Spaces:
Runtime error
Runtime error
File size: 3,079 Bytes
890de26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# -*- coding:utf-8 -*-
# @FileName :lmOrtInderRuntimeSession.py.py
# @Time :2023/10/13 17:24
# @Author :lovemefan
# @Email :[email protected]
import logging
from pathlib import Path
import numpy as np
from onnxruntime import (
SessionOptions,
GraphOptimizationLevel,
get_device,
get_available_providers,
InferenceSession,
)
from paraformer.runtime.python.utils.singleton import singleton
@singleton
class LMOrtInferRuntimeSession:
def __init__(self, model_file, device_id=-1, intra_op_num_threads=4):
sess_opt = SessionOptions()
sess_opt.log_severity_level = 4
sess_opt.intra_op_num_threads = intra_op_num_threads
sess_opt.enable_cpu_mem_arena = False
sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
cuda_ep = "CUDAExecutionProvider"
cuda_provider_options = {
"device_id": device_id,
"arena_extend_strategy": "kNextPowerOfTwo",
"cudnn_conv_algo_search": "EXHAUSTIVE",
"do_copy_in_default_stream": "true",
}
cpu_ep = "CPUExecutionProvider"
cpu_provider_options = {
"arena_extend_strategy": "kSameAsRequested",
}
EP_list = []
if (
device_id != "-1"
and get_device() == "GPU"
and cuda_ep in get_available_providers()
):
EP_list = [(cuda_ep, cuda_provider_options)]
EP_list.append((cpu_ep, cpu_provider_options))
self._verify_model(model_file)
self.session = InferenceSession(
model_file, sess_options=sess_opt, providers=EP_list
)
if device_id != "-1" and cuda_ep not in self.session.get_providers():
logging.warning(
f"{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n"
"Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, "
"you can check their relations from the offical web site: "
"https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html"
)
def __call__(
self,
texts: np.ndarray,
) -> np.ndarray:
"""
Args:
texts: numpy.ndarray , [batch size , sequence length] batch only support 1, dtype is int64
Returns:
"""
input_dict = dict(zip(self.get_input_names(), (texts,)))
return self.session.run(None, input_dict)[0]
def get_input_names(
self,
):
return [v.name for v in self.session.get_inputs()]
def get_output_names(
self,
):
return [v.name for v in self.session.get_outputs()]
@staticmethod
def _verify_model(model_path):
model_path = Path(model_path)
if not model_path.exists():
raise FileNotFoundError(f"{model_path} does not exists.")
if not model_path.is_file():
raise FileExistsError(f"{model_path} is not a file.")
|