metadata
license: apache-2.0
Convert pytorch model to onnx format.
import torch
import onnx
import onnxruntime
from onnxruntime import InferenceSession
from transformers import RobertaTokenizer, RobertaModel
from transformers.convert_graph_to_onnx import convert
import numpy as np
from onnxruntime.transformers import optimizer
from pathlib import Path
from onnxruntime.quantization import quantize_dynamic, QuantType
from sentence_transformers import SentenceTransformer, util
sbert = SentenceTransformer('sentence-transformers/all-roberta-large-v1')
sbert.save('sbert-all-roberta-large-v1')
tokenizer = RobertaTokenizer.from_pretrained('sentence-transformers/all-roberta-large-v1')
model = RobertaModel.from_pretrained('sentence-transformers/all-roberta-large-v1')
model.save_pretrained('./all-roberta-large-v1/')
tokenizer.save_pretrained('./all-roberta-large-v1/')
opt_model_path = "onnx-model/sbert-roberta-large.onnx"
convert(framework='pt', model='./all-roberta-large-v1/', output= Path(opt_model_path), opset=12, use_external_format=False, pipeline_name='feature-extraction')
quantize_dynamic(
model_input='onnx-model/sbert-roberta-large.onnx',
model_output='onnx-model/sbert-roberta-large-quant.onnx',
per_channel=True,
reduce_range=True,
activation_type=QuantType.QUInt8,
weight_type=QuantType.QInt8,
optimize_model=False,
use_external_data_format=False
)
##Copy pooling layer and tokenizer files to the output directory
How to generate embeddings?
from onnxruntime import InferenceSession
import torch
from transformers.modeling_outputs import BaseModelOutput
from transformers import RobertaTokenizerFast
import torch.nn.functional as F
from sentence_transformers.models import Transformer, Pooling, Dense
class RobertaEncoder(torch.nn.Module):
def __init__(self, encoder_sess):
super().__init__()
self.encoder = encoder_sess
def forward(
self,
input_ids,
attention_mask,
inputs_embeds=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
encoder_hidden_state = torch.from_numpy(
self.encoder.run(
None,
{
"input_ids": input_ids.cpu().numpy(),
"attention_mask": attention_mask.cpu().numpy(),
},
)[0]
)
return BaseModelOutput(encoder_hidden_state)
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def sbert_onnx_encode(sentence_input):
token = roberta_tokenizer(sentence_input, return_tensors='pt')
encoder_outputs = encoder_layer(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
sbert_embeddings = mean_pooling(encoder_outputs, token['attention_mask'])
sbert_embeddings = F.normalize(sbert_embeddings, p=2, dim=1)
return sbert_embeddings.tolist()[0]
roberta_tokenizer = RobertaTokenizerFast.from_pretrained('sbert-onnx-all-roberta-large-v1')
encoder_sess = InferenceSession('sbert-onnx-all-roberta-large-v1/sbert-roberta-large-quant.onnx')
encoder_layer = RobertaEncoder(encoder_sess)
pooling_layer = Pooling.load('./sbert-onnx-all-roberta-large-v1/1_Pooling/')
m1 = sbert_onnx_encode('That is a happy person')
m2 = sbert.encode('That is a happy person').tolist()
print(util.cos_sim(m1,m2))
##tensor([[0.9925]])