Update README.md

README.md

@@ -1,3 +1,101 @@
 ---
 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]])