FilesExpand file tree

main

/

onnx_utils.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

187 lines (161 loc) · 7.28 KB

main

/

onnx_utils.py

Copy path

Top

File metadata and controls

• Code
• Blame

187 lines (161 loc) · 7.28 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

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

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

# coding=utf-8

# Copyright 2022 The HuggingFace Inc. team.

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import os

import shutil

from pathlib import Path

from typing import Optional, Union

import numpy as np

from huggingface_hub import hf_hub_download

from .utils import ONNX_WEIGHTS_NAME, is_onnx_available, logging

if is_onnx_available():

import onnxruntime as ort

logger = logging.get_logger(__name__)

class OnnxRuntimeModel:

def __init__(self, model=None, **kwargs):

logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")

self.model = model

self.model_save_dir = kwargs.get("model_save_dir", None)

self.latest_model_name = kwargs.get("latest_model_name", "model.onnx")

def __call__(self, **kwargs):

inputs = {k: np.array(v) for k, v in kwargs.items()}

return self.model.run(None, inputs)

@staticmethod

def load_model(path: Union[str, Path], provider=None, sess_options=None):

"""

Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`

Arguments:

path (`str` or `Path`):

Directory from which to load

provider(`str`, *optional*):

Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`

"""

if provider is None:

logger.info("No onnxruntime provider specified, using CPUExecutionProvider")

provider = "CPUExecutionProvider"

return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)

def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):

"""

Save a model and its configuration file to a directory, so that it can be re-loaded using the

[`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the

latest_model_name.

Arguments:

save_directory (`str` or `Path`):

Directory where to save the model file.

file_name(`str`, *optional*):

Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the

model with a different name.

"""

model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME

src_path = self.model_save_dir.joinpath(self.latest_model_name)

dst_path = Path(save_directory).joinpath(model_file_name)

if not src_path.samefile(dst_path):

shutil.copyfile(src_path, dst_path)

def save_pretrained(

self,

save_directory: Union[str, os.PathLike],

**kwargs,

):

"""

Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class

method.:

Arguments:

save_directory (`str` or `os.PathLike`):

Directory to which to save. Will be created if it doesn't exist.

"""

if os.path.isfile(save_directory):

logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

return

os.makedirs(save_directory, exist_ok=True)

# saving model weights/files

self._save_pretrained(save_directory, **kwargs)

@classmethod

def _from_pretrained(

cls,

model_id: Union[str, Path],

use_auth_token: Optional[Union[bool, str, None]] = None,

revision: Optional[Union[str, None]] = None,

force_download: bool = False,

cache_dir: Optional[str] = None,

file_name: Optional[str] = None,

provider: Optional[str] = None,

sess_options: Optional["ort.SessionOptions"] = None,

**kwargs,

):

"""

Load a model from a directory or the HF Hub.

Arguments:

model_id (`str` or `Path`):

Directory from which to load

use_auth_token (`str` or `bool`):

Is needed to load models from a private or gated repository

revision (`str`):

Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id

cache_dir (`Union[str, Path]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

force_download (`bool`, *optional*, defaults to `False`):

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

file_name(`str`):

Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load

different model files from the same repository or directory.

provider(`str`):

The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.

kwargs (`Dict`, *optional*):

kwargs will be passed to the model during initialization

"""

model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME

# load model from local directory

if os.path.isdir(model_id):

model = OnnxRuntimeModel.load_model(

os.path.join(model_id, model_file_name), provider=provider, sess_options=sess_options

)

kwargs["model_save_dir"] = Path(model_id)

# load model from hub

else:

# download model

model_cache_path = hf_hub_download(

repo_id=model_id,

filename=model_file_name,

use_auth_token=use_auth_token,

revision=revision,

cache_dir=cache_dir,

force_download=force_download,

)

kwargs["model_save_dir"] = Path(model_cache_path).parent

kwargs["latest_model_name"] = Path(model_cache_path).name

model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)

return cls(model=model, **kwargs)

@classmethod

def from_pretrained(

cls,

model_id: Union[str, Path],

force_download: bool = True,

use_auth_token: Optional[str] = None,

cache_dir: Optional[str] = None,

**model_kwargs,

):

revision = None

if len(str(model_id).split("@")) == 2:

model_id, revision = model_id.split("@")

return cls._from_pretrained(

model_id=model_id,

revision=revision,

cache_dir=cache_dir,

force_download=force_download,

use_auth_token=use_auth_token,

**model_kwargs,

)