vamsibanda
commited on
Commit
•
a286e00
1
Parent(s):
b2dfc40
Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,101 @@
|
|
1 |
---
|
2 |
license: apache-2.0
|
3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
license: apache-2.0
|
3 |
---
|
4 |
+
|
5 |
+
## Convert pytorch model to onnx format.
|
6 |
+
|
7 |
+
import torch
|
8 |
+
import onnx
|
9 |
+
import onnxruntime
|
10 |
+
from onnxruntime import InferenceSession
|
11 |
+
from transformers import RobertaTokenizer, RobertaModel
|
12 |
+
from transformers.convert_graph_to_onnx import convert
|
13 |
+
import numpy as np
|
14 |
+
from onnxruntime.transformers import optimizer
|
15 |
+
from pathlib import Path
|
16 |
+
from onnxruntime.quantization import quantize_dynamic, QuantType
|
17 |
+
from sentence_transformers import SentenceTransformer, util
|
18 |
+
|
19 |
+
sbert = SentenceTransformer('sentence-transformers/all-roberta-large-v1')
|
20 |
+
sbert.save('sbert-all-roberta-large-v1')
|
21 |
+
|
22 |
+
tokenizer = RobertaTokenizer.from_pretrained('sentence-transformers/all-roberta-large-v1')
|
23 |
+
model = RobertaModel.from_pretrained('sentence-transformers/all-roberta-large-v1')
|
24 |
+
model.save_pretrained('./all-roberta-large-v1/')
|
25 |
+
tokenizer.save_pretrained('./all-roberta-large-v1/')
|
26 |
+
|
27 |
+
opt_model_path = "onnx-model/sbert-roberta-large.onnx"
|
28 |
+
convert(framework='pt', model='./all-roberta-large-v1/', output= Path(opt_model_path), opset=12, use_external_format=False, pipeline_name='feature-extraction')
|
29 |
+
|
30 |
+
quantize_dynamic(
|
31 |
+
model_input='onnx-model/sbert-roberta-large.onnx',
|
32 |
+
model_output='onnx-model/sbert-roberta-large-quant.onnx',
|
33 |
+
per_channel=True,
|
34 |
+
reduce_range=True,
|
35 |
+
activation_type=QuantType.QUInt8,
|
36 |
+
weight_type=QuantType.QInt8,
|
37 |
+
optimize_model=False,
|
38 |
+
use_external_data_format=False
|
39 |
+
)
|
40 |
+
##Copy pooling layer and tokenizer files to the output directory
|
41 |
+
|
42 |
+
|
43 |
+
## How to generate embeddings?
|
44 |
+
|
45 |
+
from onnxruntime import InferenceSession
|
46 |
+
import torch
|
47 |
+
from transformers.modeling_outputs import BaseModelOutput
|
48 |
+
from transformers import RobertaTokenizerFast
|
49 |
+
import torch.nn.functional as F
|
50 |
+
from sentence_transformers.models import Transformer, Pooling, Dense
|
51 |
+
|
52 |
+
class RobertaEncoder(torch.nn.Module):
|
53 |
+
def __init__(self, encoder_sess):
|
54 |
+
super().__init__()
|
55 |
+
self.encoder = encoder_sess
|
56 |
+
|
57 |
+
def forward(
|
58 |
+
self,
|
59 |
+
input_ids,
|
60 |
+
attention_mask,
|
61 |
+
inputs_embeds=None,
|
62 |
+
head_mask=None,
|
63 |
+
output_attentions=None,
|
64 |
+
output_hidden_states=None,
|
65 |
+
return_dict=None,
|
66 |
+
):
|
67 |
+
|
68 |
+
encoder_hidden_state = torch.from_numpy(
|
69 |
+
self.encoder.run(
|
70 |
+
None,
|
71 |
+
{
|
72 |
+
"input_ids": input_ids.cpu().numpy(),
|
73 |
+
"attention_mask": attention_mask.cpu().numpy(),
|
74 |
+
|
75 |
+
},
|
76 |
+
)[0]
|
77 |
+
)
|
78 |
+
|
79 |
+
return BaseModelOutput(encoder_hidden_state)
|
80 |
+
|
81 |
+
def mean_pooling(model_output, attention_mask):
|
82 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
83 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
84 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
85 |
+
|
86 |
+
def sbert_onnx_encode(sentence_input):
|
87 |
+
token = roberta_tokenizer(sentence_input, return_tensors='pt')
|
88 |
+
encoder_outputs = encoder_layer(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
|
89 |
+
sbert_embeddings = mean_pooling(encoder_outputs, token['attention_mask'])
|
90 |
+
sbert_embeddings = F.normalize(sbert_embeddings, p=2, dim=1)
|
91 |
+
return sbert_embeddings.tolist()[0]
|
92 |
+
|
93 |
+
roberta_tokenizer = RobertaTokenizerFast.from_pretrained('sbert-onnx-all-roberta-large-v1')
|
94 |
+
encoder_sess = InferenceSession('sbert-onnx-all-roberta-large-v1/sbert-roberta-large-quant.onnx')
|
95 |
+
encoder_layer = RobertaEncoder(encoder_sess)
|
96 |
+
pooling_layer = Pooling.load('./sbert-onnx-all-roberta-large-v1/1_Pooling/')
|
97 |
+
|
98 |
+
m1 = sbert_onnx_encode('That is a happy person')
|
99 |
+
m2 = sbert.encode('That is a happy person').tolist()
|
100 |
+
print(util.cos_sim(m1,m2))
|
101 |
+
##tensor([[0.9925]])
|