FileNotFoundError("not load autoencoder model yet, or load model failed")
res = self.model.predict(self.list)
insert_ts_list(res, self.start_ts, self.time_step, self.rows)
if self.return_conf:
res1 = [res.tolist(), res.tolist(), res.tolist()], None
else:
res1 = [res.tolist()], None
# add the conf range if required
return {
"mse": None,
"res": res1
}
def set_params(self, params):
if "model" not in params:
raise ValueError("model needs to be specified")
name = params['model']
module_file_path = f'{self.root_path}/{name}.keras'
# module_info_path = f'{self.root_path}/{name}.info'
app_logger.log_inst.info("try to load module:%s", module_file_path)
if os.path.exists(module_file_path):
self.model = keras.models.load_model(module_file_path)
else:
app_logger.log_inst.error("failed to load LSTM model file: %s", module_file_path)
raise FileNotFoundError(f"{module_file_path} not found")
def get_params(self):
return {"dir": self.dir + '/*'}

70
tools/tdgpt/taosanalytics/algo/fc/timemoe.py Normal file
View File

@ -0,0 +1,70 @@
# encoding:utf-8
# pylint: disable=c0103
""" auto encoder algorithms to detect anomaly for time series data"""
import json
import requests
from taosanalytics.algo.forecast import insert_ts_list
from taosanalytics.conf import app_logger, conf
from taosanalytics.service import AbstractForecastService
class _TimeMOEService(AbstractForecastService):
name = 'timemoe'
desc = ("Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts; "
"Ref to https://github.com/Time-MoE/Time-MoE")
def __init__(self):
super().__init__()
self.table_name = None
self.service_host = 'http://127.0.0.1:5001/timemoe'
self.headers = {'Content-Type': 'application/json'}
def execute(self):
if self.list is None or len(self.list) < self.period:
raise ValueError("number of input data is less than the periods")
if self.rows <= 0:
raise ValueError("fc rows is not specified yet")
# let's request the gpt service
data = {"input": self.list, 'next_len': self.rows}
try:
response = requests.post(self.service_host, data=json.dumps(data), headers=self.headers)
except Exception as e:
app_logger.log_inst.error(f"failed to connect the service: {self.service_host} ", str(e))
raise e
if response.status_code == 404:
app_logger.log_inst.error(f"failed to connect the service: {self.service_host} ")
raise ValueError("invalid host url")
elif response.status_code != 200:
app_logger.log_inst.error(f"failed to request the service: {self.service_host}, reason: {response.text}")
raise ValueError(f"failed to request the service, {response.text}")
pred_y = response.json()['output']
res = {
"res": [pred_y]
}
insert_ts_list(res["res"], self.start_ts, self.time_step, self.rows)
return res
def set_params(self, params):
super().set_params(params)
if "host" in params:
self.service_host = params['host']
if self.service_host.startswith("https://"):
self.service_host = self.service_host.replace("https://", "http://")
elif "http://" not in self.service_host:
self.service_host = "http://" + self.service_host
app_logger.log_inst.info("%s specify gpt host service: %s", self.__class__.__name__,
self.service_host)

50
tools/tdgpt/taosanalytics/app.py
View File

@ -57,23 +57,30 @@ def list_all_models():
def handle_ad_request():
"""handle the anomaly detection requests"""
app_logger.log_inst.info('recv ad request from %s', request.remote_addr)
app_logger.log_inst.debug('req payload: %s', request.json)
algo = request.json["algo"].lower() if "algo" in request.json else "ksigma"
try:
req_json = request.json
except Exception as e:
app_logger.log_inst.error('invalid json format, %s, %s', e, request.data)
raise ValueError(e)
app_logger.log_inst.debug('req payload: %s', req_json)
algo = req_json["algo"].lower() if "algo" in req_json else "ksigma"
# 1. validate the input data in json format
try:
validate_pay_load(request.json)
validate_pay_load(req_json)
except ValueError as e:
return {"msg": str(e), "rows": -1}
payload = request.json["data"]
payload = req_json["data"]
# 2. white noise data check
wn_check = request.json["wncheck"] if "wncheck" in request.json else 1
wn_check = req_json["wncheck"] if "wncheck" in req_json else 1
data_index = get_data_index(request.json["schema"])
ts_index = get_ts_index(request.json["schema"])
data_index = get_data_index(req_json["schema"])
ts_index = get_ts_index(req_json["schema"])
if wn_check:
try:
@ -86,7 +93,7 @@ def handle_ad_request():
# 3. parse the options for different ad services
# the default options is like following: "algo=ksigma,k=2,invalid_option=44"
options = request.json["option"] if "option" in request.json else None
options = req_json["option"] if "option" in req_json else None
params = parse_options(options)
# 4. do anomaly detection
@ -108,24 +115,31 @@ def handle_ad_request():
def handle_forecast_req():
"""handle the fc request """
app_logger.log_inst.info('recv fc from %s', request.remote_addr)
app_logger.log_inst.debug('req payload: %s', request.json)
try:
req_json = request.json
except Exception as e:
app_logger.log_inst.error('forecast recv invalid json format, %s, %s', e, request.data)
raise ValueError(e)
app_logger.log_inst.debug('req payload: %s', req_json)
# holt-winters by default
algo = request.json['algo'].lower() if 'algo' in request.json else 'holtwinters'
algo = req_json['algo'].lower() if 'algo' in req_json else 'holtwinters'
# 1. validate the input data in json format
try:
validate_pay_load(request.json)
validate_pay_load(req_json)
except ValueError as e:
app_logger.log_inst.error('validate req json failed, %s', e)
return {"msg": str(e), "rows": -1}
raise ValueError(e)
payload = request.json["data"]
payload = req_json["data"]
# 2. white noise data check
wn_check = request.json["wncheck"] if "wncheck" in request.json else 1
data_index = get_data_index(request.json["schema"])
ts_index = get_ts_index(request.json["schema"])
wn_check = req_json["wncheck"] if "wncheck" in req_json else 1
data_index = get_data_index(req_json["schema"])
ts_index = get_ts_index(req_json["schema"])
if wn_check:
try:
@ -136,11 +150,11 @@ def handle_forecast_req():
except Exception as e:
return {"msg": str(e), "rows": -1}
options = request.json["option"] if "option" in request.json else None
options = req_json["option"] if "option" in req_json else None
params = parse_options(options)
try:
do_add_fc_params(params, request.json)
do_add_fc_params(params, req_json)
except ValueError as e:
app_logger.log_inst.error("invalid fc params: %s", e)
return {"msg": f"{e}", "rows": -1}